Method for evaluating running state of large road maintenance machine

Abstract

The invention relates to the technical field of large-scale road maintenance machinery state safety, in particular to a large-scale road maintenance machinery operation state evaluation method based on multi-sensor data analysis. Comprising the following steps: step 1, measuring operation parameters of each composition system of the large-scale road maintenance machine through a sensor pre-installed on the large-scale road maintenance machine; step 2, comparing the measured operation parameters of each composition system of the large-scale road maintenance machinery with pre-calculated standard operation parameters, and judging the deterioration degree of the operation state of the large-scale road maintenance machinery so as to determine the evaluation score of the operation state of each composition system; and step 3, multiplying the preset weight of each composition system and the weight of each risk point by the evaluation score of the operation state of each composition system and each risk point so as to calculate the score of the overall operation state of the current large road maintenance machinery. The problems of excessive maintenance and advanced maintenance can be effectively avoided, and waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of the state safety of large track maintenance machinery, and particularly relates to a method for evaluating the operation state of large track maintenance machinery based on multi-sensor data analysis. Background Art

[0002] Large track maintenance machinery is an important self-propelled vehicle for railway laying and maintenance. For the maintenance of large track maintenance machinery, the current method we adopt is periodic maintenance. Some of the maintenance regulations carried out according to the periodic maintenance are too conservative. Some parts are repaired in advance before reaching the service life, resulting in over-maintenance problems and waste. At the same time, the railway lines in our country are widely distributed and the climates vary from place to place. When operating under different line conditions, the wear of parts is uneven, and there is a lack of a more general method and means for evaluating the state of parts. Summary of the Invention

[0003] Aiming at the technical defects of over-maintenance, waste and lack of a more general method for evaluating the state of parts in the prior art, the present invention provides a method for evaluating the state of large track maintenance machinery.

[0004] The present invention is realized through the following technical solutions: A method for evaluating the operation state of large track maintenance machinery includes the following steps: Step 1: Measure the operation parameters of each component system of the large track maintenance machinery through sensors pre-installed on the large track maintenance machinery; Step 2: Compare the measured operation parameters of each component system of the large track maintenance machinery with the pre-calculated standard operation parameters, judge the degree of deterioration of its operation state, and determine the evaluation score of the operation state of each component system; Step 3: Multiply the pre-set weights of each component system and the weights of each risk point by the evaluation scores of the operation states of each component system and each risk point, so as to calculate the overall operation state score of the current large track maintenance machinery.

[0005] Preferably, the method for setting the weights of each risk point is as follows: A: Set the subjective weight: (1) Establish a judgment matrix model: {C}_{n\times n}=\left [ {{C}_{ij}} \right ] Among them, the elements in the matrix represent the importance degree of the i-th index compared with the j-th index; (2) Through the nine-order ratio scale of the analytic hierarchy process, construct the matrix A = lgC according to the judgment of the importance degree of the index by a single expert, and obtain the result , construct n anti-symmetric matrices A by n experts, and use the standard deviation of the overall sample to represent the differences in judgments among experts: Among them, t represents the expert, and n represents the number of experts; (3) Calculate the arithmetic mean of the anti-symmetric matrices constructed by multiple experts to obtain matrix A, and further obtain the optimal transfer matrix B through the following formula: (4) Construct a judgment matrix , when the matrix meets the consistency condition, use the eigenvalue method to obtain the maximum eigenvalue and the corresponding eigenvector, and normalize to obtain the subjective weights of each component ; B Set the objective weights: (1) For k groups of data of n indicators, obtain the evaluation matrix Q. Q = \left [ {{q}_{ij}} \right ] Among them, is the value of the jth group of objects in the ith group, and each column in the matrix represents a group of data; (2) According to the definition of information entropy, calculate the entropy value of each indicator: In the formula, is the probability of the actual value of each indicator in the entire group of data of the corresponding indicator, (3) According to the information entropy of each indicator, calculate the objective weights of each component: C Calculate the comprehensive weight: .

[0006] The large maintenance machinery operation status evaluation method according to claim 2, wherein the nine-order proportional scale is as shown in the following table: Scale Meaning 1 Indicates that when two indicators are compared, they are equally important 3 Indicates that when two indicators are compared, one indicator is slightly more important than the other 5 Indicates that when two indicators are compared, one indicator is significantly more important than the other 7 Indicates that when two indicators are compared, one indicator is strongly more important than the other 9 Indicates that when two indicators are compared, one indicator is extremely more important than the other 2、4、6、8 The median of the above two adjacent judgments Reciprocal The scale value of the comparison between indicators i and j is the reciprocal of the scale value of the comparison between j and i The present invention conducts a comprehensive evaluation of the wear levels of various accessories and the operation status of each component system of large maintenance technology at any time, so as to mainly perform state-based maintenance and supplemented by planned preventive maintenance, thereby effectively avoiding the problems of over-maintenance and premature maintenance, and avoiding waste. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1This is the flowchart of the method for evaluating the status of large track maintenance machinery of the present invention. Sensors are used to monitor faults and abnormalities in systems such as diesel engines and running systems. If abnormal information of faults is collected, it will enter the information processing module. This module has a diagnostic reasoning module, a omen reasoning module, and an abnormal reasoning program. The diagnostic reasoning module can infer the current fault information based on the collected fault information. The omen reasoning module can predict the upcoming fault information based on the collected fault information. Since the collected information may contain some abnormal information, the abnormal reasoning program can judge the content represented by the abnormal information. The fault information processing method of the total system is the same as that of the subsystem. Subsequently, an inference report is generated to the status information reporting module to judge the severity of the fault or abnormality and its impact on the working ability, and the operator is prompted and warned according to the severity. Figure 2 This is the state evolution process of the large track maintenance machinery of the present invention. In actual engineering, the characteristic parameters as evaluation indicators are only allowed to vary within a certain range. When the value of the characteristic parameter exceeds the set threshold, the equipment will evolve from one state to another operating state. When the characteristic parameter exceeds the deterioration threshold, it indicates that the equipment operation has deviated from the normal operating state and enters the state deterioration area. At this time, the equipment operation state should be closely monitored. When the characteristic parameter exceeds the warning threshold, functional faults may be triggered. When the characteristic parameter exceeds the danger threshold, the equipment operation should be stopped immediately to avoid the occurrence of serious accidents, and corresponding maintenance measures should be taken. Specific embodiments

[0007] To describe the present invention in more detail, the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0008] Various sensors such as temperature and pressure that can collect vehicle status information are installed on the large track maintenance machinery. When evaluating the status of the large track maintenance machinery, the large track maintenance machinery is divided into systems such as diesel engines, power transmission systems, running systems, braking systems, and coupler buffer devices. First, determine the object of status evaluation. Taking the diesel engine as an example, the component with the largest number of faults in the diesel engine is the oil circuit, and most of these are caused by oil leakage from the oil pipe and loosening of the clamp, resulting in a decrease in oil pressure. It can be considered that the oil pressure of the diesel engine can be used as a monitoring object required for status evaluation. Taking the oil pressure as the monitoring object, an evaluation index system for the status is established.

[0009] In actual engineering, the characteristic parameters as evaluation indicators are only allowed to vary within a certain range. When the value of the characteristic parameter exceeds the set threshold, the equipment will evolve from one state to another operating state, as shown in the Figure 2 accompanying

[0010] When the characteristic parameter exceeds the deterioration threshold, it indicates that the device operation has deviated from the normal operation state and entered the state deterioration area. At this time, the device operation state should be closely monitored. When the characteristic parameter exceeds the warning threshold, functional failures may be triggered. When the characteristic parameter exceeds the danger threshold, the device operation should be stopped immediately to avoid the occurrence of serious accidents, and corresponding maintenance measures should be taken. That is to say, when the oil pressure exceeds or is lower than a certain value, it is considered that it has deviated from the normal operation state. The same applies to the other evaluation indicators.

[0011] After determining the evaluation index system, different weight coefficients are assigned to each evaluation index, and the analytic hierarchy process is used to determine the weights of each system and evaluation index.

[0012] Establish a judgment matrix model: {C}_{n\times n}=\left [ {{C}_{ij}} \right ]

[0013] Among them, the elements in the matrix represent the importance degree of the i-th index compared to the j-th index.

[0014] Nine-order ratio scale Scale Meaning 1 Indicates that when two indicators are compared, they are equally important 3 Indicates that when two indicators are compared, one indicator is slightly more important than the other 5 Indicates that when two indicators are compared, one indicator is significantly more important than the other 7 Indicates that when two indicators are compared, one indicator is strongly more important than the other 9 Indicates that when two indicators are compared, one indicator is extremely more important than the other 2、4、6、8 The median of the above two adjacent judgments Reciprocal The scale value of the comparison between indicators i and j is the reciprocal of the scale value of the comparison between j and i As shown in the table, it is the nine-order ratio scale of the analytic hierarchy process. According to a single expert, construct the matrix A = lgC to obtain the result. For n experts, construct n anti-symmetric matrices A, and use the standard deviation of the overall sample to represent the differences in judgments among experts:

[0015] Among them, t represents the expert, and n represents the number of experts.

[0016] Calculate the arithmetic mean of the anti-symmetric matrices corresponding to multiple experts to obtain the matrix A, and further obtain the optimal transfer matrix B through the following formula:

[0017] Construct a judgment matrix , when it meets the consistency condition, use the eigenvalue method to obtain the maximum eigenvalue and the corresponding eigenvector, and normalize to obtain the subjective weight.

[0018] In order to obtain the subjective weights of each key component system, after finding the corresponding experts to score each component, use the above method to construct a judgment matrix. After meeting the conditions, use the eigenvalue method to obtain the maximum eigenvalue and normalize to obtain the corresponding weights. As shown in the table.

[0019] Subjective weight Name Diesel engine Power transmission system Running gear Braking system Coupler buffer device Others Weight 0.09 0.15 0.27 0.42 0.02 0.05 Calculate the objective weight For k groups of data with respect to n indicators, an evaluation matrix Q is obtained, where \(Q = [q_{ij}]\). Among them, is the value of the j-th group of objects for the i-th group. Each column in the matrix represents a group of data.

[0020] According to the definition of information entropy, the entropy value of each indicator is calculated: In the formula, is the probability of the actual value of each indicator in the entire group of data of the corresponding indicator.

[0021] Based on the information entropy of each indicator, the objective weight is calculated

[0022] Calculate the comprehensive weight:

[0023] The key component systems affecting the safety of the large machine are: diesel engine, hydraulic transmission, power transmission system, running gear system, braking system and coupler buffer device. According to the fault information collected previously and after normalizing the fault information, the fault proportion from high to low is diesel engine, running gear system, power transmission system, braking system, hydraulic transmission, coupler buffer device in turn. The objective weight is calculated using the aforementioned formula, and the weights of each subsystem are also different according to the high and low of the fault proportion.

[0024] Objective weight Name Diesel engine Power transmission system Running gear Braking system Coupler buffer device Others Weight 0.42 0.15 0.19 0.16 0.01 0.07 Based on the subjective weight and objective weight obtained above, the comprehensive weight of each system is obtained.

[0025] Comprehensive weight Name Diesel engine Power transmission system Running gear Braking system Coupler buffer device Others Weight 0.255 0.15 0.23 0.29 0.015 0.06 After obtaining the comprehensive weight, taking the braking system as an example, repeat the above operations for each component point in the braking system. The subjective weight is calculated according to the scores of experts, the objective weight is calculated according to the collected data, and the comprehensive weight is calculated as shown in the following table. The same applies to the other systems.

[0026] Weight of braking system Name Weight Brake beam 0.05 Brake shoe 0.05 Brake cylinder 0.10 Brake hose 0.02 Valve parts 0.03 Angle cock 0.02 Others 0.02 After determining the weights of each subsystem, since the component causes of the failures of each subsystem are different, analyze the failure proportion of each component one by one and give the corresponding weights. According to the sensor and the recorded failure status rating of the measurement subsystem, obtain its current state (that is, the normal area, the deterioration area, the warning area, and the danger area. The corresponding score for the danger area is 25, the corresponding score for the warning area is 50, the corresponding score for the deterioration area is 75, and the normal area is 100). Multiply the weight by the current state to obtain its current score, so as to calculate the overall health level of the current large track maintenance machinery.

[0027] After establishing the index evaluation system for large track maintenance machinery, according to the obtained evaluation indexes, use k-clustering to further classify the current indexes, and continuously use the analytic hierarchy process to optimize the weights of each sub-index. Thus, continuously optimize the comprehensive evaluation system of the health level of large track maintenance machinery to determine the current health level of large track maintenance machinery.

Claims

1. A method for evaluating the operating status of large track maintenance machinery, characterized in that it includes the following steps: Step 1: Measure the operating parameters of each component system of the large track maintenance machinery through sensors pre-installed on the large track maintenance machinery; Step 2: Compare the measured operating parameters of each component system of the large track maintenance machinery with the pre-calculated standard operating parameters, judge the degree of deterioration of its operating status, and determine the evaluation scores of the operating status of each component system; Step 3: Multiply the pre-set weights of each component system and the weights of each risk point by the evaluation scores of the operating status of each component system and each risk point, so as to calculate the overall operating status score of the current large track maintenance machinery.

2. The method for evaluating the operating status of large track maintenance machinery according to claim 1, characterized in that the method for setting the weights of each risk point is as follows: A: Set subjective weights: (1) Establish a judgment matrix model: {C}_{n\times n}=\left [ {{C}_{ij}} \right ] Among them, the elements in the matrix represent the importance degree of the i-th index compared to the j-th index; (2)Using the nine - order ratio scale of the analytic hierarchy process, construct the matrix A = lgC according to the judgment of the importance of indicators by a single expert, and obtain the result , construct n anti - symmetric matrices A by n experts, and use the standard deviation of the overall sample to represent the differences in the evaluations among experts: where, t represents an expert, and n represents the number of experts; (3) Calculate the arithmetic mean of the anti-symmetric matrices constructed by multiple experts to obtain matrix A, and further obtain the optimal transfer matrix B through the following formula: (4)Construct the judgment matrix When the matrix meets the consistency condition, the eigenvalue method is used to obtain the maximum eigenvalue and the corresponding eigenvector, and the subjective weights of each component are obtained by normalization ; B Set objective weights: (1) For k sets of data for n indicators, an evaluation matrix Q is obtained. Q = \left [ {{q}_{ij}} \right ] Among them, is the value of the j-th group of objects in the i-th group. Each column in the matrix represents a set of data; (2) According to the definition of information entropy, calculate the entropy value of each index: Wherein, is the probability of the actual value of each index in the entire set of data of the corresponding index, (3) Based on the information entropy of each indicator , the objective weights of each component are calculated : C Calculate the comprehensive weight: 。 3. The method for evaluating the operating status of large track maintenance machinery according to claim 2, characterized in that the nine-order scale is shown in the following table:

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