Mine production scheduling management method based on equipment failure prediction and health management

Abstract

The present application relates to the technical field of fault prediction and health management, and specifically discloses a mine production scheduling management method based on equipment fault prediction and health management, comprising the following steps: S1: collecting the fault records and environmental parameters of the ventilator, and calculating and determining the gas and dust fault thresholds; S2: obtaining the average fault time by counting the equipment operation time, and determining the detection period in combination with the production shift; S3: collecting the operation parameters at three points in the detection period, and taking the average value as the core detection data; S4: standardizing the scores of the air volume, negative pressure and cumulative operation time to obtain three sub-item scores; S5: weighting the health degree and correcting it in combination with the environmental parameters to form a comprehensive health evaluation result; and S6: scheduling production according to the health degree classification and updating the data quarterly. The method constructs an intelligent scheduling mechanism, changes the traditional experience-driven mode, improves the continuity and safety guarantee level of mine production, and provides strong support for the construction of smart mines.

Description

Technical Field

[0001] This invention relates to the field of fault prediction and health management technology, specifically to a mine production scheduling and management method based on equipment fault prediction and health management. Background Technology

[0002] Mine ventilation fans are core equipment for ensuring safe underground production, diluting toxic and harmful gases, regulating the working environment, and supplying oxygen. The mine ventilation system is essentially the "breathing system" for underground operations; its continuous and stable operation directly determines mine ventilation safety, the quality of the working environment, and production continuity. With the continuous advancement of smart mine construction, mine production is developing towards informatization, centralization, and intelligence. Production scheduling is gradually transforming from traditional experience-driven to data-driven and collaborative decision-making. The operating status of ventilation fans has become one of the core bases for production scheduling decisions, placing higher demands on equipment reliability, status perception accuracy, and scheduling coordination capabilities.

[0003] Current mine ventilation fan monitoring and production scheduling largely adopt a decentralized, independent monitoring and reactive maintenance model. This model only collects single parameters and triggers alarms for exceeding thresholds, lacking multi-source data fusion and in-depth analysis. It also fails to establish a health assessment, fault early warning, and trend prediction mechanism covering the entire equipment lifecycle. The scheduling end cannot predict potential faults and deterioration trends in advance, and can only react passively after abnormal equipment shutdowns. This easily leads to ventilation interruptions and the accumulation of toxic and harmful gases, seriously threatening underground operational safety and hindering intelligent, safe, and efficient mine production.

[0004] In response to the aforementioned shortcomings and safety hazards of existing technologies, there is an urgent need for a mine ventilation fan production scheduling and management method based on fault prediction and health management. This method would achieve ventilation fan health status assessment and early fault warning through multi-source data fusion and intelligent algorithms, and integrate the real-time health status of equipment into production scheduling decisions. This would build a proactive, collaborative, and intelligent mine production scheduling mechanism, fundamentally improving the continuity of mine production and the level of safety assurance. Summary of the Invention

[0005] The purpose of this invention is to provide a mine production scheduling and management method based on equipment failure prediction and health management, thereby solving the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions: A mine production scheduling management method based on equipment failure prediction and health management includes the following steps: Obtain fault records of the main ventilation fan and standby ventilation fan in the mine, and obtain environmental parameters of the mine operation area a hour before the fault occurs and a hour after the fault occurs. The environmental parameters include gas concentration C and dust concentration F, where a represents a preset time period. For each set of fault records, calculate the average value of the environmental parameters before and after the fault, which is taken as the environmental correlation average value of the fault in that set. The environmental correlation average of all fault records is taken as the overall arithmetic average and recorded as the fault threshold, which is defined as the gas concentration threshold C0 and the dust threshold F0, respectively.

[0007] As a further aspect of the present invention: obtain the running time of each ventilator from the completion of the last maintenance to the occurrence of the current failure, calculate the arithmetic average of the running time, and denot it as the average failure time t_avg; Obtain the mine production shift N, and calculate the detection cycle T = t_avg / λ, where λ represents the preset gradient value, λ = 1, 2, 3, ...; The detection stops when the gradient value λ first satisfies the constraint 24 / N≥t_avg / λ, and the corresponding detection period T is determined.

[0008] As a further aspect of the present invention: detection nodes are set at the start, middle, and end times of each detection period T, and the following operations are performed when the device is at a detection node: Obtain the operating parameters of the ventilator during this inspection cycle, including air volume Q, negative pressure P, and cumulative running time L. The cumulative running time L refers to the time accumulated since the last maintenance was completed. The average value of the data collected from the above three detection nodes is taken as the core detection data, and is defined as the average air volume Q. avg Average negative pressure P avg .

[0009] As a further aspect of the present invention: performing parameter standardization scoring includes the following steps: Preset reference coefficient G; Obtain the rated air volume Qs of the fan, if the average air volume Q avg If the airflow is within the interval [0.9Qs, 1.1Qs], let the airflow score S1 = G; if the average airflow Q avg Since the interval [0.9Qs, 1.1Qs] is outside the interval, let S1 = G × (Qs - |Qs - Q) avg |) / Qs; To obtain the rated negative pressure Ps of the ventilator, if the average air volume P avg ≤Ps, let the negative pressure score S2=G; if the average negative pressure P avg >Ps, for every 10% exceeding the limit, the negative pressure score S2 decreases by 10%, and the decrease stops for the portion exceeding the limit by less than 10%; Calculate the cumulative runtime score S3 = (1 - L / t_avg) × G.

[0010] As a further aspect of the present invention: calculate the health score as Score = γ1×S1 + γ2×S2 + γ3×S3, where γ1, γ2, and γ3 represent the preset first, second, and third weight coefficients, respectively, and γ1 + γ2 + γ3 = 1.

[0011] As a further aspect of the present invention: the average values ​​of environmental parameters of the mine operation area where the ventilation fan is located during the current detection period are obtained and denoted as c and f respectively; The health score has been revised as follows: When c < C0 and f < F0, let the health score remain unchanged; When either c or f is less than the corresponding threshold, the health score is reduced to 60% of its original value. When c≥C0 and f≥F0, the health score is reduced to 10% of its original value.

[0012] As a further aspect of the present invention: the analysis of the degree of degradation based on the modified health score includes the following steps: Preset the first health score (Score1) and the second health score (Score2) of the ventilator. When Score≥Score1, the ventilation fan is in good condition, allowing all mining faces and transportation systems to operate at full load, and the production plan will be executed according to the original plan. When Score2≤Score<Score1, the fan is slightly deteriorated. The inspection cycle will be shortened to 1 / 2 of the original cycle, and the production plan will still be executed according to the original plan. When Score < Score2, the ventilation fan is severely deteriorated. Production should be stopped immediately and all underground workers should be notified to cease operations.

[0013] As a further aspect of the present invention: new ventilator failure records and operating data are collected quarterly, gas concentration thresholds and dust thresholds are recalculated, and mean time between failures is updated.

[0014] The beneficial effects of this invention are as follows: 1. It effectively solves the core pain points of existing technologies, which rely on decentralized monitoring and post-event maintenance, enabling early prediction and proactive prevention of ventilation fan failures, and significantly improving the safety level of mine ventilation. Compared with traditional single-parameter monitoring and over-limit alarm modes, this method integrates ventilation fan failure records with environmental parameters such as underground gas and dust to establish scientific failure thresholds. By combining the average failure time to determine the adaptive detection cycle, it can accurately capture equipment deterioration trends, avoid ventilation interruptions caused by sudden failures, reduce the accumulation of toxic and harmful gases at the source, ensure the safety of underground personnel, and overcome the constraints of traditional passive handling on mine safety.

[0015] 2. A multi-parameter integrated health assessment system and hierarchical scheduling strategy are constructed to achieve deep collaboration between equipment health status and production scheduling, balancing production continuity and safety reliability. By standardizing the scoring of air volume, negative pressure, and cumulative operating time, and combining environmental parameters to correct the health status, the operating status of ventilation fans is accurately quantified. Differentiated scheduling plans are formulated based on the health level, ensuring full-load and efficient production when equipment is in good condition, shortening the detection cycle and avoiding risks when equipment is slightly deteriorated, and forcing production shutdown when equipment is severely deteriorated. This achieves a dual improvement in safety and efficiency, meeting the data-driven and collaborative decision-making needs of smart mines.

[0016] 3. This approach can be implemented using existing mine monitoring systems, reducing the cost of intelligent transformation. By updating fault data and threshold parameters quarterly, the health assessment model adapts to real-world scenarios such as equipment aging and changes in operating conditions, maintaining long-term accuracy and applicability. Overall, this method constructs a proactive, collaborative, and intelligent scheduling mechanism, completely changing the traditional experience-driven model, effectively improving mine production continuity and safety assurance levels, and providing strong support for the construction of smart mines. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the structure of the mine production scheduling and management method based on equipment failure prediction and health management of the present invention; Figure 2 This is a flowchart illustrating the mine production scheduling and management method based on equipment failure prediction and health management according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 As shown, this invention is a mine production scheduling and management method based on equipment failure prediction and health management, comprising the following steps: Obtain fault records of the main ventilation fan and standby ventilation fan in the mine, and obtain environmental parameters of the mine operation area a hour before the fault occurs and a hour after the fault occurs. The environmental parameters include gas concentration C and dust concentration F, where a represents a preset time period. For each set of fault records, calculate the average value of the environmental parameters before and after the fault, which is taken as the environmental correlation average value of the fault in that set. The environmental correlation average of all fault records is taken as the overall arithmetic average and recorded as the fault threshold, which is defined as the gas concentration threshold C0 and the dust threshold F0, respectively.

[0021] It should be noted that, firstly, a correlation analysis mechanism between mine ventilation fan failures and underground environmental parameters should be established to provide a basic threshold basis for subsequent health assessments and scheduling decisions. Historical failure records of the main and backup ventilation fans in the mine should be collected and organized. For each failure event, environmental parameters monitored in real-time within the corresponding mine operating area should be extracted within a preset time period *a* before and after the failure.

[0022] For each set of independent fault records, the time-series data of gas and dust concentrations are calculated, with the arithmetic mean of the values ​​a hours before and a hours after the fault. This average is used as the environmental correlation average for this fault, characterizing the typical changes in the underground environment before and after the fault. After calculating the environmental correlation average for all historical fault records, the overall arithmetic mean of the gas concentration environmental correlation average for all faults is calculated, and the result is defined as the gas concentration fault threshold C0. Similarly, the overall arithmetic mean of the dust concentration environmental correlation average for all faults is calculated, and the result is defined as the dust concentration fault threshold F0. The fault thresholds determined in this way reflect the quantitative relationship between ventilation fan failure and the accumulation of toxic and harmful gases and dust underground, providing a standard basis for subsequent ventilation fan health correction and production scheduling judgment, and avoiding deviations caused by setting thresholds solely based on experience.

[0023] In another preferred embodiment of the present invention, the running time of each ventilator from the completion of the last maintenance to the occurrence of the current failure is obtained, and the arithmetic average of the running time is calculated and denoted as the average failure time t_avg; Obtain the mine production shift N, and calculate the detection cycle T = t_avg / λ, where λ represents the preset gradient value, λ = 1, 2, 3, ...; The detection stops when the gradient value λ first satisfies the constraint 24 / N≥t_avg / λ, and the corresponding detection period T is determined.

[0024] It is worth noting that an adaptive detection cycle is determined based on historical operation and failure data of the ventilation fans, avoiding monitoring redundancy or missed detections caused by fixed detection cycles. First, historical operation data for each main and standby ventilation fan are collected and statistically analyzed. The continuous operating time of each fan from the last maintenance to the current failure is obtained. The arithmetic mean of these multiple operating time data is calculated, and the result is recorded as the average failure time t_avg of the ventilation fan, reflecting the average reliable operating level of the ventilation fan under the current operating conditions. Simultaneously, the actual number of production shifts N in the mine is obtained. The detection cycle T is set based on the average failure time t_avg and the number of production shifts, let T = t_avg / λ. During the gradient value selection process, the constraint 24 / N ≥ t_avg / λ is judged in real time. When the gradient value λ first meets this constraint, the value selection stops, and the T calculated at this time is determined as the formal detection cycle of the ventilation fan. The detection cycle determined in this way matches both the average failure pattern of the ventilation fan itself and adapts to the mine's production shift arrangement, ensuring the effectiveness of monitoring while taking into account the continuity of on-site production.

[0025] In a preferred embodiment, detection nodes are set at the start, middle, and end times of each detection period T. When a detection node is in operation, the following operations are performed: Obtain the operating parameters of the ventilator during this inspection cycle, including air volume Q, negative pressure P, and cumulative running time L. The cumulative running time L refers to the time accumulated since the last maintenance was completed. The average value of the data collected from the above three detection nodes is taken as the core detection data, and is defined as the average air volume Q. avg Average negative pressure P avg .

[0026] It is understandable that multi-point sampling improves the comprehensiveness of operational parameter collection and avoids the impact of single-point data fluctuations on status assessment.

[0027] When the system reaches any of the aforementioned detection nodes, it collects and records key operating parameters of the current ventilation fan in real time, including the actual operating air volume, operating negative pressure, and cumulative operating time. The cumulative operating time refers to the total continuous, uninterrupted operating time of the ventilation fan since its last overhaul and return to normal operation. After collecting parameters from three detection nodes within a complete detection cycle, the arithmetic mean of the same type of parameters is calculated. The average air volume data collected from the three nodes is taken as the average air volume of the ventilation fan within this detection cycle; the average negative pressure data collected from the three nodes is taken as the average negative pressure of the ventilation fan within this detection cycle. By using multi-point sampling and averaging, interference caused by instantaneous data anomalies can be effectively reduced.

[0028] In a preferred embodiment, parameter standardization scoring includes the following steps: Preset reference coefficient G; Obtain the rated air volume Qs of the fan, if the average air volume Q avg If the airflow is within the interval [0.9Qs, 1.1Qs], let the airflow score S1 = G; if the average airflow Q avg Since the interval [0.9Qs, 1.1Qs] is outside the interval, let S1 = G × (Qs - |Qs - Q) avg |) / Qs; To obtain the rated negative pressure Ps of the ventilator, if the average air volume P avg ≤Ps, let the negative pressure score S2=G; if the average negative pressure P avg >Ps, for every 10% exceeding the limit, the negative pressure score S2 decreases by 10%, and the decrease stops for the portion exceeding the limit by less than 10%; Calculate the cumulative runtime score S3 = (1 - L / t_avg) × G.

[0029] It is important to note that, firstly, a fixed reference coefficient should be pre-set, and reasonably set according to the model and specifications of the mine ventilation fan, operating conditions, and safety requirements to ensure the rationality and applicability of the scoring results.

[0030] Obtain the rated air volume set at the factory for the ventilator and use it as the reference standard for air volume scoring. Score the average air volume obtained during testing. If the average air volume is within the reasonable range of 90% to 110% of the rated air volume, it indicates that the ventilator is operating normally, and the air volume score is equal to the pre-set reference coefficient. If the average air volume exceeds this reasonable range, it indicates a deviation in air volume operation. In this case, the air volume score needs to be recalculated based on the degree of deviation. The recalculation method is to multiply the reference coefficient by a factor and then divide by the rated air volume, thereby achieving standardized scoring of the air volume parameters.

[0031] Simultaneously, the rated negative pressure of the ventilation fan is obtained as a reference standard for negative pressure scoring, and the average negative pressure obtained is scored. If the average negative pressure is less than or equal to the rated negative pressure, it indicates that the negative pressure operation of the ventilation fan meets the requirements, and the negative pressure score is equal to the reference coefficient. If the average negative pressure exceeds the rated negative pressure, the score is reduced according to the extent of the excess. For every 10% exceeding the rated negative pressure, the negative pressure score decreases by 10%. If the excess is less than 10%, no further reduction is made, thus balancing the rigor and rationality of the scoring.

[0032] Finally, the cumulative runtime of the ventilation fans is scored, and combined with the previously calculated mean time between failures, a cumulative runtime score is obtained. This standardized scoring method transforms operating parameters from different types and units into a unified benchmark score, providing comparable basic data for subsequent comprehensive health assessments.

[0033] In a preferred embodiment, the health score is calculated as Score = γ1×S1 + γ2×S2 + γ3×S3, where γ1, γ2, and γ3 represent the preset first, second, and third weighting coefficients, respectively, and γ1 + γ2 + γ3 = 1.

[0034] It should be noted that, in order to comprehensively characterize the overall operational health status of mine ventilation fans, after standardizing the scoring of three parameters—air volume, negative pressure, and cumulative operating time—a weighted summation method is used to calculate the fan's health score. The air volume score, negative pressure score, and cumulative operating time score are each multiplied by their corresponding preset weighting coefficients, and then the three products are summed to obtain the final total, which represents the fan's health score.

[0035] The three preset weighting coefficients correspond to the airflow score, negative pressure score, and cumulative runtime score, respectively, and are designated as the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient. To ensure the rationality and standardization of the health score calculation, the three weighting coefficients must satisfy the constraint that their sum is 1, meaning that the sum of the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient equals 1.

[0036] The weighting coefficients can be preset according to the actual production conditions of the mine, the model and specifications of the ventilation fan, and the safety priority, to ensure that the health status can reflect the impact of key operating parameters.

[0037] In a preferred embodiment, the average values ​​of environmental parameters of the mine operation area where the ventilation fan is located during the current detection period are denoted as c and f, respectively. The health score has been revised as follows: When c < C0 and f < F0, let the health score remain unchanged; When either c or f is less than the corresponding threshold, the health score is reduced to 60% of its original value. When c≥C0 and f≥F0, the health score is reduced to 10% of its original value.

[0038] Understandably, after completing the initial calculation of the ventilation fan's health status, in order to make the assessment results more consistent with the actual safety environment underground, it is necessary to correct the health status based on the environmental parameters of the ventilation fan's service area during this testing cycle, so that the equipment status assessment matches the underground safety conditions.

[0039] First, environmental parameters of the mine operating area served by the ventilation fan during this testing period are collected, mainly including methane and dust concentrations, and these are used as the key basis for health level correction. Based on the comparison between environmental parameters and corresponding fault thresholds, health level corrections are made in three scenarios. When both methane and dust concentrations are below their respective fault thresholds, the underground environment is considered safe, and the ventilation fan operation has not caused significant adverse effects on the environment; in this case, the health level remains unchanged. When only one parameter (methane or dust concentration) is below its corresponding threshold, while the other exceeds it, it indicates a certain degree of abnormality in the underground environment, and there is a mismatch between the ventilation fan's operating status and environmental safety; in this case, the health level is corrected to 60% of its original value. When both methane and dust concentrations are above their respective fault thresholds, it indicates a significant safety hazard in the underground environment, and the ventilation fan's operating status has seriously affected operational safety; in this case, the health level is significantly corrected to 10% of its original value.

[0040] By using the aforementioned environmental linkage correction method, the health status of the ventilation fan can more accurately reflect the overall condition of the equipment operation and the underground environment, providing a more reliable and realistic assessment basis for subsequent graded scheduling decisions.

[0041] In a preferred embodiment, the analysis of the degree of degradation based on the corrected health score includes the following steps: Preset the first health score (Score1) and the second health score (Score2) of the ventilator. When Score≥Score1, the ventilation fan is in good condition, allowing all mining faces and transportation systems to operate at full load, and the production plan will be executed according to the original plan. When Score2≤Score<Score1, the fan is slightly deteriorated. The inspection cycle will be shortened to 1 / 2 of the original cycle, and the production plan will still be executed according to the original plan. When Score < Score2, the ventilation fan is severely deteriorated. Production should be stopped immediately and all underground workers should be notified to cease operations.

[0042] It is worth noting that after completing the environmental correction for the health of the ventilation fans, the degree of deterioration of the ventilation fan operation is graded based on the corrected health status, and a corresponding production scheduling strategy is formulated.

[0043] Before conducting the degradation analysis, two health thresholds are pre-set: a first health threshold and a second health threshold. These two thresholds are divided sequentially according to their numerical values ​​to distinguish different operating status levels of the ventilation fans. Based on the comparison between the corrected health status and the two thresholds, the operating status of the ventilation fans is divided into three levels, and corresponding scheduling measures are implemented. When the corrected health status is greater than or equal to the first health threshold, the ventilation fan is determined to be in good condition, the equipment is operating stably and reliably, and the underground ventilation capacity is sufficient. At this time, all mining faces and transportation systems in the mine are allowed to operate normally at full load, and the overall production plan is executed according to the original plan.

[0044] When the corrected health status is greater than or equal to the second health status threshold and less than the first health status threshold, the ventilator is determined to be in a slightly deteriorated state. Although the equipment has not shown obvious faults, it has shown a certain trend of performance degradation. At this time, the original detection cycle is shortened to half of the original cycle to increase the monitoring frequency, keep abreast of changes in equipment status, and the production plan can still be executed normally according to the original plan.

[0045] When the corrected health level is less than the second health level threshold, the ventilation fan is determined to be in a severely deteriorated state, and there are significant safety hazards in the operation of the equipment. It cannot guarantee the normal ventilation needs underground. At this time, mine production operations should be stopped immediately, and all underground workers should be notified in a timely manner to stop working and evacuate to a safe area to effectively ensure the safety of underground operations.

[0046] In another preferred embodiment of the invention, new ventilator failure records and operating data are collected quarterly, gas concentration thresholds and dust thresholds are recalculated, and mean time between failures is updated.

[0047] It is worth noting that this method collects the latest ventilation fan failure records and operating data every quarter, and recalculates the gas concentration threshold, dust threshold and mean time between failures. This allows the assessment thresholds and detection cycles to be dynamically adapted to the equipment operating conditions, avoiding deviations from fixed parameters and improving the long-term accuracy of health assessment and fault early warning.

[0048] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A mine production scheduling and management method based on equipment failure prediction and health management, characterized in that, Includes the following steps: Obtain fault records of the main ventilation fan and standby ventilation fan in the mine, and obtain environmental parameters of the mine operation area a hour before the fault occurs and a hour after the fault occurs. The environmental parameters include gas concentration C and dust concentration F, where a represents a preset time period. For each set of fault records, calculate the average value of the environmental parameters before and after the fault, which is taken as the environmental correlation average value of the fault in that set. The environmental correlation average of all fault records is taken as the overall arithmetic average and recorded as the fault threshold, which is defined as the gas concentration threshold C0 and the dust threshold F0, respectively.

2. The mine production scheduling and management method based on equipment failure prediction and health management according to claim 1, characterized in that, Obtain the running time of each fan from the completion of the last maintenance to the occurrence of the current failure, calculate the arithmetic mean of the running time, and denot it as the mean time between failures t_avg; Obtain the mine production shift N, and calculate the detection cycle T = t_avg / λ, where λ represents the preset gradient value, λ = 1, 2, 3, ...; The detection stops when the gradient value λ first satisfies the constraint 24 / N≥t_avg / λ, and the corresponding detection period T is determined.

3. The mine production scheduling and management method based on equipment failure prediction and health management according to claim 2, characterized in that, Detection nodes are set at the start, middle, and end times of each detection period T. When a detection node is in operation, the following operations are performed: Obtain the operating parameters of the ventilator during this inspection cycle, including air volume Q, negative pressure P, and cumulative running time L. The cumulative running time L refers to the time accumulated since the last maintenance was completed. The average value of the data collected from the above three detection nodes is taken as the core detection data, and is defined as the average air volume Q. avg Average negative pressure P avg .

4. The mine production scheduling and management method based on equipment fault prediction and health management according to claim 3, characterized in that, Performing parameter standardization scoring includes the following steps: Preset reference coefficient G; Obtain the rated air volume Qs of the fan, if the average air volume Q avg If the airflow is within the interval [0.9Qs, 1.1Qs], let the airflow score S1 = G; if the average airflow Q avg Since the interval [0.9Qs, 1.1Qs] is outside the interval, let S1 = G × (Qs - |Qs - Q) avg |) / Qs; To obtain the rated negative pressure Ps of the ventilator, if the average air volume P avg ≤Ps, let the negative pressure score S2=G; if the average negative pressure P avg >Ps, for every 10% exceeding the limit, the negative pressure score S2 decreases by 10%, and the decrease stops for the portion exceeding the limit by less than 10%; Calculate the cumulative runtime score S3 = (1 - L / t_avg) × G.

5. The mine production scheduling and management method based on equipment fault prediction and health management according to claim 4, characterized in that, The health score is calculated as Score = γ1×S1 + γ2×S2 + γ3×S3, where γ1, γ2, and γ3 represent the preset first, second, and third weight coefficients, respectively, and γ1 + γ2 + γ3 = 1.

6. The mine production scheduling and management method based on equipment failure prediction and health management according to claim 5, characterized in that, The mean values ​​of environmental parameters of the mine operation area where the ventilation fan is located during this testing period are denoted as c and f, respectively. The health score has been revised as follows: When c < C0 and f < F0, let the health score remain unchanged; When either c or f is less than the corresponding threshold, the health score is reduced to 60% of its original value. When c≥C0 and f≥F0, the health score is reduced to 10% of its original value.

7. The mine production scheduling and management method based on equipment failure prediction and health management according to claim 6, characterized in that, The analysis of the degree of degradation is based on the revised health score, including the following steps: Preset the first health score (Score1) and the second health score (Score2) of the ventilator. When Score≥Score1, the ventilation fan is in good condition, allowing all mining faces and transportation systems to operate at full load, and the production plan will be executed according to the original plan. When Score2≤Score<Score1, the fan is slightly deteriorated. The inspection cycle will be shortened to 1 / 2 of the original cycle, and the production plan will still be executed according to the original plan. When Score < Score2, the ventilation fan is severely deteriorated. Production should be stopped immediately and all underground workers should be notified to cease operations.

8. The mine production scheduling and management method based on equipment failure prediction and health management according to claim 1, characterized in that, New ventilator failure records and operating data are collected quarterly, gas concentration thresholds and dust thresholds are recalculated, and mean time between failures is updated.

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