Real-time monitoring method and system for mine truck engine data

Abstract

The invention relates to the technical field of data processing, in particular to a real-time monitoring method and system for mine car engine data. The method comprises the following steps: acquiring rotating speed data and torque data of a to-be-detected mine car engine, and acquiring a comprehensive pressure bearing level of the to-be-detected mine car engine at each moment according to the rotating speed data and the torque data; based on the comprehensive pressure bearing level, obtaining the accumulated health degradation degree of the mine car engine to be detected at each moment; clustering the accumulated health degeneration degree to obtain a cluster, and obtaining a healthy transportation time period and an unhealthy transportation time period according to the cluster; obtaining a healthy period loss factor and an unhealthy period loss factor of the to-be-detected mine car engine, obtaining a dynamic abnormal threshold value of the to-be-detected mine car engine according to the healthy period loss factor and the unhealthy period loss factor, and judging the fault damage of the to-be-detected mine car engine. According to the invention, the accuracy of engine fault damage judgment is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for real-time monitoring of mining truck engine data. Background Technology

[0002] Mining trucks are core transportation equipment in mining production. Their engines operate under harsh conditions of heavy load, high vibration, and large temperature differences for extended periods, making them prone to failure. To avoid the huge economic losses caused by sudden shutdowns, predictive maintenance technology for real-time monitoring and fault warning of engine operating data has become an important development direction in the industry.

[0003] Traditional monitoring methods identify early signs of failure by analyzing multi-dimensional sensor data generated by the engine during operation. In practice, a large amount of historical data of the engine in a healthy state is usually collected to train a benchmark model. The newly collected data is input into the model. If the output deviation or anomaly score exceeds a preset fixed threshold, it is determined that there may be an early failure and an alarm is issued. However, the engine's operating condition is not static. As operating time accumulates, various components will naturally wear out, age, and degrade in performance. This will cause the threshold of its health status to slowly and continuously decrease. At the same time, changes in the mining truck's operating environment, such as rugged or complex mining road surfaces, will increase the burden on the engine, leading to an increase in normal operating parameters. The fixed thresholds used in existing technologies cannot adapt to the natural degradation of engine performance and changes in mining road conditions. They may misjudge normal performance degradation as early failure or identify normal data in complex road conditions as abnormal data. Summary of the Invention

[0004] To address the technical problem that fixed threshold-based anomaly detection cannot adapt to the natural decline in engine performance and changes in road conditions in mining areas, and may misjudge normal performance degradation as early failures or identify normal data in complex road conditions as abnormal data, this invention provides a method and system for real-time monitoring of mining truck engine data.

[0005] In a first aspect, the present invention provides a method for real-time monitoring of mining truck engine data, employing the following technical solution: A method for real-time monitoring of mining truck engine data, including the following steps: Collect speed and torque data of the engine of the mining truck under test and the normal engine of the mining truck on the same transportation route at each moment; Based on speed and torque data, the comprehensive pressure level of the engine under test at each moment is obtained; according to the time difference between the torque data of the engine under test and the normal engine when they reach their peak values, and the torque data difference between the engine under test and the normal engine at the same speed, the health degradation factor of the mining car engine under test at each moment is obtained; based on the comprehensive pressure level and health degradation factor, the cumulative health degradation degree of the mining car engine under test at each moment is obtained; cluster analysis of the cumulative health degradation degree is performed to obtain healthy transportation periods and unhealthy transportation periods; Based on the number of healthy transport periods and the difference in cumulative health degradation between the engine under test and the normal engine within the healthy transport period, a healthy period loss factor is obtained; based on the number of consecutive unhealthy periods and the difference in cumulative health degradation between the engine under test and the normal engine within the consecutive unhealthy periods, an unhealthy period loss factor is obtained. Based on the two loss factors, the dynamic anomaly threshold of the engine of the mining truck to be tested is obtained, and the fault damage of the engine to be tested is judged.

[0006] The innovation of this invention lies in the following: First, by constructing a comprehensive pressure-bearing level and health degradation factor, the cumulative health degradation degree of the mine car under test at each moment is obtained, enabling a comprehensive health assessment of the engine from two dimensions: instantaneous load and long-term performance degradation. Next, by clustering the cumulative health degradation degree sequences at all moments, the transportation process is divided into healthy and unhealthy periods, distinguishing the changes in operating conditions of the current transportation segment, which facilitates subsequent analysis of the engine's loss factors under different road conditions. Finally, based on the sum of the engine's loss factors under different road conditions, a dynamic anomaly threshold for the engine of the mine car under test is obtained. This threshold can adapt to the engine's own long-term health degradation and changes in operating conditions of the transportation segment, thereby accurately distinguishing normal performance degradation from actual engine failure, fundamentally solving the problem that traditional fixed threshold methods may misjudge normal performance degradation as early failure. Preferably, obtaining the comprehensive pressure level of the engine under test at each moment includes: The absolute value of the difference between the speed data of the engine of the mine car under test at the i-th time point and the speed data of the engine of the mine car under test at the (i-1)-th time point is denoted as the speed change rate of the engine of the mine car under test at the i-th time point. , This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the torque data of the engine of the mining truck to be tested at the i-th time point; This represents the rated torque data of the mine car engine under test; This represents the rotational speed data of the engine of the mine car to be tested at the i-th time point; This represents the rotational speed data of the engine of the mine car to be tested at the (i-1)th time. This represents the maximum value among all time points of the rate of change of the engine speed of the mine car being tested.

[0007] Taking into account the effects of torque and speed on engine load makes the assessment of the current engine load more accurate.

[0008] Preferably, obtaining the health degradation factor of the mining truck engine to be detected at each time point includes: The time range consisting of all moments before the i-th moment is denoted as the historical time range of the i-th moment; the target moment, the comparison time, and the moments to be compared within the historical time range of each moment are obtained; , This represents the health degradation factor of the mining truck engine to be tested at time i. Represents the comparison time at the i-th time. Represents the target time at the i-th time; This represents the number of times to be compared within the historical time range of the i-th time point; as well as These represent the torque data of the normal mining truck engine and the torque data of the mining truck engine to be tested at the j-th comparison time within the historical time range of the i-th time, respectively; || represents the absolute value symbol; norm() represents the normalization function.

[0009] Preferably, obtaining the target time, comparison time, and comparison time within the historical time range of each time step includes: The moment corresponding to the maximum torque data in the historical time range of the normal mining truck engine at moment i is recorded as the comparison moment of moment i; the moment corresponding to the maximum torque data in the historical time range of the mining truck engine to be tested at moment i is recorded as the target moment of moment i; within the historical time range of moment i, the time range consisting of all moments after the target moment of moment i is used as the comparison time range of moment i; within the comparison time range of moment i, the moment corresponding to the moment when the speed data of the normal mining truck engine and the mining truck engine to be tested are equal is used as the comparison moment within the historical time range of moment i.

[0010] Preferably, obtaining the cumulative health degradation degree of the engine of the mining truck under test at each time moment includes: A neighborhood parameter L is preset, and the time range consisting of the L nearest moments before the i-th moment is denoted as the historical neighborhood time range of the i-th moment; , This represents the cumulative health degradation degree of the mining truck engine under test at time i. This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the number of all times within the historical neighborhood of time i. as well as Representing the historical neighborhood time range of the i-th time respectively The health degradation factors of the mine car engine to be tested at a given time and the first The health degradation factor of the mine car engine to be tested at a given time; norm() represents the normalization function; exp() is the exponential function with the natural constant as the base.

[0011] By constructing a comprehensive pressure level and health degradation factor, the cumulative health degradation degree of the mining truck under test at each moment can be obtained, enabling a comprehensive health assessment of the engine from two dimensions: instantaneous load and long-term performance degradation.

[0012] Preferably, the cluster analysis of the cumulative health degradation to obtain healthy transport periods and unhealthy transport periods includes: The cumulative health degradation of the mining truck engines under test at all times is clustered to obtain several clusters. Two preset health threshold parameters K1 and K2 are used. If the mean of all cumulative health degradation data in any cluster is greater than or equal to K1, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as an unhealthy transportation period. If the mean of all cumulative health degradation data in any cluster is less than K2, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as a healthy transportation period.

[0013] Cluster analysis based on cumulative health degradation identifies different road conditions, facilitating subsequent analysis of engine damage under each road condition.

[0014] Preferably, obtaining the health period loss factor includes: ; In the formula, The health period loss factor represents the engine of the mining truck to be tested. This represents the number of healthy transport periods for the engine of the mining truck to be tested. This represents the average cumulative degradation of the engine of the mining truck under test at all times within the nth healthy transportation period; represents the mean cumulative degradation of the engine of a normal mining car at all times during the nth healthy transportation period; || represents the absolute value symbol; exp() represents an exponential function with the natural constant as the base.

[0015] Preferably, obtaining the loss factor for unhealthy periods includes: Consecutive unhealthy transport periods are merged and recorded as consecutive unhealthy periods; , The loss factor represents the unhealthy period of the engine of the mining truck to be tested; This represents the number of consecutive unhealthy periods; This represents the number of unhealthy transport periods in the m-th consecutive unhealthy period; This represents the average cumulative health degradation of the mining car engine under test at all times during the m-th consecutive unhealthy period. represents the average cumulative health degradation of normal mine car engines at all times during the m-th consecutive unhealthy period; || represents the absolute value symbol.

[0016] Preferably, the step of obtaining the dynamic anomaly threshold of the engine of the mining car to be tested and judging the fault damage of the engine to be tested includes: , This represents the dynamic anomaly threshold of the engine of the mining truck to be tested. The loss factor represents the unhealthy period of the engine of the mining truck to be tested; represents the health period loss factor of the engine of the mining truck to be tested; norm() represents the normalization function. A preset abnormal threshold T3 is set. If the dynamic abnormal threshold of the engine of the mine car under test is greater than or equal to the abnormal threshold, the engine of the mine car under test is seriously damaged, and relevant personnel are notified to carry out repairs.

[0017] The cumulative damage to the engine caused by different road conditions was quantified, and the dynamic abnormal threshold of the engine of the mining truck under test was obtained, making the judgment of fault damage more accurate.

[0018] Secondly, this invention provides a real-time monitoring system for mining truck engine data, employing the following technical solution: A real-time monitoring system for mining truck engine data includes a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned real-time monitoring method for mining truck engine data is implemented.

[0019] By adopting the above technical solution, the real-time monitoring method for mining truck engine data is generated into a computer program and stored in a memory for loading and execution by a processor. This allows for the creation of a terminal device based on the memory and processor, facilitating its use.

[0020] This invention has the following technical advantages: First, it obtains the dynamic anomaly threshold of the engine of the mining truck under test based on the speed and torque data collected during transportation. This threshold can adapt to the long-term health degradation of the engine itself and the changes in operating conditions of the transportation route, thereby accurately distinguishing normal performance degradation from actual engine failure. This fundamentally solves the problem that traditional fixed threshold methods may misjudge normal performance degradation as early failure. Second, by constructing a comprehensive pressure level and health degradation factor, this invention obtains the cumulative health degradation degree of the mining truck under test at each moment, enabling a comprehensive health assessment of the engine from both instantaneous load and long-term performance degradation dimensions. Third, by clustering the cumulative health degradation degree sequences at all moments, this invention divides the transportation process into healthy and unhealthy periods, distinguishing the changes in operating conditions of the current transportation route, and enabling analysis of engine loss factors under different road conditions. Attached Figure Description

[0021] Figure 1 This is a flowchart of the real-time monitoring method for mining truck engine data in an embodiment of the present invention; Figure 2 A schematic diagram showing the relationship between the speed and torque data of a mining truck engine. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0023] This invention discloses a method for real-time monitoring of mining truck engine data, referring to... Figure 1 This includes steps S1-S4: S1: Collect the engine speed and torque data of the mining truck engine to be tested.

[0024] In this embodiment of the invention, the preset sampling time is 1 second / time. The speed sensor is installed on the rotating shaft of the engine of the mine car under test, and the torque sensor is installed on the drive shaft of the engine of the mine car under test. The mine car under test is driven on the transportation section, and the speed data and torque data of the engine of the mine car under test are collected at each moment. A speed sensor is installed on the shaft of a normal mining car engine, and a torque sensor is installed on the drive shaft of the normal mining car engine. The normal mining car is driven on the same transport route, and the speed and torque data of the normal mining car engine are collected at each moment.

[0025] It should be noted that the speed and torque data of the engine of the mine car under test correspond in timing to the speed and torque data of the normal mine car engine.

[0026] S2: Based on the speed and torque data of the engine of the mine car under test at each time point, obtain the comprehensive pressure level of the engine of the mine car under test at each time point; obtain the health degradation factor of the engine of the mine car under test at each time point; based on the comprehensive pressure level and health degradation factor, obtain the cumulative health degradation degree of the engine of the mine car under test at each time point.

[0027] It should be noted that the speed data reflects the engine's operating speed. The higher the speed, the greater the engine load and the faster the operating speed. The torque data reflects the rotational force output by the engine. The greater the torque, the more resistance the engine needs to overcome to drive the vehicle. Therefore, by using the speed and torque data of the engine of the mine car under test, the overall pressure level of the engine of the mine car under test can be obtained.

[0028] In this embodiment of the invention, the absolute value of the difference between the rotational speed data of the mine car engine to be tested at the i-th time point and the rotational speed data of the mine car engine to be tested at the (i-1)-th time point is denoted as the rotational speed change rate of the mine car engine to be tested at the i-th time point. Obtain the overall pressure level of the engine of the mine car under test at each time point: ； In the formula, This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the torque data of the engine of the mining truck to be tested at the i-th time point; This represents the rated torque data of the mine car engine under test; This represents the rotational speed data of the engine of the mine car to be tested at the i-th time point; This represents the rotational speed data of the engine of the mine car to be tested at the (i-1)th time. This represents the maximum value among all time points of the rate of change of the engine speed of the mine car being tested.

[0029] It should be noted that in a mining truck with a normal engine, the engine speed gradually increases over time during transportation. As the engine speed increases, the torque will reach a peak at a certain point and then begin to decline. However, if the engine of the mining truck under test deteriorates, the peak torque may occur at a lower engine speed (the torque will reach its peak earlier). Furthermore, the deterioration of the engine's health may cause a premature decrease in torque due to reduced intake efficiency or mechanical wear. At the same engine speed, its performance will decline, resulting in lower torque. In other words, the greater the difference in torque between the engine under test and a normal mining truck engine at the same engine speed, the more significant the difference will be. Figure 2 As shown.

[0030] In this embodiment of the invention, the time range consisting of all times before the i-th time is denoted as the historical time range of the i-th time. The moment corresponding to the maximum torque data in the historical time range of the i-th moment of a normal mining truck engine is recorded as the comparison moment of the i-th moment. The time corresponding to the maximum torque data in the historical time range of the i-th moment of the mining truck engine to be tested is recorded as the target time of the i-th moment; Within the historical time range of the i-th moment, the time range consisting of all moments after the target moment of the i-th moment is taken as the comparison time range of the i-th moment; within the comparison time range of the i-th moment, the moment corresponding to when the speed data of the normal mining car engine is equal to the speed data of the mining car engine to be tested is taken as the comparison moment within the historical time range of the i-th moment. Obtain the health degradation factor of the engine of the mining truck under test at each time point: ; In the formula, This represents the health degradation factor of the mining truck engine to be tested at time i. Represents the comparison time at the i-th time. Represents the target time at the i-th time; This represents the number of times to be compared within the historical time range of the i-th time point; This represents the torque data of a normal mining truck engine at the j-th time point within the historical time range of the i-th time point; This represents the torque data of the mine car engine to be tested at the j-th comparison time within the historical time range of the i-th time; norm() represents the normalization function; || represents the absolute value sign; The larger the value, the greater the difference between the torque data of the tested mine car engine and the normal mine car engine at the i-th time point in the historical time range when the torque data reaches the maximum value. This indicates that the health condition of the tested mine car engine has deteriorated, causing the torque data to reach the peak value earlier.

[0031] The value represents the difference in torque data between the engine of the mine car under test and the normal mine car engine at the corresponding time when the engine speed data are equal within the historical time range of the i-th time. The larger the value, the more it indicates that the health condition of the engine of the mine car under test has deteriorated, resulting in an earlier decrease in torque data.

[0032] It should be noted that the lower the overall pressure level of the mine car engine under test at the current moment, the lower the load that the mine car engine can withstand at the current moment, which means that the health status of the mine car engine is constantly deteriorating and the cumulative degree of health degradation is greater; when the health degradation factor of the mine car engine is getting bigger and bigger, it means that the health degradation degree of the mine car engine is also getting bigger and bigger.

[0033] With a preset neighborhood parameter L=10, the time range consisting of the L nearest moments before moment i is denoted as the historical neighborhood time range of moment i. Then, the cumulative health degradation degree of the mining truck engine to be detected at each moment is obtained: ; In the formula, This represents the cumulative health degradation degree of the mining truck engine under test at time i. This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the number of all times within the historical neighborhood of time i. Represents the historical neighborhood time range of the i-th moment. Health degradation factors of the mining truck engine to be tested at any given moment; Represents the historical neighborhood time range of the i-th moment. The health degradation factor of the mining car engine to be tested at a given time; norm() represents the normalization function; exp() represents the exponential function with the natural constant as the base; S3: Cluster the cumulative health degradation of the mining car engine under test at all times to obtain several clusters. Based on the clusters, obtain the healthy transportation period and the unhealthy transportation period; obtain the healthy period loss factor of the mining car engine under test; obtain the unhealthy period loss factor of the mining car engine under test.

[0034] It should be noted that during the transportation of the mine car under test, if the cumulative health degradation of the mine car engine does not change or the change is small within a certain period, it indicates that the transportation road conditions are good during that period. This is because good transportation road conditions cause less damage to the engine. If the cumulative health degradation of the mine car engine is large during that period, it indicates that the transportation road conditions are worse and cause greater wear and tear on the engine. When the cumulative health degradation of the mine car engine is small during that period, it indicates that the transportation road conditions are good and cause less wear and tear on the engine. Therefore, this invention performs cluster analysis on the cumulative health degradation of the mine car engine under test at all times to obtain healthy transportation periods and unhealthy transportation periods. The transportation road conditions are better during the healthy transportation periods and worse during the unhealthy transportation periods.

[0035] In this embodiment of the invention, the cumulative health degradation of the mining truck engine under test at all times is clustered to obtain several clusters; Two preset health threshold parameters K1=0.7 and K2=0.3 are used. If the mean of all cumulative health degradation data in any cluster is greater than or equal to K1, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as the unhealthy transportation period. If the mean of all cumulative health degradation data in any cluster is less than K2, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as the healthy transportation period. It should be noted that during the transportation of mine cars, the more healthy transportation periods there are, the more road sections that do not cause or cause minimal damage to the mine car engine during transportation, and the smaller the healthy road section loss factor. Furthermore, during healthy transportation periods, the smaller the difference in cumulative degradation between normal mine cars and mine cars under test, the lower the damage to the mine car engine during that healthy transportation period.

[0036] Obtain the health period loss factor of the engine of the mining truck to be tested: ; In the formula, The health period loss factor represents the engine of the mining truck to be tested. This represents the number of healthy transport periods for the engine of the mining truck to be tested. This represents the average cumulative degradation of the engine of the mining truck under test at all times within the nth healthy transportation period; This represents the mean cumulative degradation of the normal mining car engine at all times within the nth healthy transportation period; || represents the absolute value symbol; exp() represents an exponential function with the natural constant as the base. It should be noted that during the transportation of mine cars, the more unhealthy transportation periods there are, and the more consecutive they are, the greater and more continuous the damage to the mine car engine will be. Furthermore, during consecutive unhealthy transportation periods, the greater the difference in the cumulative degradation between normal mine cars and mine cars under test, the greater the damage to the mine car engine caused by unhealthy transportation periods.

[0037] Consecutive unhealthy transport periods are merged and recorded as consecutive unhealthy periods; Obtain the loss factor for the unhealthy period of the engine of the mining truck to be tested: ; In the formula, The loss factor represents the unhealthy period of the engine of the mining truck to be tested; This represents the number of consecutive unhealthy periods; This represents the number of unhealthy transport periods in the m-th consecutive unhealthy period; This represents the average cumulative health degradation of the mining car engine under test at all times during the m-th consecutive unhealthy period. represents the average cumulative health degradation of normal mine car engines at all times during the m-th consecutive unhealthy period; || represents the absolute value symbol. S4: Based on the healthy period loss factor and the unhealthy period loss factor of the engine under test, obtain the dynamic abnormality threshold of the engine under test, and judge the fault damage of the engine under test.

[0038] It should be noted that the larger the loss factor during healthy periods and the loss factor during unhealthy periods, the greater the damage to the engine during the transportation of the mine car.

[0039] In this embodiment of the invention, the dynamic anomaly threshold of the engine of the mining truck to be detected is obtained: ; In the formula, This represents the dynamic anomaly threshold of the engine of the mining truck to be tested. The loss factor represents the unhealthy period of the engine of the mining truck to be tested; The health period loss factor represents the engine of the mining truck to be tested; norm() represents the normalization function.

[0040] The preset abnormal threshold T3=0.75. If the dynamic abnormal threshold of the engine of the mine car to be tested is greater than or equal to the abnormal threshold, it indicates that the engine of the mine car to be tested is seriously damaged and relevant personnel need to be notified for repair.

[0041] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of mining truck engine data, characterized in that, include: Collect speed and torque data of the engine of the mining truck under test and the normal engine of the mining truck on the same transportation route at each moment; Based on speed and torque data, the comprehensive pressure level of the engine under test at each moment is obtained; based on the time difference when the torque data of the engine under test and the normal engine reach the peak and the torque data difference between the engine under test and the normal engine at the same speed, the health degradation factor of the mining car engine under test at each moment is obtained. Based on the comprehensive pressure bearing level and health degradation factor, the cumulative health degradation degree of the mine car engine under test at each time point is obtained; Cluster analysis of the cumulative health degradation results in healthy transport periods and unhealthy transport periods; Based on the number of healthy transport periods and the difference in cumulative health degradation between the engine under test and the normal engine within the healthy transport period, the loss factor of the healthy transport period is obtained. Based on the number of consecutive unhealthy periods and the difference in cumulative health degradation between the engine under test and the normal engine within the consecutive unhealthy periods, the loss factor of the unhealthy period is obtained. Based on the two loss factors, the dynamic anomaly threshold of the engine of the mining truck to be tested is obtained, and the fault damage of the engine to be tested is judged.

2. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The process of obtaining the comprehensive pressure level of the engine under test at each moment includes: The absolute value of the difference between the speed data of the engine of the mine car under test at the i-th time point and the speed data of the engine of the mine car under test at the (i-1)-th time point is denoted as the speed change rate of the engine of the mine car under test at the i-th time point. , This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the torque data of the engine of the mining truck to be tested at the i-th time point; This represents the rated torque data of the mine car engine under test; This represents the rotational speed data of the engine of the mine car to be tested at the i-th time point; This represents the rotational speed data of the engine of the mine car to be tested at the (i-1)th time. This represents the maximum value among all time points of the rate of change of the engine speed of the mine car being tested.

3. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The process of obtaining the health degradation factor of the mining truck engine under test at each time point includes: The time range consisting of all moments before the i-th moment is denoted as the historical time range of the i-th moment; the target moment, the comparison time, and the moments to be compared within the historical time range of each moment are obtained; , This represents the health degradation factor of the mining truck engine to be tested at time i. This is the comparison time at the i-th time. Represents the target time at the i-th time; The number of times to be compared within the historical time range of the i-th time point; as well as These are the torque data of the normal mining truck engine and the torque data of the mining truck engine to be tested at the j-th comparison time within the historical time range of the i-th time, respectively; || is the absolute value symbol; norm() represents the normalization function.

4. The method for real-time monitoring of mining truck engine data according to claim 3, characterized in that, The process of obtaining the target time, comparison time, and comparison time within the historical time range of each moment includes: The moment corresponding to the maximum torque data in the historical time range of the normal mining truck engine at moment i is recorded as the comparison moment of moment i; the moment corresponding to the maximum torque data in the historical time range of the mining truck engine to be tested at moment i is recorded as the target moment of moment i; within the historical time range of moment i, the time range consisting of all moments after the target moment of moment i is used as the comparison time range of moment i; within the comparison time range of moment i, the moment corresponding to the moment when the speed data of the normal mining truck engine and the mining truck engine to be tested are equal is used as the comparison moment within the historical time range of moment i.

5. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The process of obtaining the cumulative health degradation degree of the engine of the mining truck under test at each time point includes: A neighborhood parameter L is preset, and the time range consisting of the L nearest moments before the i-th moment is denoted as the historical neighborhood time range of the i-th moment; , This represents the cumulative health degradation degree of the mining truck engine under test at time i. This represents the overall pressure bearing level of the engine of the mine car under test at the i-th time point; This represents the number of all times within the historical neighborhood of time i. as well as Representing the historical neighborhood time range of the i-th time respectively The health degradation factors of the mine car engine to be tested at a given time and the first The health degradation factor of the mine car engine to be tested at time point ; exp() is an exponential function with the natural constant as the base; norm() represents the normalization function.

6. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The cluster analysis of the cumulative health degradation yields healthy transport periods and unhealthy transport periods, including: The cumulative health degradation of the mining truck engines under test at all times is clustered to obtain several clusters. Two preset health threshold parameters K1 and K2 are used. If the mean of all cumulative health degradation data in any cluster is greater than or equal to K1, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as an unhealthy transportation period. If the mean of all cumulative health degradation data in any cluster is less than K2, the time period consisting of the times corresponding to all cumulative health degradation data in the cluster is recorded as a healthy transportation period.

7. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The loss factor during the healthy period is obtained. include: ； In the formula, The health period loss factor represents the engine of the mining truck to be tested. This represents the number of healthy transport periods for the engine of the mining truck to be tested. This represents the average cumulative degradation of the engine of the mining truck under test at all times within the nth healthy transportation period; represents the mean cumulative degradation of the engine of a normal mining car at all times during the nth healthy transportation period; || represents the absolute value symbol; exp() represents an exponential function with the natural constant as the base.

8. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The acquisition of the loss factor for unhealthy periods includes: Consecutive unhealthy transport periods are merged and recorded as consecutive unhealthy periods; , The loss factor represents the unhealthy period of the engine of the mining truck to be tested; This represents the number of consecutive unhealthy periods; This represents the number of unhealthy transport periods in the m-th consecutive unhealthy period; This represents the average cumulative health degradation of the mining car engine under test at all times during the m-th consecutive unhealthy period. represents the average cumulative health degradation of normal mine car engines at all times during the m-th consecutive unhealthy period; || represents the absolute value symbol.

9. The method for real-time monitoring of mining truck engine data according to claim 1, characterized in that, The process of obtaining the dynamic anomaly threshold of the engine of the mining truck to be tested and judging the fault damage of the engine to be tested includes: , This represents the dynamic anomaly threshold of the engine of the mining truck to be tested. The loss factor represents the unhealthy period of the engine of the mining truck to be tested; represents the health period loss factor of the engine of the mining truck to be tested; norm() represents the normalization function. A preset abnormal threshold T3 is set. If the dynamic abnormal threshold of the engine of the mine car under test is greater than or equal to the abnormal threshold, the engine of the mine car under test is seriously damaged, and relevant personnel are notified to carry out repairs.

10. A real-time monitoring system for mining truck engine data, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the real-time monitoring method for mining truck engine data according to any one of claims 1-9.

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