Editor's Note: Sensors, as the "nerve endings of the information age," have penetrated every critical field of the socio-economic landscape. Since October 2025, China Electronic News has invited Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Jiusan Society and Executive Vice Chairman of the China Sensor and IoT Industry Alliance, to launch the column "Guo Yuansheng Explains Sensors." Focusing on eight major fields and scenarios: electric power, major equipment, intelligent manufacturing, smart agriculture, smart healthcare and big health, smart home appliances and consumer electronics, urban security, and low-altitude economy, it has successively published articles such as "Stationed on the Power Generation Side, Sensors Become the Cornerstone of Stable Operation of New Power Systems" and "Energy Storage Sensors Clarify Three Core Development Directions," which have received widespread attention and high praise from readers. The article published this time focuses on smart sensors in mining scenarios, elaborating on their application prospects, industrial status, and future suggestions to build industry consensus and promote industrial development.
Mines are the core hubs for national energy and strategic raw material supply. Their safety production level is not only related to the life and well-being of millions of miners but also the cornerstone for maintaining national energy security and ensuring the stable operation of the industrial system. However, accidents such as coal mine gas leaks and metal mine slope collapses still occur occasionally. How to achieve accurate prediction of accident risks through real-time dynamic data collection has become a key bottleneck restricting the intelligent transformation and safety efficiency upgrade of mines. The dual demands of safety production and quality improvement have thus become increasingly urgent.
As the core basic equipment for mine digitalization, smart sensors not only promote the qualitative leap of on-site monitoring data from "scattered collection" to "linkage analysis," but also need to deeply integrate AI (Artificial Intelligence) algorithms and multi-parameter linkage technologies. Targeting complex mining scenarios of different minerals such as coal mines, metal mines, and non-metallic mines, they innovatively develop a new generation of customized smart sensors to accurately solve core problems such as advanced gas warning, slope stability monitoring, and equipment fault prevention. In the fully mechanized coal mining face, gas sensors can predict abnormal gas concentrations 10 minutes in advance; in open-pit metal mines, slope displacement sensors can combine rainfall data to warn of landslide risks; in limestone mines, level sensors can penetrate dust to achieve precise monitoring. Consequently, mine safety management can achieve a fundamental transformation from "passive response to accidents" to "proactive risk prevention and control," injecting new momentum into the digital transformation and high-quality development of safety production in mines.
I. Application Practice of Smart Sensors in Coal Mining Scenarios
Underground coal mining faces multiple risks such as gas explosions, roof collapses, and equipment overheating. Traditional coal mine sensors have problems such as high false alarm rates (gas false alarm rates often reach 15%-20%), isolated data (pressure sensors are not linked with coal machine operation data), and weak anti-interference capabilities (underground vibrations easily lead to data drift), making it difficult to meet the needs of "unmanned operation and precise warning" in intelligent fully mechanized mining faces. The new generation of smart sensors has achieved full-process control of core coal mine risks through hardware protection upgrades and AI algorithm integration. The following analysis is combined with the application practice of fully mechanized mining faces:
Sensor Deployment at the Coal Mining Face
The coal mining face is the core area of underground production, gathering large equipment such as coal machines, hydraulic supports, and conveyor belts. The space is narrow, the environment is damp and dusty, and there is a high risk of gas emission. Multiple dedicated smart sensors need to be deployed to form a perception cluster:
1. Gas Concentration Sensor: The "Gas Sentinel" in High-Risk Areas
Gas concentration is the primary monitoring indicator for fully mechanized coal mining faces and needs to be deployed at high density in areas where gas is easy to accumulate: 1 unit is arranged every 50 meters along the strike of the fully mechanized mining face; in the coal cutting area (where gas emission is the highest), an additional mobile gas sensor is added, moving synchronously with the coal machine. The smart gas sensor adopts a dual-principle design of "catalytic combustion element + infrared spectroscopy detection." The catalytic combustion element is responsible for real-time monitoring of low-concentration gas (0-10% CH₄), and infrared spectroscopy is responsible for high-precision detection of high-concentration gas (10%-100% CH₄), avoiding the problems of "range overflow" or "sensitivity drop" of a single principle in high-concentration environments. At the same time, the sensor shell is made of mine explosion-proof material, with an IP68 waterproof and dustproof rating, able to resist the impact of moisture, coal dust, and mechanical vibration, and its service life is extended to more than 3 years.
2. Roof Pressure Sensor: The "Force Monitor" of Hydraulic Supports
The cause of roof collapse is insufficient or uneven supporting force of hydraulic supports, so 1 roof pressure sensor needs to be deployed on the column of each hydraulic support. Using the strain measurement principle, strain gauges attached to the surface of the column capture the roof pressure changes borne by the support in real time. When the pressure of a certain support exceeds the set threshold (usually 80% of the rated working pressure of the column), the sensor can immediately trigger a warning to remind the remote control center to adjust the support height. Attention should be paid to the installation angle of the sensor during deployment: keep it perpendicular to the column axis to avoid uneven force on the strain gauge due to tilt, and the data error is controlled within ±1%FS; at the same time, the sensor cable uses mine flame-retardant cables resistant to pulling to prevent cable breakage when the coal machine moves.
3. Mine Infrared Temperature Measurement Sensor: The "Temperature Guardian" for Equipment Safety
Coal machine motors, conveyor belt rollers, hydraulic support pump stations, and other equipment run at high speeds for a long time, which can easily cause abnormal temperatures due to overload or insufficient lubrication, leading to fires or equipment damage. Sensors are deployed at fixed points according to the heating characteristics of different equipment: 2 units are arranged on the shell of each coal machine motor (monitoring stator and rotor temperatures respectively); 1 unit is arranged every 5 rollers at the conveyor belt roller bearings; 1 unit is arranged for the oil pump and oil tank respectively at the hydraulic support pump station. This sensor uses non-contact infrared detection with a measurement distance of 0.5-3 meters, avoiding direct contact with high-temperature equipment that could cause damage; at the same time, it has a built-in "temperature trend analysis" algorithm that can identify "slow rises" in equipment temperature (e.g., temperature rising by 0.5℃ daily due to bearing wear) and "sudden spikes" (e.g., temperature rising by 10℃ in 1 minute due to motor short circuit), distinguishing normal heating from fault precursors and reducing false alarms.
Key Technical Parameters and Intelligent Upgrades
Smart sensors achieve a closed loop of "data collection → analysis and prediction → proactive warning" through algorithm upgrades. The technical parameters and intelligent functions of the sensors are as follows:
II. Application Practice of Multi-Scenario Sensors in Metal Mines
Metal mining is divided into two main modes: open-pit mining and underground mining, with significantly different risk characteristics: open-pit mines face risks such as slope instability, rainstorm landslides, and blasting flying rocks. According to statistics, slope collapses account for 40% of accidents in open-pit metal mines; underground metal mines need to deal with toxic gas (such as carbon monoxide, hydrogen sulfide) leaks, dust exceeding standards, and roadway collapses, among which toxic gas poisoning accidents account for 25% of underground accidents. Traditional metal mine sensors have shortcomings such as "easy aging in open-pit deployment," "weak corrosion resistance underground," and "inability to link environmental data." The new generation of smart sensors achieves full-scenario adaptation for open-pit and underground mines through weather-resistant design and multi-parameter fusion algorithms. The following analysis is presented:
Application of Core Sensors in Open-Pit Mines
Open-pit metal mines (such as iron, copper, and gold mines) usually have mining bench heights of 10-15 meters, and slope lengths can reach hundreds of meters. As the mining depth increases, slope stability gradually decreases. At the same time, affected by natural factors such as rainstorms and earthquakes, landslide accidents are prone to occur. In addition, the vibration and flying rocks from blasting operations in open-pit mines also require real-time safety monitoring. The deployment of its core sensors is as follows:
1. Slope Displacement Sensor: The "Landslide Warning Sentinel" of Open-Pit Mines
Slope displacement is the core indicator for judging slope stability and needs to be deployed on the main slope, secondary slope, and dump slope of open-pit mines: 1 surface displacement sensor is arranged every 50 meters along the slope strike; in areas where the slope height exceeds 50 meters, additional borehole deep displacement sensors are arranged (the borehole depth is 1/3 of the slope height, e.g., a 60-meter high slope is drilled 20 meters deep) to monitor deep rock mass displacement. The smart slope displacement sensor adopts a dual-principle design of "GNSS + inclination measurement." The GNSS module achieves millimeter-level surface displacement monitoring (accuracy ±0.5mm), and the borehole deep displacement sensor achieves sub-millimeter-level monitoring (accuracy ±0.8mm). The inclination module captures tiny slope tilt changes (accuracy ±0.1°), avoiding the problem of signal interruption of a single GNSS in severe weather (such as rainstorms and heavy fog). The sensor shell is made of UV-resistant and acid rain-resistant polyester alloy material, with a protection rating of IP66, able to adapt to temperature changes of -30℃~70℃ in the open-pit environment, and has a service life of ≥5 years.
2. Mine Rainfall Sensor: The "Meteorological Linkage Instrument" for Slope Safety
Rainstorms are a key factor inducing slope landslides in open-pit mines. Rainwater seeping into the slope rock and soil mass will reduce the soil cohesion and increase the sliding force. Mine rainfall sensors need to be deployed at the top of the open-pit mine slope and around the dump, with 1 unit arranged every 200 meters. It uses the tipping bucket measurement principle, with an accuracy of ±0.1mm and a response time of ≤1 second. It can monitor rainfall and rainfall intensity in real time (e.g., hourly rainfall ≥20mm is the rainstorm warning threshold). Unlike traditional rainfall sensors, smart rainfall sensors can link with slope displacement sensors: when the hourly rainfall exceeds 15mm, it automatically increases the data refresh frequency of the slope displacement sensor (from 1 time/10 minutes to 1 time/minute), and triggers the "key slope monitoring during rainstorm" mode. If the displacement rate exceeds 0.5mm/hour (normal rate ≤0.2mm/hour), a landslide warning is immediately pushed.
3. Blasting Vibration Sensor: The "Vibration Monitor" for Operation Safety
Blasting operations in open-pit mines generate vibration waves, which may cause loosening of surrounding slopes or damage to adjacent buildings. Blasting vibration sensors need to be deployed within 50-500 meters around the blasting area: 1 unit is arranged every 100 meters in areas close to the slope; 1 unit is arranged every 50 meters in areas adjacent to industrial squares or residential areas. The sensor uses the piezoelectric vibration measurement principle, with a measurement range of 0-20cm/s (vibration velocity), an accuracy of ±0.01cm/s, and a response frequency of 5-500Hz (covering the main frequency range of blasting vibration). In terms of intelligent functions, the sensor can input the blasting plan in advance (such as explosive amount and blasting hole depth), and predict the theoretical vibration value combined with distance parameters; after blasting, it measures the actual vibration value in real time. If the actual value exceeds the theoretical value by 15%, it immediately warns "blasting parameters are abnormal, and the explosive amount needs to be adjusted," and uploads the vibration data to the local housing and urban-rural development department to meet the vibration safety standards for civil buildings (GB 6722).
Application of Core Sensors in Underground Metal Mines
Underground metal mines (such as underground copper, lead, and zinc mines) have narrow mining roadways and poor ventilation conditions. After blasting operations, a large amount of toxic gas such as carbon monoxide (CO) and nitrogen oxides (NOx) will be generated. At the same time, the dust concentration is easy to exceed the standard (the "Occupational Exposure Limits for Hazardous Agents in the Workplace Part 1: Chemical Hazardous Agents" GBZ 2.1-2007 stipulates ≤4mg/m³). Long-term exposure will lead to occupational diseases in miners. In addition, risks such as roof collapse of underground roadways and equipment failures also need to be monitored in real time. The deployment of core sensors is as follows:
1. Multi-Gas Composite Sensor: The "Toxic Gas Detector" for Underground Air
In response to the coexistence of multiple toxic gases underground, multi-gas composite sensors need to be deployed instead of single gas sensors. In the excavation face (where toxic gas concentration is the highest), 1 unit is arranged every 50 meters; in the stoping face, 1 unit is arranged every 100 meters; at key nodes such as roadway intersections and refuge chambers, an additional 1 unit is arranged. The smart multi-gas composite sensor can simultaneously monitor four core gases: CO (0-1000ppm), H₂S (0-100ppm), NO₂ (0-20ppm), and O₂ (0-25%), with accuracies of ±5%FS, ±3%FS, ±2%FS, and ±0.5%FS respectively, and a response time of ≤20 seconds. The sensor adopts an anti-corrosion design: the inner wall of the gas chamber is coated with polytetrafluoroethylene to prevent acidic gases such as hydrogen sulfide and sulfur dioxide from corroding the sensor elements; the air inlet is equipped with a dust filtering device (filtering accuracy ≥99.9%) to avoid dust blocking the gas chamber and causing measurement errors.
2. Smart Dust Concentration Sensor: The "Dust Guardian" for Respiratory Safety
Underground blasting, excavation, and transportation processes will generate a large amount of dust, among which respirable dust (particle size ≤5μm) is easy to deposit in the lungs, leading to diseases such as silicosis. Smart dust concentration sensors need to be deployed at the excavation face (5-10 meters from the heading), the stoping face (5 meters from the coal cutting machine), and the conveyor belt transfer point (dust diffusion source), with each sensor covering a radius of about 10 meters. Using the laser scattering principle, the measurement range is 0-1000mg/m³, the accuracy is ±10%, and it can distinguish between total dust and respirable dust (through a built-in particle size screening module). Intelligent functions include: 1. Automatic cleaning: an ultrasonic cleaning device is started once every 30 minutes to remove dust on the surface of the sensor probe to avoid high measurement values (traditional sensors can have an error of 30%-50% due to dust adhesion); 2. Ventilation linkage: when the dust concentration exceeds 4mg/m³, it automatically triggers the nearby local ventilator to speed up to reduce the dust concentration to within the standard; 3. Occupational health warning: records the dust exposure time of miners in the working area. If the average daily dust concentration in a certain area exceeds 2mg/m³ and the exposure time exceeds 6 hours, a reminder is pushed that "individual protection needs to be strengthened in this area (such as wearing dust masks)."
3. Roadway Roof Separation Sensor: The "Structural Monitor" for Underground Support
Relative displacement between rock layers is a precursor to collapse. 1 roof separation sensor needs to be arranged every 50 meters in the excavation roadway, and 1 unit every 30 meters in the stoping roadway. The sensor adopts "anchor bolt" installation, fixing the deep anchor bolt in the stable rock layer (depth ≥2 meters) and the shallow anchor bolt in the roadway support layer (depth 0.5 meters). By measuring the relative displacement between the deep and shallow anchor bolts, the roof separation condition is judged. The measurement range is 0-50mm, the accuracy is ±0.1mm, and the data refresh frequency is 1 time/5 minutes (normal state) and 1 time/1 minute (separation rate exceeds 0.1mm/hour). When the separation displacement exceeds 3mm (warning threshold), the sensor emits an audible and visual alarm (visible distance underground ≥5 meters), and uploads data to the monitoring center to remind support personnel to reinforce the roadway in time (such as increasing the number of anchor bolts and spraying concrete).
Key Technical Parameters and Intelligent Upgrades
In response to the environmental differences between open-pit and underground mines, smart sensors have been customized and optimized in technical parameters and intelligent functions, as follows:
III. Application Practice of Sensors in Non-Metallic Mines (Limestone, Sand and Gravel Mines, etc.)
As basic raw materials for construction, building materials, chemical industry, and other industries, the mining and processing of non-metallic mines are characterized by "large-scale, high-load, and continuous operations." Open-pit non-metallic mines such as limestone and sand and gravel mines need to process ores into aggregates through processes such as blasting, crushing, and conveyor belt transportation. During the operation, they face risks such as equipment wear, level loss of control, and conveyor belt breakage; underground mining of non-metallic mines (such as gypsum and talc mines) needs to deal with problems such as roadway support failure and dust exceeding standards. Smart sensors achieve efficient monitoring of the entire mining and processing process through structural optimization and working condition adaptation design.
Core Sensors in Mining and Processing Links
The monitoring needs of non-metallic mines are concentrated in key links such as blasting, crushing, transportation, and storage. The deployment of core sensors is as follows:
1. Radar Level Sensor: The "Level Manager" for Storage and Bins
The raw material bins (storing blasted ores) and finished product bins (storing crushed aggregates) of limestone and sand and gravel mines usually have a capacity of 1000-5000m³. If the level is too high, it will easily lead to overflow (causing raw material waste and equipment blockage); if the level is too low, it will easily lead to idling of downstream crushing and screening equipment (reducing production efficiency). Smart radar level sensors need to be deployed at the top center of the bin, using 24GHz high-frequency radar wave technology to penetrate the dust environment in the bin and achieve non-contact level measurement. The sensor needs to adopt a "parabolic antenna" design (antenna diameter ≥250mm) to enhance the radar wave focusing ability and avoid signal interference caused by the reflection of ore edges and corners; at the same time, it has a built-in "dust compensation algorithm" that can correct the radar wave propagation speed according to the dust concentration in the bin (by linking with dust sensors) to improve the level measurement accuracy.
2. Conveyor Belt Tension and Deviation Sensor: The "Safety Line" for Material Transportation
Non-metallic mine conveyor belts are usually several kilometers long (e.g., the length of large limestone mines can reach 5km), with a conveying capacity of 1000-3000t/h. Long-term high-load operation is prone to abnormal tension (too loose causing slipping, too tight causing breakage) and deviation (wear of the conveyor belt edge, or even tearing). The deployment of sensors is as follows:
(1) Tension sensor: installed on the bearing seats of the driving roller and redirecting roller of the conveyor belt, with 2 units arranged for each roller (monitoring the bearing tension on both sides respectively), using the strain measurement principle, the measurement range is 0-50kN (suitable for conveyor belts 1.2-1.8m wide), and the accuracy is ±2%FS;
(2) Deviation sensor: installed on the frames on both sides of the conveyor belt, with 1 group (2 units per group, monitoring left and right deviation respectively) arranged every 50 meters, using a swing arm structure. When the conveyor belt deviates and touches the swing arm, the sensor triggers a signal, and the swing arm reset force is ≥5N (to avoid false alarms caused by slight vibrations).
3. Crushing System Temperature and Vibration Sensor: The "Health Monitor" for Core Equipment
Jaw crushers and cone crushers are the core equipment for non-metallic mine processing. They bear huge impact forces when crushing ores, and are prone to faults such as bearing overheating (insufficient lubrication), liner wear (leading to reduced crushing efficiency), and excessive machine vibration (loose parts). The deployment of sensors is as follows:
(1) Temperature sensor: installed on the main shaft bearing seat of the crusher, with 2 units arranged for each crusher (monitoring the front and rear bearings respectively), using the contact platinum resistance (PT100) measurement principle, the measurement range is 0-200℃, the accuracy is ±1℃, and the response time is ≤5 seconds;
(2) Vibration sensor: installed on the side of the crusher (close to the main shaft), with 1 unit arranged for each, using the piezoelectric vibration measurement principle, the measurement range is 0-10mm/s (vibration velocity), the accuracy is ±0.1mm/s, and the response frequency is 10-1000Hz (covering the normal operation and fault vibration frequencies of the crusher).
4. Mine Dust Sensor (Processing Link): The "Dust Protector" for Environment and Equipment
The crushing and screening links of non-metallic mines will generate a large amount of dust (such as limestone dust and sand and gravel dust), which not only affects the health of operators but also leads to poor equipment heat dissipation (dust covering the motor cooling fins) and sensor signal interference (dust blocking radar waves). Smart dust sensors need to be deployed near dust sources such as the crusher feed inlet, screening machine discharge outlet, and conveyor belt transfer points, with 1 unit arranged at each place. It uses the laser backward scattering principle, with a measurement range of 0-2000mg/m³ and an accuracy of ±10%. It has an "anti-high-concentration dust" design. The sampling gas path adopts a spiral structure to avoid direct impact of high-concentration dust on the sensor probe. At the same time, it has a built-in compressed air reverse blowing device (reverse blowing once every 15 minutes) to remove dust on the probe surface.
Key Technical Parameters and Adaptation Features
Non-metallic mine sensors need to focus on solving three core problems: "anti-blockage, wear resistance, and dust resistance." According to the working condition differences of different equipment, the technical parameters and adaptation designs are as follows:
Typical Case: Application of a Limestone Mine Crushing and Conveying System
A large limestone mine is located in Central China, with an annual output of 3 million tons, mainly supplying surrounding cement enterprises. Its crushing and conveying system includes 2 raw material bins (capacity 3000m³/bin), 1 jaw crusher (processing capacity 1500t/h), 1 cone crusher (processing capacity 1200t/h), and 3 conveyor belts (total length 8km). Previously, inaccurate level monitoring led to raw material bin overflow (wasting about 200 tons of ore per month), conveyor belt deviation led to 2-3 shutdowns for maintenance per month, and crusher bearing overheating and burning occurred once (direct loss of about 150,000 RMB). In 2025, the mine deployed a smart sensor system. After application, the raw material bin overflow phenomenon was completely eliminated, the number of conveyor belt shutdowns for maintenance dropped to 0-1 times per month, the crusher bearing failure rate dropped by 90%, and direct economic losses were reduced by about 1.2 million RMB/year.
IV. Comparison of Core Performance Differences of Sensors for Different Minerals
Coal mines, metal mines, and non-metallic mines have significant differences in mining environment, risk characteristics, and equipment working conditions, leading to different core performance requirements for smart sensors. Clarifying the performance differences of sensors for different minerals can not only avoid monitoring failures caused by "one-size-fits-all" selection but also provide adaptation references for cross-mineral applications.
Protection Performance Differences: Core Guarantee for Adapting to Extreme Environments
The harshness of the environment varies among different minerals, and there are obvious differences in the requirements for sensor shell protection, anti-corrosion, and anti-vibration performance:
1. Underground Coal Mine Sensors: Explosion-proof and Waterproof/Dustproof Priority
Underground coal mines belong to high-gas environments. Sensors need to meet the ExdIMb explosion-proof rating (suitable for methane and coal dust explosion environments). The shell material needs to be cast aluminum or stainless steel (thickness ≥3mm) to prevent sparks caused by collisions; at the same time, the underground environment is damp and dusty (relative humidity often reaches over 90%, and coal dust concentration can reach 1000mg/m³). The protection rating needs to reach IP68 (completely dustproof and can be soaked in 1-meter deep water for a long time). The air inlet of the gas chamber needs to be equipped with a three-stage dust filtering device (primary + medium + high efficiency filtration) to avoid gas measurement errors caused by coal dust blockage.
2. Metal Mine Sensors: Equal Emphasis on Open-Pit Weather Resistance and Underground Anti-Corrosion
(1) Open-pit metal mine sensors: need to withstand UV exposure (annual radiation can reach 5000MJ/m²), rainstorm scouring (hourly rainfall ≥25mm), and extreme temperatures (-30℃~70℃). The shell needs to be made of polyester alloy or fluorocarbon coated steel plate (thickness ≥2mm), with a protection rating of IP66 (completely dustproof and can withstand strong water spraying). At the same time, it has UV aging resistance (service life ≥5 years); for example, the solar panel of the open-pit slope displacement sensor needs to adopt an anti-glare coating to avoid the decrease in transmittance caused by UV.
(2) Underground metal mine sensors: need to resist the corrosion of acidic gases such as hydrogen sulfide and sulfur dioxide (gas concentration can reach 100ppm). The inner wall of the shell needs to be coated with polytetrafluoroethylene (thickness ≥0.1mm), and the gas chamber components use corrosion-resistant precious metal electrodes (such as gold electrodes) to avoid component oxidation caused by acidic gases; for example, the gas chamber of the multi-gas composite sensor needs to undergo corrosion detection every 3 months to ensure the coating is intact.
3. Non-Metallic Mine Sensors: Key to Wear Resistance and Anti-Adhesion
The crushing and transportation links of non-metallic mines have a large number of hard particles (e.g., limestone particle Mohs hardness 3-4, sand and gravel particle Mohs hardness 6-7). Sensors are easy to be worn by particle impact, and dust is easy to attach and cause signal interference: the shell needs to be made of high manganese steel or tungsten carbide material (hardness HRC≥55), and impact resistance ≥10J/cm²; the level sensor antenna needs to be coated with polytetrafluoroethylene anti-stick coating (thickness ≥0.05mm) to avoid limestone powder and gypsum powder adhesion; for example, the swing arm of the conveyor belt deviation sensor needs to be made of tungsten carbide material, and its service life is extended by more than 3 times compared to ordinary steel swing arms.
Functional Requirement Differences: Monitoring Focus Matching Core Risks
Different minerals have different safety risks and production efficiency bottlenecks, leading to different functional requirements:
1. Coal Mines: Dual Core Monitoring of Gas and Roof
The primary risks in coal mines are gas explosions and roof collapses. Sensors need to have dual functions of "gas concentration prediction + roof force analysis": gas sensors need to fuse wind speed and temperature data for multi-parameter correction to avoid false alarms from single concentration monitoring; roof pressure sensors need to support data comparison of adjacent supports to identify hidden dangers of uneven force. In addition, considering the limited communication conditions in underground coal mines (5G signal coverage has blind spots), sensors need to have local data storage functions (storage capacity ≥16GB). When the network is interrupted, they can store data locally for more than 3 days and automatically upload it after the network recovers.
2. Metal Mines: Collaborative Monitoring of Open-Pit Slopes and Underground Toxic Gases
Metal mines need to achieve collaborative prevention and control of open-pit and underground risks: open-pit mine sensors need to have the "rainfall-displacement" linkage function to automatically increase the slope monitoring frequency during rainstorms; underground sensors need to have the "toxic gas tracing + ventilation linkage" function to automatically adjust the ventilator speed after locating the leak source. In addition, blasting operations in metal mines are frequent. Blasting vibration sensors need to have the function of linking with blasting plans, automatically calculating the safety vibration threshold according to the explosive amount to avoid errors caused by manual settings.
3. Non-Metallic Mines: Dual Guarantee for Equipment Health and Material Flow Stability
The core need of non-metallic mines is to ensure the continuous operation of crushing and conveying equipment. Sensors need to focus on "equipment fault warning + precise level control": crusher vibration sensors need to filter the inherent vibration frequency of the equipment and only monitor abnormal vibrations; conveyor belt tension sensors need to be closed-loop controlled with the driving motor to adjust the tension in real time to avoid slipping/breakage; radar level sensors need to have the level trend prediction function to match the feeding and crushing capacity in advance to avoid overflow or empty bins.
Algorithm Adaptation Differences: Optimization Direction for Coping with Complex Data Characteristics
The data characteristics collected by sensors for different minerals are different (e.g., coal mine gas data fluctuates frequently, and metal mine slope data changes slowly), requiring targeted algorithm optimization:
1. Coal Mine Sensors: Combination of Anti-Interference Algorithm and Short-Term Prediction
Underground coal mine data is greatly affected by mechanical vibration and electromagnetic interference. Gas sensors need to adopt the "wavelet transform denoising algorithm" to filter electromagnetic interference (frequency 50-100Hz) generated by equipment such as coal machines and pump stations to improve concentration measurement accuracy; roof pressure sensors need to adopt the "short-term trend prediction algorithm" (based on data from the past 1 hour) to identify precursors of sudden pressure changes and avoid warning delays caused by long-term data smoothing.
2. Metal Mine Sensors: Long-Term Trend Analysis and Multi-Source Data Fusion
The slope displacement in open-pit mines changes slowly (daily displacement ≤0.2mm). The "long-term time series analysis algorithm" (based on data from the past 30 days) needs to be adopted to identify the slow rising trend of displacement rate; underground multi-gas sensors need to adopt the "multi-source data fusion algorithm," combining ventilation volume and personnel location data to judge the actual threat degree of toxic gases to personnel.
3. Non-Metallic Mine Sensors: Combination of Real-Time Closed-Loop Control and Fault Diagnosis
Non-metallic mine equipment runs at high speeds (conveyor belt speed reaches 2-3m/s, crusher speed reaches 500-1000r/min). The sensor algorithm needs to have "millisecond-level real-time response" capability: after the conveyor belt deviation sensor triggers a signal, the algorithm needs to calculate the deviation amount and output an adjustment command within 500ms; the crusher temperature sensor needs to adopt the "fault tree diagnosis algorithm" to judge the fault type (such as insufficient lubrication vs. bearing wear) according to data such as temperature change rate and vibration value, and push targeted maintenance suggestions.
Power Supply Mode Differences: Adapting to Energy Conditions of Different Minerals
The convenience of power supply varies among different minerals, and sensors need to match diversified power supply modes:
1. Underground Coal Mines: Mainly Wired Power Supply, Supplemented by Battery Backup
Underground coal mine roadways are equipped with complete power supply lines (127V/660V mine power supply). Sensors prioritize wired power supply and are equipped with lithium battery backup (capacity ≥5Ah). When the power supply is interrupted, they can work continuously for more than 8 hours to avoid safety hazards caused by monitoring interruption.
2. Open-Pit Areas of Metal Mines: Solar Energy + Lithium Battery Power Supply
Open-pit metal mine slopes, blasting areas, and other areas are far away from power supply lines. Sensors adopt the "solar panel + lithium battery" power supply mode: the solar panel power is ≥10W (adapting to rainy and cloudy weather), the lithium battery capacity is ≥20Ah, supporting low-power mode (data refresh frequency drops to 1 time/10 minutes during non-monitoring periods), and the battery life on rainy days is ≥7 days; for example, the solar panel of the open-pit slope displacement sensor needs to be installed at a 30° tilt to maximize sunlight reception.
3. Non-Metallic Mine Processing Areas: Combination of Wired Power Supply + POE Power Supply
The crushing and conveying systems of non-metallic mines are concentrated in processing plants with convenient power supply. Sensors use wired power supply (220V mains). For level sensors deployed at high positions (such as bin tops), POE (Power Over Ethernet) power supply is used to transmit data and power simultaneously through network cables, avoiding the need to lay separate power supply lines and reducing installation costs; for example, the raw material bin radar level sensor uses POE power supply, and the installation time is shortened by 50% compared to traditional wired power supply.
Adaptation Suggestions for Cross-Mineral Applications
If it is necessary to apply sensors of one mineral to other minerals, the following differences need to be transformed:
1. Coal mine gas sensors used in underground metal mines: need to improve the ability to resist acidic gas corrosion (coated with polytetrafluoroethylene), adjust the gas concentration threshold (gas emission in underground metal mines is low, and the alarm threshold can be reduced from 1.0% CH₄ to 0.5% CH₄), and turn off the wind speed linkage function.
2. Metal mine slope displacement sensors used in non-metallic mine dumps: need to increase dust resistance (shell protection rating upgraded from IP66 to IP67), adjust the displacement rate threshold (rock and soil mass in non-metallic mine dumps is looser, and the warning threshold is reduced from 0.3mm/hour to 0.2mm/hour), and adapt to solar power supply.
3. Non-metallic mine conveyor belt sensors used in underground coal mines: need to improve the explosion-proof rating (from non-explosion-proof to Ex d I Mb), replace the shell material resistant to coal dust wear (high manganese steel changed to stainless steel), and adjust the tension threshold (coal mine conveyor belts transport coal blocks, and the tension threshold is reduced from 50kN to 40kN).
V. Intelligent Development Trends of Mine Smart Sensors
As the mining industry transforms towards "intelligentization, unmanned operation, and greenization," smart sensors, as the core of the mine perception layer, are developing in four directions: deep AI integration, multi-modal perception, autonomous operation and maintenance, and green low power consumption. These trends will not only improve the accuracy and real-time performance of mine monitoring but also promote the upgrade of mines from "passive monitoring" to "proactive warning and intelligent decision-making." The following analysis is presented:
Deep Integration of AI Algorithms: From "Data Collection" to "Intelligent Prediction"
The deep integration of AI algorithms will endow sensors with the full-process capability of "data analysis - trend prediction - fault diagnosis," which is reflected in the following three levels:
1. Anomaly Pattern Recognition Based on Deep Learning
By training a large amount of mine historical data (such as gas concentration, slope displacement, and equipment vibration data), deep learning models (such as CNN convolutional neural networks and LSTM long short-term memory networks) are constructed to enable sensors to automatically identify abnormal data patterns, avoiding missed/false alarms caused by relying on manually set thresholds. For example, after adopting the LSTM model, coal mine gas sensors can identify the abnormal pattern of "slow rise followed by sudden drop" in gas concentration (which may indicate gas leakage in the goaf), and the warning accuracy rate is improved from 85% to 98%; metal mine slope displacement sensors using the CNN model can distinguish between "normal displacement caused by rainfall" and "abnormal displacement caused by rock and soil mass instability," reducing the number of false alarms during rainstorms by 70%.
2. Edge-Cloud Collaborative Real-Time Decision-Making Algorithm
In response to the large amount of mine data and transmission delay, a collaborative mode of "edge-side lightweight model + cloud-side big data analysis" is adopted: a lightweight AI model (such as TensorFlow Lite) is deployed on the sensor side to achieve millisecond-level local anomaly judgment (such as immediately triggering shutdown when equipment vibration exceeds the standard); a full-data model is deployed on the cloud to conduct correlation analysis of multi-mine and multi-sensor data (such as analyzing the gas emission laws of multiple coal mines in a certain area to predict regional gas risks). For example, after deploying edge-cloud collaborative sensors in a large coal mine, the gas accident response time was shortened from 5 minutes to 1 minute, and the regional gas risk prediction accuracy reached 90%.
3. Continuous Optimization of Self-Evolutionary Algorithms
Sensors have built-in self-evolutionary algorithms that can dynamically adjust model parameters according to the actual working conditions of the mine, avoiding the failure of fixed algorithms when working conditions change. For example, the self-evolutionary algorithm of the non-metallic mine crusher vibration sensor can automatically adjust the vibration warning threshold (from 3mm/s to 3.5mm/s) according to changes in ore hardness (such as limestone hardness changing from Mohs 3 to Mohs 4); the self-evolutionary algorithm of the coal mine roof pressure sensor can dynamically optimize the pressure prediction model according to the support time of the support (changes in rock and soil mass stress distribution after 1 month of support), and the warning accuracy rate continues to be maintained above 95%.
Multi-Modal Perception Fusion: From "Single Monitoring" to "Comprehensive Perception"
By integrating multiple sensing elements (such as gas, vibration, temperature, and displacement sensors), multi-dimensional data collection of the same monitoring object is realized, and then the monitoring accuracy and reliability are improved through data fusion algorithms. Specific application scenarios include:
1. Multi-Health Parameter Fusion Monitoring of Mine Equipment
Integrating vibration, temperature, noise, and current sensors to conduct multi-dimensional health monitoring of core equipment such as crushers and conveyor belt motors. Through fusion algorithms such as D-S evidence theory and Kalman filtering, the equipment fault type and severity are comprehensively judged. For example, after deploying multi-modal sensors on the motor of a coal mine scraper conveyor, it can simultaneously monitor motor vibration (judging bearing wear), stator temperature (judging winding insulation aging), and current (judging overload). The fault diagnosis accuracy rate is improved from 70% to 95%, avoiding missed alarms from a single sensor (such as missing early bearing wear by only monitoring temperature).
2. Multi-Risk Factor Fusion Warning of Mine Environment
Integrating gas, dust, temperature and humidity, and wind speed sensors to monitor multiple risk factors in areas such as underground working faces and open-pit mine slopes. The environmental risk level is judged by fusing data through fuzzy logic algorithms. For example, the multi-modal sensor at the underground excavation face of a metal mine can fuse CO concentration (≥24ppm), dust concentration (≥4mg/m³), and wind speed (≤0.5m/s) data. When any two items exceed the standard, a "Level 2 warning" is triggered; when all three items exceed the standard, a "Level 1 warning" is triggered, avoiding excessive warning when a single gas exceeds the standard but ventilation is good.
3. Cross-Scenario Multi-Sensor Data Linkage
Breaking the boundaries of mineral types and regions, realizing the linkage analysis of sensor data in different scenarios, and improving the overall risk prevention and control capability of mines. For example, the rainfall sensor data of open-pit metal mines can be linked with the flow sensor of underground drainage pumps. When the rainfall exceeds the warning threshold, the underground drainage pump is started in advance to avoid rainwater seeping underground and causing water accumulation; the gas sensor data of coal mines can be linked with the start/stop sensor of the conveyor belt. When the gas concentration approaches the alarm threshold, the conveyor belt speed is automatically reduced (to reduce sparks generated by coal block friction), and personnel are reminded to evacuate.
Autonomous Operation and Maintenance Capability: From "Manual Maintenance" to "Self-Diagnosis and Self-Repair"
Mine sensors are deployed in harsh environments (such as deep underground and slope tops). Manual maintenance is costly and difficult. Autonomous operation and maintenance will be the key to reducing operation and maintenance costs and improving sensor reliability. Specific development directions include:
1. Self-Diagnosis and Fault Warning Function
The sensor has a built-in self-diagnosis module that can monitor its own hardware status in real time (such as battery level, sensing element sensitivity, and communication module signal strength). When a hardware fault precursor appears (such as battery level below 20% or element sensitivity dropping by 10%), it automatically pushes a maintenance reminder.
2. Self-Calibration and Drift Compensation Technology
In response to zero drift and sensitivity decline during long-term use of sensors, self-calibration technology is adopted — through built-in standard reference sources (such as standard methane gas chambers built into gas sensors and PT100 standard resistors built into temperature sensors), zero and range calibration are automatically performed regularly without manual disassembly for calibration.
3. Modular Design and Hot-Swap Function
Adopting modular design, the sensor is divided into a sensing module, a communication module, and a power module. Each module is independently packaged and can be hot-swapped — when a module fails, there is no need to replace the entire sensor, only the faulty module needs to be replaced, reducing maintenance costs and downtime.
Green Low-Power Technology: From "High Energy Consumption" to "Long Battery Life and Low Emissions"
In response to the green development requirements of mines, sensors will adopt low-power design and clean energy power supply technology to reduce energy consumption and carbon emissions. Specific technical paths include:
1. Low-Power Sensing Elements and Communication Protocols
Adopting new low-power components, such as MEMS sensors (power consumption ≤1mA), low-power communication protocols (such as LoRaWAN, NB-IoT, communication power consumption reduced by 80% compared to 4G), combined with dynamic sleep technology (entering sleep mode during non-monitoring periods, power consumption drops to below 0.1mA), extending battery life.
2. Combination of Clean Energy and Energy Recovery for Power Supply
In addition to solar power supply, explore energy recovery technology in mine scenarios. Convert mechanical energy/thermal energy such as mine machinery vibration, temperature difference, and pressure into electrical energy to power sensors, achieving "zero carbon emission" power supply.
3. Environmentally Friendly Materials and Degradable Design
Use environmentally friendly materials to manufacture sensor shells and components, reducing the use of heavy metals and toxic chemicals, and exploring degradable sensor designs. For disposable or short-term deployed sensors (such as temporary monitoring sensors for blasting vibration), degradable plastic (such as polylactic acid PLA) shells and degradable batteries (such as magnesium-air batteries) are used, which can be naturally degraded after use to avoid secondary pollution of the mine environment.
Challenges in Implementing Trends and Response Suggestions
The intelligent development of mine smart sensors faces three major challenges: first, AI algorithm training requires a large amount of high-quality mine data, but mine data has the problems of "fragmentation and strong privacy"; second, multi-modal sensor fusion requires unified data interfaces and communication protocols, but current mine sensors have the problems of "incompatible protocols and diverse data formats" (e.g., coal mines use the Modbus protocol, and metal mines use the Profibus protocol); third, autonomous operation and maintenance and low-power technology increase sensor costs (costs increase by 20%-30%), which is difficult for small and medium-sized mining enterprises to bear.
Response suggestions include:
1. The National Mine Safety Administration takes the lead in establishing a "mine sensor data sharing platform," formulating data sharing standards, and encouraging mining enterprises to anonymously share historical data for AI algorithm training;
2. Formulate a unified communication protocol standard for mine smart sensors (such as a mine-specific protocol based on 5G-A), and mandate newly deployed sensors to be compatible with the standard protocol;
3. Introduce a "smart sensor subsidy policy" to provide 30%-50% subsidies to small and medium-sized mining enterprises for purchasing sensors with AI and low-power functions to lower the threshold for implementation.
VI. Current Status of Market Size of Mine Smart Sensors
Overview of the Global Mine Smart Sensor Market
In 2025, the global mine smart sensor market size reached 4.62 billion US dollars, a year-on-year increase of 8.5%. From the perspective of regional demand, it is mainly concentrated in major mineral resource countries and regions such as China, the United States, Australia, Canada, and Russia, accounting for more than 75% of the global market. Among them, the Chinese market has the highest share, reaching 35%. This is mainly due to China's vigorous promotion of mine intelligent transformation in recent years and the introduction of policies such as the "Guiding Opinions on Accelerating the Intelligent Development of Coal Mines" and the "Guidelines for the Intelligent Construction of Metal and Non-Metallic Mines," promoting the large-scale deployment of smart sensors in coal, metal, and non-metallic mines. The US market accounts for about 20%, and Australia accounts for about 15%. Mining enterprises in these two countries have a strong demand for high-end sensors and are willing to invest higher costs to improve mine safety and efficiency.
1. From the perspective of technology drivers, AI algorithm fusion, multi-modal perception, and other technology trends have significantly driven market growth: in 2025, the market size of global mine equipment sensors with AI anomaly recognition functions (such as crusher vibration sensors and conveyor belt tension sensors) increased by 13% year-on-year, higher than the overall market growth rate. This is mainly because these sensors can warn of equipment failures 3-5 days in advance, reducing mine downtime losses. Procurement volumes from large mining enterprises in the United States and Australia increased by more than 20% year-on-year; the market size of multi-modal environmental sensors (such as underground sensors integrating gas, dust, temperature, and humidity) increased by 11% year-on-year, and the application penetration rate in coal mines in China and Canada rose from 45% in 2024 to 58% in 2025, driving the overall coal mine sensor market to grow by 8%.
2. From the perspective of mineral demand structure, coal mines are the largest application field for global mine smart sensors, accounting for 40%. The main demand is concentrated in gas sensors, roof pressure sensors, and personnel positioning sensors; metal mines rank second, accounting for 30%, with key demand for slope displacement sensors, blasting vibration sensors, and underground toxic gas sensors; non-metallic mines account for 20%, with main demand for level sensors, conveyor belt sensors, and crusher vibration sensors; the remaining 10% comes from other mineral types such as rare metal mines and chemical mines.
Segmentation of the Chinese Mine Smart Sensor Market
1. Market Size and Structure
In 2025, the market size of China's mine smart sensors reached 1.72 billion US dollars (about 120 billion RMB), a year-on-year increase of 12.4%, with the growth rate higher than the global average. From the perspective of mineral segmentation: the coal mine market is the largest, reaching 690 million RMB, accounting for 45%. Among them, gas sensors account for 35% of the coal mine sensor market, and roof pressure sensors account for 25%; the metal mine market size is 460 million RMB, accounting for 30%. Slope displacement sensors and underground multi-gas sensors together account for 60% of the metal mine sensor market; the non-metallic mine market size is 320 million RMB, accounting for 21%. Level sensors and conveyor belt tension sensors are the main demand products, accounting for 70% together; the market size of other mineral types is 60 million RMB, accounting for 4%.
From the perspective of technology segmentation, different technology trends correspond to obvious growth differences in different products:
(1) AI algorithm sensors: in coal mine gas sensors, the proportion of products with LSTM trend prediction functions increased from 35% in 2024 to 50% in 2025, driving the coal mine gas sensor market to grow by 18%; in metal mine slope displacement sensors, the procurement volume of CNN pattern recognition products increased by 28% year-on-year, driving the slope sensor market to grow by 18%.
(2) Multi-modal fusion sensors: multi-modal sensors integrating gas, temperature and humidity, and wind speed in underground coal mines had their penetration rate increased to 50% in 2024, with sales increasing by 30% year-on-year; multi-modal crusher sensors integrating vibration, temperature, and current in non-metallic mines had sales increasing by 22% year-on-year, driving the crusher sensor market to grow by 15%.
(3) Low-power and self-powered sensors: low-power slope displacement sensors using solar energy + vibration energy recovery in open-pit mines, due to reducing operation and maintenance costs by 50%, had their procurement volume in western Chinese metal mines (such as Xinjiang and Inner Mongolia) increase by 40% year-on-year in 2025; low-power personnel positioning sensors in underground coal mines (using the NB-IoT protocol), due to extending battery life from 1 year to 3 years, had sales increase by 30% year-on-year.
2. Growth Dynamics Analysis
The growth of China's mine smart sensor market is mainly driven by three factors, all of which are deeply bound to technology trends:
First, the continuous promotion of coal mine intelligent transformation policies has driven the explosive demand for AI and multi-modal sensors. The National Mine Safety Administration requires that all large coal mines across the country basically achieve intelligentization by the end of 2025. By the end of 2025, the intelligentization rate of large coal mines nationwide has reached 92%, exceeding the phased target. Small and medium-sized coal mines are accelerating intelligent transformation. The number of sensors deployed in a single coal mine has increased from 500 units in 2023 to more than 1000 units in 2024, of which AI gas sensors and multi-modal environmental sensors account for 60% of the newly deployed volume.
Second, the increase in metal mine slope monitoring and underground safety demand has driven the growth of multi-modal and high-precision sensors. As the mining depth of metal mines increases (e.g., the average mining depth of iron mines in China has reached 800 meters), the risks of slope instability and underground toxic gas leakage have intensified, and mining enterprises have an urgent demand for "high-precision + multi-parameter" sensors. In 2024, in China's metal mine slope displacement sensor market, the proportion of products with an accuracy of ±0.1mm increased from 30% to 45%, and the procurement volume of multi-modal underground gas sensors (simultaneously monitoring CO, H₂S, NO₂) increased by 35% year-on-year, driving the overall metal mine sensor market to grow by 16%.
Third, the increase in non-metallic mine equipment upgrade demand has driven the demand for low-power and easy-to-maintain sensors. To meet the high requirements of the construction and building materials industry for aggregate quality, non-metallic mine enterprises are accelerating the intelligent upgrade of crushing and conveying equipment, requiring sensors to have long-term stable operation and low maintenance costs. In 2024, in China's sand and gravel mine conveyor belt sensors, the proportion of products with self-diagnosis functions increased from 25% to 40%, and the sales of limestone mines using laser self-calibrating level sensors increased by 25% year-on-year, driving the overall non-metallic mine sensor market to grow by 13%.
Conclusion
Smart sensors have become the core support for the intelligent transformation of mines: through applications in scenarios such as coal mine gas warning, metal mine slope prevention and control, and non-metallic mine equipment operation and maintenance, sensors have realized the transformation of mine risks from "manual inspection" to "automatic monitoring," and equipment management from "post-maintenance" to "pre-warning," significantly improving the safety production level and production efficiency of mines.
Multi-dimensional technology upgrades promote the evolution of mine sensors from "perception" to "cognition": the deep integration of AI algorithms endows sensors with the ability of anomaly pattern recognition and trend prediction; multi-modal perception fusion realizes comprehensive monitoring across parameters and scenarios; autonomous operation and maintenance reduce maintenance costs in harsh environments; and green low-power technology responds to the green development needs of mines.
The development of mine smart sensors is not only the result of technology iteration but also an inevitable requirement for the transformation of the mining industry from "high-risk, high-consumption, and low-efficiency" to "safe, green, and efficient." In the future, with technological breakthroughs and the improvement of the industrial ecosystem, smart sensors will become the core engine of mine intelligentization, promoting the mining industry to enter a new era of "more accurate perception, more intelligent decision-making, and more autonomous operation and maintenance."
Author | Guo Yuansheng, Deputy Director of the Science and Technology Committee of the Central Committee of Jiusan Society, Executive Vice Chairman of the China Sensor and IoT Industry Alliance
Editor | Yang Pengyue Art Editor | Ma Liya Supervisor | Zhao Chen