Today, as coal mines push forward with digitalization, the fully mechanized longwall face is no longer just a monitoring scenario with a lot of equipment and a lot of data. It is a tightly coupled business site where output, safety, pace, and coordination efficiency are bound together.
For coal mine operators, the real challenge is not a lack of data. It is that when the shearer, conveyors, hydraulic supports, pump stations, and safety sensors all keep producing vast amounts of data at the same time, the site still struggles to answer the most critical questions at a moment’s notice. Where exactly is the anomaly? Will it keep amplifying along the process chain? Is what we are seeing a short-term fluctuation, or a real risk that is already affecting output and safety?
This is also the dividing line that has become increasingly clear as intelligent coal mine monitoring moves into its next phase. Operators no longer only need to “bring the data in and render the screens.” They need data that actually supports judgment, explains anomalies, and helps the site act faster.
Fully mechanized mining faces: a new data-judgment challenge under tightly coupled production
The fully mechanized longwall face is the core unit of coal mine production and one of the most typical tightly coupled scenarios. The shearer cuts the coal, the armored face conveyor (AFC), stage loader, and extensible belt conveyor carry the coal continuously, the hydraulic supports hold the roof and advance behind the machine, and the emulsion pump station, gas sensors, dust sensors, and power supply system together keep the entire production chain running steadily. Any local fluctuation can propagate quickly along the chain from cutting to conveying to support to safety, and finally show up as output fluctuation, unplanned downtime, or even an escalating safety risk.
This complexity means the data problem at a fully mechanized longwall face has never been a single-point problem. In this example, the mine produces more than 8 million tonnes a year, its main seam averages 5.8 m thick, and the face uses fully mechanized single-pass full-seam extraction. The site is subject to extreme environmental interference such as heavy dust, high humidity, and strong vibration, and it also endures severe load swings of 50% to 300%. When the shearer cuts hard coal or hard partings, the current can double in an instant, and as the face advances, sensor communication can drop out frequently.
More importantly, the “three machines” of the fully mechanized longwall face (shearer, armored face conveyor, and hydraulic supports) are coupled in a strict process sequence with tight timing windows. The shearer’s haulage speed determines how much coal is cut, that volume sets the load on the conveying chain, and the hydraulic supports must advance within 3 to 5 support positions after the shearer passes. In other words, the site does not lack monitoring tools. The hard part is seeing the context quickly amid high-frequency change and judging, in time, the nature of a problem, how far it reaches, and how urgent the response should be.
Plenty of alerts, judgment still slow: missing data is not what holds back operations
The same fluctuation is visible, but its meaning is still unclear
The most common problem at a mining face is not that equipment “has no signal.” It is that an anomaly “has a signal but no conclusion.” For example, a rise in the shearer’s cutting current could mean the coal seam has turned harder or the hard-parting content has increased, or it could mean the picks are worn and the machine is under abnormal load. At the same time, a drop in haulage speed could mean the automatic protection has kicked in, or it could mean someone has intervened manually. Looking at any single value in isolation, a frontline operator cannot reach a reliable conclusion. The value has to be placed in the context of a specific cut, a specific operating condition, and specific upstream and downstream relationships before anyone can tell whether it is a risk that needs an immediate response.
This is the common tension many industrial scenarios face. It is not that there is no system or no data. It is that data keeps flowing in but cannot be understood quickly enough, let alone directly support on-site decisions.
A single-point alert has triggered, but the chain-wide impact is still hard to judge
The difficulty at a fully mechanized longwall face is also that problems rarely stay at the level of a single machine. An anomaly in the shearer’s cutting state usually carries through to the AFC load, the stage-loader pace, and the belt conveying efficiency, and ultimately shows up as lower output for the whole cut. A change in hydraulic support pressure is often more than a “support state fluctuation” as well. It can develop into declining support force, roof settlement, and even rising methane emissions.
So what really caps operational efficiency is not too few alerts but alerts without context. The site can usually “see that a problem has happened,” yet it struggles to tell, in the shortest possible time, whether this is a brief disturbance, a local anomaly, or something already spreading along the production chain. This lag in judgment directly lengthens troubleshooting time and magnifies the cost of misjudgment and delayed response.
The business consequences are not abstract: delayed judgment amplifies both output loss and safety risk
On a mining face, slow judgment is never a minor administrative inconvenience. It converts directly into lost output and added safety pressure. Take a hard-parting scenario. When a high-hardness hard parting appears in the seam, the shearer’s left cutting current climbs quickly past the 250 A alert threshold, with an anomalous peak around 310 A. Meanwhile, ranging arm vibration rises to 6.8 mm/s, the haulage system automatically slows to 6 m/min to protect the equipment, and the AFC load rate drops further to 48%. If this kind of anomaly repeats across several cuts, the case gives a result of roughly 1,250 tonnes lost across five cuts, an economic loss of about US$97,000.
Another, more worrying class of problem is the cascading anomaly triggered by periodic weighting. A rise in support leg pressure is not unusual by itself. But when it is followed by leakage from aging seals, declining support force, roof settlement, and opened gas pathways, the risk at the site quickly shifts from “equipment anomaly” to “safety anomaly.” In this scenario, methane concentration can climb to 0.88%, already close to the boundary for high-risk response. In other words, what makes many risks genuinely dangerous is not that they strike suddenly. It is that they were already evolving, and the site lacked a chain that could quickly recognize and explain them.
From passive monitoring to proactive judgment: reorganizing the data chain
Object modeling: scattered tags return to a unified business view
The judgment problem at a fully mechanized longwall face does not really come from the number of devices or monitoring pages. It comes from how the data is organized. Through a tree-based hierarchy, TDengine maps objects such as sensors, equipment, systems, and production lines into one clear data catalog, and every node can carry attributes, analyses, panels, events, and linked documents. After object modeling, the shearer, AFC, hydraulic supports, emulsion pump station, and gas sensors are no longer scattered tags. They are understandable objects sitting on the same business chain.
Figure 1: A tree hierarchy organizes plant assets and measurement points into a unified business view
This change matters to coal mine operators directly. In the past, many anomalies were hard to judge not because historical data was missing, but because the same data carried different risk meaning across different equipment, different operating conditions, and different shifts. Only after the data structure, asset relationships, and business semantics are straightened out can later analysis and judgment rest on a common foundation. Once there is a unified entry point built around objects, the site no longer sees isolated tags. It sees business objects that can be understood together with the cut number, operating condition, and upstream and downstream state.
Real-time analysis and event linkage: alerts return to a complete process context
Finding an anomaly is not enough to improve on-site response. What a fully mechanized longwall face needs is that, the moment an anomaly appears, the system can also present the key context around it: which cut this fluctuation occurred in, what operating condition it corresponds to, whether upstream and downstream equipment have been affected, and whether it is moving together with other risk indicators. On top of data modeling, TDengine adds real-time analysis, event management, and alert linkage. It continuously monitors the data stream, automatically generates KPIs, detects anomalies, and triggers events, organizing each event together with its associated assets, duration, severity, and context trend, instead of just throwing out an isolated alert.
Take a rise in the shearer’s cutting current. In that situation the site does not only need to know that “the current exceeded the limit.” It also needs to see, at the same time, the linked changes in haulage speed, conveying load, ranging arm vibration, and current operating state, to tell whether this is a brief impact or a persistent coal-quality anomaly. And when support leg pressure is abnormal, nobody should fixate on the pressure value alone. It has to be read together with emulsion pressure, fluid level, support state, and methane concentration changes to judge whether the problem is evolving toward support failure and gas risk. The real value is not how many more alerts are added. It is that an anomaly finally has a process context that can explain it.
Figure 2: General information settings for real-time analysis
Figure 3: Trigger conditions for real-time analysis
Figure 4: Action after the real-time analysis trigger
TDengine also provides intelligent data querying. You describe the real-time analysis requirement in natural language, the AI understands it and can generate the real-time analysis task, which greatly reduces the difficulty and requirements of manual configuration. TDengine also provides proactive recommendations. It can detect the scenario and recommend the real-time analysis tasks that should be created for it, further reducing the dependence on industry knowledge and lowering the difficulty of data analysis.
Process analysis and AI insight: judgment moves from experience-driven to evidence-driven
Once data is organized into object relationships and anomalies can be explained along the process chain, insight no longer depends on a handful of highly experienced experts. Shift leaders, dispatchers, equipment managers, and safety managers can all share the same basis for judgment around the same time axis, the same business objects, and the same set of key indicators. The way people work together shifts from “each looking at their own page” to “reaching a consistent judgment around the same facts,” and on-site decisions become faster and more stable.
TDengine provides process analysis, correlation analysis, regression, batch comparison, anomaly detection, and natural-language Q&A for panel interpretation, helping users go from “what happened” to “why it happened.” Troubleshooting that used to require repeated confirmation across multiple systems and multiple people can now be done largely within one set of objects, events, and analysis chains. The site is no longer left with “we know there is a problem.” It can reach an evidence-backed judgment faster and turn that judgment into action.
Figure 5: AI interpretation and data mining on the analysis panel
Based on the anomaly events that occur, TDengine supports root-cause analysis with AI. It automatically searches relevant historical data, forms hypotheses about the cause, verifies those hypotheses, and produces a structured analysis report, with less manual work, greatly reducing the reliance on IT skills and on industry knowledge and experience.
Figure 6: AI root-cause analysis of an event
Analysis loops in typical anomaly scenarios
Hard parting scenario: the judgment path from a cutting-current alert to whole-cut output
In the hard-parting scenario, the first signal captured by the system comes from the mining system. Shearer-1’s left cutting current climbs from a baseline of 180 A, crosses the 250 A alert threshold, and stays there for more than 5 minutes, triggering the Warning alert defined in a standing rule. Right after that, ranging arm vibration rises to 6.8 mm/s, crosses the 5.0 mm/s alert line, and stays there for more than 3 minutes, triggering a Major alert. The two standing rules work in sequence, and the double-factor confirmation avoids false alarms while giving the site a clear entry point at once. This is no longer an isolated current fluctuation. It is a cutting anomaly that needs to be traced immediately.
After the alert triggers, the site adds the event to the analysis workbench and runs a three-step trace around Shearer-1 and AFC-1 to judge the nature of the anomaly, how far it has developed, and its business impact.
Step 1: Confirm the equipment protection response
Add the haulage speed attribute to the Shearer-1 object and watch whether it automatically drops after the current rises. The figure shows haulage speed falling from 11.5 m/min to 6.0 m/min, a drop of nearly 48%, far beyond the 20% judgment threshold. This confirms that the speed-reduction protection is active. The anomaly is not a mechanical jam or an electrical fault. It is the shearer’s normal protective behavior in response to a sudden change in coal-wall hardness.
Step 2: Confirm the degree of heat buildup
Next, add the left motor temperature attribute to judge whether this overload has produced enough heat load to affect the equipment’s condition. The figure shows left motor temperature rising steadily from 68 °C to 105 °C, crossing the 95 °C judgment threshold. The result indicates that the motor has entered a high thermal-load range and needs to dissipate heat through speed reduction or a short stop. It cannot be treated as a short-term overload that can be left alone.
Step 3: Quantify the impact on output
The trace then extends to the conveying stage. Add the load-rate attribute on AFC-1. The figure shows the AFC-1 load rate falling from a baseline of 72% to 48%, a drop of about 24 percentage points, above the 20% judgment threshold. This shows that the reduced cutting speed at the front has reached the conveying chain and this cut is losing output noticeably. At this point the nature of the anomaly and its business impact are both confirmed: the equipment protection intervened as designed, the motor is building up heat, and the whole cut’s output is being pulled down.
Figure 7: Correlation analysis of shearer cutting current anomaly across mining equipment variables
Around this analysis loop, several key conclusions come into focus. Cutting current and haulage speed form a typical mirror-image negative correlation, which directly confirms that the equipment’s self-protection works as intended. Vibration and cutting current show a strong positive correlation, so a sudden change in coal-wall hardness can be assessed from operating data before a geological report is available. The AFC load rate and shearer speed fall together, which turns “why is this cut short on output” from an experience-based guess into a data-backed conclusion.
Periodic-weighting scenario: the judgment path from pressure exceedance to gas risk
In the periodic-weighting scenario, the earliest alert also comes from a standing rule. During advance, leg pressure in the middle section of Shield-M1 fluctuates up from a baseline of 26 MPa to 35 MPa, crosses the 32 MPa alert threshold, and stays there for more than 15 minutes, triggering a Warning alert. Then the methane concentration at Gas-S1 rises from 0.25% to above 0.50%, crossing the site warning threshold, and stays there for more than 5 minutes, triggering a Major alert. One of these two standing rules sits at the convergence point of the causal chain and the other at the final safety line. Together they bring what could otherwise have been split into a “support event” and a “gas event” into one anomaly that must be traced immediately.
Once the event is added to the analysis workbench, the trace runs backward along the causal chain, working through three objects in sequence: Shield-M1, Emulsion-1, and Gas-S1.
Step 1: Distinguish normal weighting from support failure.
Keep watching the leg-pressure trend on Shield-M1. In the figure, pressure jumps sharply from 26 MPa to 35 MPa, then slowly falls back into the 18 to 20 MPa range, clearly below the 22 MPa judgment threshold. This “rise then fall” double-peak curve is not a typical response to periodic weighting. It is a sign that support force is already being lost, which points to a seal leak in the middle support group.
Step 2: Trace the emulsion system to assess the size of the leak.
Add the tank fluid level and outlet pressure attributes on Emulsion-1. The figure shows the tank level falling steadily from 70% to 42%, a drop of close to 28 percentage points, far beyond the 15% judgment threshold. The outlet pressure swings repeatedly in the 31 to 34 MPa range, which shows that make-up fluid can no longer hold the system pressure steady. Taken together, the two indicators show that the leak exceeds the pump station’s make-up capacity, the emulsion system is oscillating, and the support conditions are getting worse.
Step 3: Assess the gas safety risk.
The trace extends to the ventilation and safety stage. On Gas-S1, watch methane concentration and wind speed. The figure shows methane concentration climbing steadily from 0.25% to 0.88%, past the 0.65% warning line and approaching the 1.0% high-risk boundary. At the same time, wind speed automatically rises from 2.3 m/s to 3.5 m/s, which means the ventilation system has entered automatic dilution mode. The conclusion at this step: the safety risk is still within the range the system response can cover, but it needs close monitoring. If the ventilation response weakens or methane concentration keeps climbing, manual intervention must begin immediately.
Figure 8: Root-cause analysis of methane concentration anomaly across hydraulic support, emulsion pump, and gas sensor variables
Along these three steps, a clear root-cause chain is fully reconstructed. The rise in gas concentration comes from the emulsion system imbalance indicated by the falling fluid level. The emulsion imbalance comes from the seal leak. The seal leak comes from the frequent opening of the safety valve. And the frequent valve openings come from leg pressure being driven steadily higher by periodic weighting. The “rise then fall” double-peak curve of leg pressure shows the two-stage failure mode in a single chart. The roughly 4-hour lead of the fluid-level drop over the methane rise gives the site an extremely valuable prevention window. And this five-layer causal chain, traced back from gas concentration to periodic weighting, lets support anomalies and gas anomalies, which used to be handled separately, be explained as one evolving process within the same analysis path.
Intelligent coal mine monitoring: what is really needed is not more pages but more complete judgment
As coal mine operators keep building out their intelligent systems, what really sets the ceiling on the value of digitalization is no longer how much data has been brought in or how many pages have been built. It is whether that data can produce actionable judgment at the site. For a tightly coupled scenario like a fully mechanized longwall face, the hard part has never been collecting the data. It is, once there is plenty of data, understanding anomalies faster, explaining them, and turning the judgment into action.
TDengine turns scattered data into understandable business objects, converts alerts into explainable process judgments, and deepens that data capability into business capabilities that support output assurance, safety management, and more efficient coordination, delivering more efficient, precise, and sustainable business results for coal mine operators.
TDengine comes with a high-performance, distributed time-series database, Industrial Ontology modeling and an Industrial Agent Runtime, providing a full-stack technical solution for industrial data flows, from collection, storage, and real-time analysis to visualization, event management, and root-cause analysis. To learn more about TDengine, visit the website at www.tdengine.com and download the free trial.
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Install and deploy TDengine
Visit the TDengine Download Center, select TDengine All-in-One, choose the deployment platform and architecture that matches your environment, and follow the guided steps to complete the installation.
Load the sample data
On the first activation, on the sample-data loading selection screen choose Coal Mine Fully Mechanized Mining Face Monitoring and wait for the load to finish.
Customers who have already activated the product click the user avatar in the top-right corner, select the admin console, choose Sample Data on the left, and then select Coal Mine Fully Mechanized Mining Face Monitoring to load it. Wait a few minutes for the load to finish, and you can start exploring.
Coal Mine Fully Mechanized Mining Face Monitoring
