A failure threshold self-correcting in-service mechanical equipment health condition evaluation method

Abstract

This invention relates to a method for assessing the health status of in-service mechanical equipment with a self-correcting failure threshold. It acquires degradation data of operating parameters such as vibration, temperature rise, and torque of key components of the mechanical equipment under service conditions. Each operating parameter is normalized to obtain a characteristic index. Simultaneously, the degradation amount and rate of the degradation data are combined to achieve self-correction of the failure threshold. The method uses the measurement data of each operating parameter for evaluation, determining the contribution and influence weight of each parameter on the health status of the components. Parameter characteristic weights are allocated and normalized to obtain a component failure index. Rules for determining component failures are provided, identifying the health index of key components affecting the health status of the mechanical equipment. Based on the characteristics of the component health index, the influence weight of each component on the health status of the mechanical equipment is determined, and a mechanical equipment health index is constructed to achieve the assessment and prediction of equipment health status, improving the real-time performance and accuracy of mechanical equipment health assessment.

Description

Technical Field

[0001] This invention relates to a method for assessing the health of mechanical equipment, belonging to the field of mechanical structure strength and life technology, specifically to a method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold. Background Technology

[0002] The health status of mechanical equipment is closely related to the operating status and service performance of its key components (bearings, gears, spindles, etc.). The operating status of these key components can be characterized by measuring their characteristic parameters during service. These characteristic parameters mainly include vibration, temperature rise, and torque. These parameters change continuously during service, and their variations (such as magnitude, amplitude, and rate of change) depend on the service conditions and the inherent characteristics of the parameters themselves. When a component malfunctions, its vibration, temperature rise, and torque will change significantly. By utilizing monitoring data of these characteristic parameters during service and extracting relevant information, the health status of the mechanical equipment can be assessed.

[0003] The health index characterizes the health status of mechanical equipment and is a sensitive feature parameter for maintenance decisions and remaining service life prediction. The health index is not raw data collected by sensors, but rather a quantitative number derived from feature signal extraction and model algorithms, characterizing the reliability or performance degradation of mechanical equipment. Its value typically ranges from (0, 1). A health index closer to 1 indicates healthier equipment; a health index closer to 0 indicates more severe performance degradation.

[0004] In traditional methods for assessing the health status of mechanical equipment, the failure threshold is often given based on empirical data and is a fixed value. However, this is far from sufficient for specific in-service mechanical equipment and may lead to inaccurate assessment results. Therefore, there is a need to propose a method for assessing the health status of in-service mechanical equipment that can achieve self-correction of the failure threshold, in order to meet the requirement of accurate assessment of the health status of in-service mechanical equipment. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art by providing a self-correcting failure threshold method for assessing the health status of in-service mechanical equipment. This method enables the extraction of multi-dimensional feature parameters of key components and the self-correction of failure thresholds, thereby constructing a mechanical equipment health index for assessing the health status of equipment and providing real-time assessment and accurate prediction of the health status of in-service mechanical equipment.

[0006] To achieve the above objectives, the present invention includes: The present invention provides a technical solution for a method for assessing the health status of in-service mechanical equipment with a self-correcting failure threshold. The method involves acquiring the operating parameter data of various indicators of key components of mechanical equipment under service conditions, and obtaining the operating parameter characteristic index corresponding to each indicator after statistical processing of the operating parameter data. If the operating parameter characteristic index is greater than or equal to the failure threshold, it indicates that the corresponding component has failed. The failure threshold is determined as follows: First, an initial failure threshold is set based on experience. Then, the stage of mechanical equipment degradation is determined. If the mechanical equipment is in the early stage of degradation, the failure threshold is the initial failure threshold. If the mechanical equipment is in the middle stage of degradation, it is adjusted according to the amount of degradation. If the mechanical equipment is in the late stage of degradation, the current failure threshold is maintained and no adjustment is made.

[0007] The method of this invention uses monitoring data of the operating status of key components of mechanical equipment under service conditions as reference data. It continuously corrects the failure threshold of the components by combining the degradation amount and degradation rate of degradation data. Based on the failure threshold dynamically adjusted according to the degradation process of the mechanical equipment, the operating parameters obtained from the monitoring data are compared with the failure threshold to predict the occurrence of component failures. The failure threshold of this invention is dynamically adjusted according to the degradation process of the mechanical equipment, which better conforms to the possible failure patterns of mechanical equipment components and improves the accuracy of mechanical equipment health status assessment. Furthermore, if the mechanical equipment is in the middle stage of degradation, the failure threshold is corrected as follows: if the degradation amount is less than a first set multiple of the current failure threshold (the first set multiple is less than 1), the current failure threshold remains unchanged; otherwise, the current failure threshold is corrected by multiplying it by a second set multiple (the second set multiple is greater than 1).

[0008] Furthermore, the degradation stage of the mechanical equipment is determined as follows: if the degradation rate is greater than a set value and the degradation amount is less than the initial failure threshold, it belongs to the early stage of degradation; if the degradation rate is less than the set value, it belongs to the middle stage of degradation; if the degradation rate is greater than the set value and the degradation amount is not less than a third set multiple of the current failure threshold, wherein the third set multiple is less than 1, it belongs to the late stage of degradation.

[0009] Furthermore, by normalizing the operating parameter data, the operating parameter characteristic index is obtained; the operating parameter data is normalized using the following formula: In the formula, It is the original signal of normal operation. and the state signal to be evaluated in the operating parameter data The positive covariance function between them and These are signals and signal standard deviation For the characteristic index of operating parameters, .

[0010] Furthermore, the data characteristics of the acquired operating parameter data are used for evaluation. The weight coefficient of each operating parameter is obtained based on the contribution of each operating parameter to the health status of the part. The product of each operating parameter characteristic index and the corresponding weight coefficient is accumulated to obtain the mechanical equipment part failure index. When the operating parameter characteristic index is greater than or equal to 1, the part failure index is equal to 1, indicating that the mechanical equipment part has failed.

[0011] Furthermore, the data characteristics of the acquired operating parameter data are used for evaluation. The analytic hierarchy process (AHP) is employed to derive the weight coefficients of each operating parameter based on their contribution to the health status of the parts.

[0012] Furthermore, key components affecting the health status of mechanical equipment are identified, and their health status is characterized by a health index, which is equal to 1 minus the corresponding component failure index. Based on the characteristics of the health index of the key components, the degree of influence of each key component on the health status of the mechanical equipment is determined, and then the influence weight of each key component on the health status of the mechanical equipment is derived based on the degree of influence. The health index of each key component is multiplied by its corresponding influence weight and summed to obtain the health index of the mechanical equipment. The health status of the mechanical equipment is then assessed based on the health index.

[0013] The present invention further obtains the characteristic information of the change of key component feature parameters during service, gives the key component health index that affects the health status of mechanical equipment, and constructs a mechanical equipment health index based on the characteristics of the component health index and its influence weight on the health status of mechanical equipment, which is used for real-time assessment and prediction of the health status of mechanical equipment.

[0014] Furthermore, the higher the mechanical equipment health index, the healthier the equipment; when the mechanical equipment health index is less than a preset critical threshold, an early warning is issued that the mechanical equipment is in a dangerous state.

[0015] The beneficial effects of this invention are as follows: This invention proposes a method for assessing the health status of in-service mechanical equipment. By measuring characteristic parameters (such as vibration, temperature rise, and torque) of key components during service, the method obtains the changing trends of these parameters under service conditions, continuously revises the failure threshold, and provides a health index for key components affecting the health status of the mechanical equipment. Based on the characteristics of this component health index, a mechanical equipment health index for assessing the overall equipment health status can be constructed. This method, based on real-time measurement data of characteristic parameters of key components under service conditions, continuously revises the failure threshold, accurately reflecting the actual operating status of the equipment. This allows for the prediction of potential equipment failures, assessment of equipment health status, and improvement of the real-time nature and accuracy of equipment health assessment. Attached Figure Description

[0016] Figure 1 This is a flowchart of the mechanical equipment health status assessment method of the present invention; Figure 2 This is a schematic diagram illustrating the principle of collecting characteristic parameters of key components of mechanical equipment. Figure 3 This is a schematic diagram of the self-correction process for the failure threshold of mechanical parts according to the present invention; Figure 4 This is a schematic diagram illustrating the determination of the health status of mechanical equipment based on the mechanical equipment health index according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0018] This invention discloses a method for assessing the health status of in-service mechanical equipment with a self-correcting failure threshold. The basic concept involves acquiring operational parameter data (vibration, temperature rise, torque, etc.) of key components (bearings, gears, spindles, etc.) of the mechanical equipment under service conditions, normalizing these data to obtain characteristic indices for each operational parameter, evaluating the contribution and influence weight of each parameter on the health status of the components, allocating parameter characteristic weights and normalizing the data to obtain component failure indices, providing rules for determining component failures, and identifying the health indices of key components affecting the health status of the mechanical equipment. Based on the characteristics of the component health indices, the influence weight of each component on the health status of the mechanical equipment is determined, and a mechanical equipment health index is constructed to achieve the assessment and prediction of the equipment's health status.

[0019] Specifically, the mechanical equipment health status assessment method of the present invention is as follows: Figure 1 As shown, this can be achieved through the following steps: The first step is to normalize the parameter characteristics of the parts under service conditions.

[0020] like Figure 2As shown, for the operating parameters of key components (such as bearings, gears, and spindles) of mechanical equipment during service, such as vibration, temperature rise, and torque, a certain amount of key component operating parameter data (variables) is obtained by measuring corresponding sensors. X 1. X 2. X 3, ... X m (etc.) to perform statistical data processing and extraction on the operating parameter data of key components.

[0021] Based on the operating parameters (variables) of key components X 1. X 2. X 3, ... X m The variation characteristics of the values ​​(e.g., are analyzed, and their numerical values ​​are normalized to obtain the characteristic index of the operating parameters.) , ,in, This indicates that the system is operating normally. Indicates a status pending evaluation. It is the original signal of normal operation. and the state signal to be evaluated in the operating parameter data The positive covariance function between them and These are signals and standard deviation For the characteristic index of operating parameters, .

[0022] The second step, as Figure 3 As shown, the failure threshold is corrected: (1) Set the mid-term degradation rate based on experience. and a relatively conservative initial failure threshold ; (2) Calculate the degradation rate of the monitored mechanical equipment based on the degradation data of the monitored mechanical equipment. ; (3) Determine the degradation stage and adjust the failure threshold. If the degradation rate Larger (e.g.) Figure 3 medium degradation rate stage, And the amount of degradation Much smaller than the initial failure threshold If the degradation is in the early stage, then the failure threshold is the initial failure threshold. If the degradation rate Gradually stabilizing (degradation rate) Less than the set value, such as Figure 3 medium degradation rate (Stage), then the degradation is in the middle stage. At this time, if the degradation amount Less than the failure threshold of times ( If the degradation amount remains unchanged, then the failure threshold remains unchanged. Not less than the failure threshold of times ( If so, the failure threshold is adjusted (failure threshold at this stage). for: ); if degradation rate Larger (e.g.) Figure 3 medium degradation rate stage, And the amount of degradation Not less than the failure threshold of times ( If the degradation is in the later stages, then the current failure threshold is maintained. Stop correcting the failure threshold. .

[0023] The third step is to assign feature weights to part parameters (key part feature parameters).

[0024] Based on the characteristics of the measurement data of the operating status characterization parameters (key component characteristic parameters) of each component, the contribution of these parameters to the operating status of the component is evaluated. According to the analytic hierarchy process, the influence weights are given, and parameter characteristic weights are allocated and normalized.

[0025] in, Indicates the first i The first indicator is compared to the first j The importance of each indicator; calculate the matrix. P The feature vectors are then normalized:

[0026]

[0027] The weight vector of the evaluation index determined by the analytic hierarchy process is then: .

[0028] Based on weighting coefficients and parameter characteristic indices Obtain the component failure index F , Its value is: 0≤ F ≤1, prepresents the weighting coefficients for the influence of service parameters on the health status of a component, where m is the number of parameters.

[0029] The fourth step is to provide the rules for determining component failure.

[0030] use Y m and F These elements collectively represent the operational status information of the components. The rules for determining component faults are as follows: (1) When Greater than or equal to the failure threshold At that time, or F Greater than or equal to the threshold When this happens, it indicates that the part has malfunctioned.

[0031] (2) When If it is greater than or equal to 1, then F =1 indicates that the part has malfunctioned.

[0032] (3) Since the data curves characterizing the operating parameters of the parts (such as vibration, temperature, etc.) will change continuously over time during service, the changing trends of the data curves of different parts or different parameters have their own characteristics. Therefore, we can continue to the following steps five, six, and seven to determine whether the parts have failed by using the combination of the slope and threshold of the data curves characterizing the parameters.

[0033] The fifth step is to determine the health index of key components.

[0034] Identify the key components that affect the health status of mechanical equipment, such as bearings, gears, and spindles. Use the key component health index. H n Characterizing the health status of critical components, it is defined as follows: H n =1- F . H n The size of the component indicates its health condition. When H n The closer it is to 1, the better the health condition of the part.

[0035] The sixth step is to determine the health index of the mechanical equipment.

[0036] Based on the characteristics of the health index of each component, the degree of influence of each component on the health status of the mechanical equipment is determined, and the weight allocation of its influence on the health status of the equipment is given to form the equipment health index. S , .

[0037] Where n is the number of key parts characterizing the health status of the equipment. qThe weighting coefficients for each component's impact on the health of the mechanical equipment are calculated using the same method as in step two.

[0038] Step 7: Determine the health status of mechanical equipment.

[0039] Use equipment health index S Information representing the health status of equipment can specifically be generated as follows: Figure 4 The diagram shows the health index of key components and mechanical equipment. , , They represent the 1st and the 2nd respectively. i The, the n The health index of each key component is calculated in step six to obtain the overall equipment health index. S Equipment Health Index S The size of the scale indicates the degree of equipment health. S The closer it is to 1, the healthier the equipment. S A value close to 0 indicates a more severe degradation in equipment performance and a greater susceptibility to malfunctions. When... S Less than the critical threshold for the health status of mechanical equipment S lim The warning is issued when the machinery is in a dangerous state.

Claims

1. A method for health condition assessment of in-service mechanical equipment with failure threshold self-correction, characterized in that, Obtain the operating parameter data of various indicators of key components of mechanical equipment under service conditions, and obtain the corresponding operating parameter characteristic index of each indicator characteristic based on the operating parameter data using the following formula: It is the original signal of normal operation. and the state signal to be evaluated in the operating parameter data The positive covariance function between them and These are signals and signal The standard deviation of Y, where Y is the characteristic index of the operating parameters. If the characteristic index of the operating parameter is greater than or equal to the failure threshold, it indicates that the corresponding part has failed. The data characteristics of the operating parameters are evaluated, and the weight coefficient of each operating parameter is obtained based on its contribution to the health status of the parts. The failure index of mechanical equipment parts is obtained by multiplying and summing the characteristic index of each operating parameter with its corresponding weight coefficient. When the characteristic index of the operating parameter is greater than or equal to 1, the failure index of the part is equal to 1, indicating that the mechanical equipment part has failed. Identify the key components affecting the health status of mechanical equipment, and characterize the health status of the components using a health index, which is equal to 1 minus the corresponding component failure index. Based on the characteristics of the health index of key components, determine the degree of influence of each key component on the health status of the mechanical equipment, and then derive the influence weight of each key component on the health status of the mechanical equipment based on the degree of influence. Multiply the health index of each key component by its corresponding influence weight and sum them up to obtain the health index of the mechanical equipment. Evaluate the health status of the mechanical equipment based on the health index. The failure threshold is determined as follows: First, an initial failure threshold is set based on experience. Then, the stage of mechanical equipment degradation is determined. If the mechanical equipment is in the early stage of degradation, the failure threshold is the initial failure threshold. If the mechanical equipment is in the middle stage of degradation, it is adjusted according to the amount of degradation. If the mechanical equipment is in the late stage of degradation, the current failure threshold is maintained and no adjustment is made. If the mechanical equipment is in the middle stage of degradation, the failure threshold is corrected in the following way: if the degradation amount is less than a first set multiple of the current failure threshold, and the first set multiple is less than 1, then the current failure threshold remains unchanged; Otherwise, the current failure threshold will be corrected by multiplying it by a second set factor, where the second set factor is greater than 1.

2. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, The key components of the mechanical equipment include bearings, gears, and spindles, and the operating parameter data includes vibration, temperature rise, and torque parameters; the operating parameter data of the key components of the mechanical equipment are obtained by measuring corresponding sensors.

3. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, The degradation stage of mechanical equipment is determined as follows: if the degradation rate is greater than a set value and the degradation amount is less than the initial failure threshold, it belongs to the early stage of degradation; if the degradation rate is less than the set value, it belongs to the middle stage of degradation; if the degradation rate is greater than the set value and the degradation amount is not less than a third set multiple of the current failure threshold, wherein the third set multiple is less than 1, it belongs to the late stage of degradation.

4. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 3, characterized in that, The degradation rate is calculated based on the degradation data of the monitored mechanical equipment.

5. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, When the failure index of a part is greater than or equal to the threshold, it indicates that the part has failed.

6. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, The data characteristics of the acquired operating parameter data are used for evaluation. The analytic hierarchy process (AHP) is adopted to derive the weight coefficient of each operating parameter based on its contribution to the health status of the parts.

7. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, The closer the health index of a critical component is to 1, the better the component's health condition.

8. The method for assessing the health status of in-service mechanical equipment with self-correcting failure threshold according to claim 1, characterized in that, The higher the health index of the mechanical equipment, the healthier the equipment; when the health index of the mechanical equipment is less than the preset critical threshold, an early warning is issued that the mechanical equipment is in a dangerous state.

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