Electromechanical equipment health assessment method based on multi-parameter fusion

Abstract

The invention belongs to the technical field of electromechanical equipment state monitoring, and particularly discloses an electromechanical equipment health assessment method based on multi-parameter fusion. The method comprises the following steps: firstly, acquiring various sensing data in the operation process of electromechanical equipment through a sensor, and then preprocessing the sensing data; then, the instantaneous health degree of the equipment is obtained based on the deviation between the real-time value and the historical mean value of the sensing data of the electromechanical equipment; a sub-health threshold value is preset, and the deviation exceeding the sub-health threshold value is accumulated to obtain accumulated damage of the equipment; and finally, fusing the instantaneous health degree and the accumulated damage to obtain the comprehensive health degree of the equipment. According to the method, the instantaneous health degree in the real-time operation process of the equipment and the accumulated damage in the long-term operation process of the equipment are comprehensively considered, the instantaneous health degree and the accumulated damage are fused and quantified to obtain the comprehensive health degree of the electromechanical equipment, the health assessment method can reflect the operation state of the equipment, and effective guidance is provided for maintenance and guarantee of equipment operation.

Description

Technical Field

[0001] This application belongs to the field of electromechanical equipment condition monitoring technology, and more specifically, relates to a method for electromechanical equipment health assessment based on multi-parameter fusion. Background Technology

[0002] Health assessments of electromechanical equipment are crucial for ensuring the safety of electromechanical systems and improving economic efficiency. By analyzing data such as vibration, temperature, and fluid levels in real time, early anomalies in electromechanical equipment (such as bearing wear and insulation degradation) can be accurately identified, preventing safety accidents caused by sudden failures. Condition-based predictive maintenance can reduce unplanned downtime losses, optimize spare parts and labor costs, and extend equipment life. At the same time, it can ensure that emissions and energy efficiency comply with environmental regulations, reducing compliance risks.

[0003] Currently, existing electromechanical equipment condition monitoring technologies focus on predicting equipment failures, often neglecting the cumulative damage during equipment operation and failing to consider the performance degradation caused by long-term operation of equipment in a sub-healthy state. Therefore, there is an urgent need for a health assessment method for electromechanical equipment. Summary of the Invention

[0004] In view of the above-mentioned defects or improvement needs of the existing technology, this application provides a health assessment method for electromechanical equipment based on multi-parameter fusion, which aims to solve the problem that existing electromechanical equipment condition monitoring technology often ignores the impact of cumulative damage during equipment operation.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for health assessment of electromechanical equipment based on multi-parameter fusion, comprising: Sensor data acquisition and preprocessing; A dynamic safety zone is constructed based on historical values ​​of sensor data, and the mapping of the deviation between the real-time value and the historical average value of the sensor data in the dynamic safety zone is used as the instantaneous health of the device. The sub-health threshold is set according to the dynamic safety zone, and the cumulative damage to the equipment is obtained by accumulating the deviations that exceed the sub-health threshold. The overall health of the device is obtained by combining the instantaneous health status and the cumulative damage.

[0006] Preferably, a dynamic safety zone is constructed based on historical values ​​of sensor data, and the instantaneous health of the device is represented by the mapping of the deviation between the real-time value and the historical average value of the sensor data within the dynamic safety zone. Specifically: A dynamic safety zone is constructed based on the type of sensor data and the historical standard deviation of the sensor data; The instantaneous fluctuation level can be obtained from the ratio of the deviation to the dynamic safety zone; The instantaneous health level is inversely proportional to the instantaneous fluctuation level.

[0007] Preferably, a dynamic safety zone is constructed based on the type of sensor data and the historical standard deviation of the sensor data, specifically as follows: Obtain the corresponding preset fault tolerance factor based on the type of sensor data; The dynamic safety zone is obtained by multiplying the historical standard deviation of the sensor data and the fault tolerance factor.

[0008] Preferably, the cumulative damage to the device is obtained by accumulating deviations exceeding the sub-health threshold, specifically as follows: The cumulative deviation is obtained by accumulating deviations that exceed the sub-health threshold. Obtain the corresponding preset damage rate coefficient based on the type of sensor data; The cumulative damage is obtained by multiplying the cumulative deviation and the damage rate coefficient.

[0009] Preferably, a sub-health threshold is set according to the dynamic safety zone, specifically as follows: Obtain the corresponding preset sub-health coefficient based on the type of sensor data; The sub-health threshold is obtained by multiplying the dynamic safety zone and the sub-health coefficient.

[0010] Preferably, the overall health of the device is obtained by integrating the instantaneous health status and the cumulative damage, specifically as follows: Obtain the corresponding preset fault contribution factor based on the type of sensor data; The health of the equipment decreases exponentially under the cumulative damage, as described by the natural constant. The comprehensive health level is obtained by multiplying the fault contribution factor, the health level under cumulative damage, and the instantaneous health level.

[0011] Preferably, the health of the equipment decreases exponentially under the influence of cumulative damage, as described by the natural constant; specifically:

[0012] in, Indicates the first Sensor data in Health level under the cumulative damage over time. Represents the natural constant. This represents the preset cumulative damage impact factor. Indicates the first Sensor data in Cumulative damage over time.

[0013] Preferably, it also includes: summing up the sensor data of each sensor on the device to obtain the overall health score, and then obtaining the final overall health score of the device.

[0014] Preferably, the preprocessing of the sensor data specifically involves setting a sliding window and performing sliding filtering on the collected sensor data.

[0015] In a second aspect, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0016] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: (1) The electromechanical equipment health assessment method of this application comprehensively considers the instantaneous health of the equipment during real-time operation and the cumulative damage during long-term operation, and integrates the two to obtain the comprehensive health of the electromechanical equipment. Compared with the existing related technologies, this application takes into account the damage caused to the equipment by long-term operation in a sub-healthy state into the measurement of equipment health. The equipment health obtained in this way can better reflect the operating status of the equipment and provide effective guidance for the maintenance and protection of equipment operation.

[0017] (2) During long-term operation, the cumulative damage to the equipment is determined by the equipment material and sensor data. For example, different temperatures have different effects on equipment made of the same material, and the same temperature has different effects on equipment made of different materials. Therefore, in the process of quantifying the cumulative damage of the equipment, this application incorporates the types of sensor data into the consideration criteria and sets a damage rate coefficient to characterize the damage effect of different sensor data on different materials. The cumulative damage obtained in this way is more scientific and effective.

[0018] (3) The cumulative damage and instantaneous health of the equipment affect each other. High instantaneous health will be reduced by high cumulative damage. Therefore, in the process of integrating lost health and cumulative damage, this application describes the state of exponentially accelerating decline of equipment health under the action of cumulative damage through the exponential description of the natural constant. This accurately quantifies the impact of cumulative damage on equipment health and lays a mathematical foundation that conforms to physical laws for further obtaining comprehensive health.

[0019] (4) This application can comprehensively consider the health of the device through multiple sensor data. Different weights are obtained by the influence of different sensor data on the health of the device. The comprehensive health obtained by the sensor data is weighted and accumulated to obtain the final comprehensive health. Compared with the technology that relies on a single parameter to obtain the device status, this application considers the device status more comprehensively. Attached Figure Description

[0020] Figure 1This is a flowchart illustrating a method for assessing the health of electromechanical equipment based on multi-parameter fusion, as provided in an embodiment of this application.

[0021] Figure 2 This is a schematic diagram illustrating the exponentially accelerating decline in the health of a device under the cumulative damage provided in this application embodiment.

[0022] Figure 3 This is a schematic diagram illustrating the application of the health assessment method provided in this application embodiment to equipment operation and maintenance.

[0023] Figure 4 This is a schematic diagram of the structural composition of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] The terms "first" and "second," etc., used in the description and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first sensing data" and "second sensing data," etc., are used to distinguish different types of sensing data, not to describe a specific order of sensing data.

[0026] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0027] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple sensor data means two or more sensor data, multiple objects means two or more objects, etc.

[0028] The embodiments of this application are described below with reference to the accompanying drawings.

[0029] like Figure 1 The diagram shown is a flowchart of a multi-parameter fusion electromechanical equipment health assessment method according to an embodiment of this application, which specifically includes the following steps: S1. Collect various sensor data by deploying multiple sensors on multiple key components of electromechanical equipment: In some embodiments, vibration data on the bearing and current or voltage data at the output port are collected.

[0030] In some embodiments, pressure data of load-bearing components and temperature data of moving components are collected.

[0031] In some embodiments, only vibration data from the bearing is collected.

[0032] S2. Preprocess the collected sensor data: In some embodiments, a sliding filter is used to filter out abrupt data points, such as: Design a sliding window with a width of 10 and a sliding step of 1, and replace the data in the middle position of the window with the mean of all data in the window.

[0033] In some embodiments, wavelet transforms are used to filter out data noise.

[0034] In some embodiments, interpolation is used to fill in missing data.

[0035] S3. Calculation of instantaneous health: In this application, a dynamic safety zone is constructed using the historical mean of sensor data as a benchmark and the historical standard deviation of sensor parameters. The physical meaning of the instantaneous health of electromechanical equipment is defined as: the mapping of the deviation between the real-time value of the sensor parameters and the benchmark within the dynamic safety zone. Specifically, this includes the following steps: S31. Obtain the corresponding preset fault tolerance factor according to the type of sensor data. subscript Indicates the first Sensor data.

[0036] In some embodiments, the acquired sensing data is the temperature T of the rotating bearing, and therefore it is mapped to a preset fault tolerance factor. .

[0037] In some embodiments, the acquired sensing data is the vibration of the rotating bearing, and therefore it is mapped to a preset fault tolerance factor. .

[0038] S32. The dynamic safety zone is obtained by multiplying the historical standard deviation of the sensor data and the fault tolerance factor. ,in, Indicates the first Historical standard deviation of sensor data.

[0039] S33. The instantaneous fluctuation level is obtained by comparing the deviation between the real-time value and the historical average of the sensor data with the dynamic safety zone:

[0040] satisfy:

[0041] in Indicates the first Sensor data in Real-time value at any given moment. Indicates the first Historical average of sensor data.

[0042] S34. Instantaneous health is inversely proportional to the degree of instantaneous fluctuation:

[0043] in, For the first Sensor data in The instantaneous health status of the device as represented by a given moment.

[0044] In this application, the fluctuation of the device's sensing parameters characterizes the instantaneous health of the device, which conforms to actual physical laws, and the obtained instantaneous health can accurately reflect the state of the device at the moment of operation.

[0045] S4. Calculation of cumulative damage: In this application, the physical meaning of cumulative damage is defined as the accumulation of abnormalities caused by sensor data deviations under sub-healthy operating conditions. Specifically, it includes the following steps: S41. Obtain the corresponding preset sub-health coefficient based on the type of sensor data: The sub-health index is related to the type of sensor data.

[0046] In some embodiments, the sub-health coefficient of bearing temperature is 0.5. If the bearing temperature data exceeds 0.5 times the dynamic safety zone, the bearing is considered to be in a sub-healthy operating state.

[0047] In some embodiments, the sub-health coefficient of bearing vibration is 0.35. Therefore, if the bearing vibration data exceeds the dynamic safety zone by 0.35 times, the bearing is considered to be in a sub-healthy operating state.

[0048] The sub-health threshold is obtained by multiplying the dynamic safety zone and the sub-health coefficient.

[0049] In this embodiment, the sub-health coefficient is 0.5.

[0050] S42, Cumulative 0 to The cumulative deviation is calculated for deviations that exceed the sub-health threshold at any given time.

[0051] in, express The deviation between the real-time value and the historical average of the sensor data. This indicates that the condition exceeds the sub-health threshold. The deviation. This indicates finding the maximum value.

[0052] S43. Obtain the corresponding preset damage rate coefficient according to the type of sensor data: .

[0053] Indicates the first The damage rate coefficient corresponding to the sensor data is the physical meaning of the damage rate coefficient, which is the rate at which the material of the device consumes its lifespan under the sensor data, that is, the damage rate, and is determined by the physical properties of the material of the device.

[0054] For example, the damage rate coefficient of temperature sensing data reflects the thermal aging law of insulating materials.

[0055] The damage rate coefficient of vibration sensing data reflects the fatigue characteristics of bearing steel.

[0056] S44. The cumulative damage is obtained by multiplying the cumulative deviation and the damage rate coefficient:

[0057] in, Indicates the first Sensor data in The cumulative damage to the equipment at any given time.

[0058] This application incorporates the types of sensor data into the criterion for quantifying cumulative equipment damage, and sets a damage rate coefficient to characterize the impact of different sensor data on the damage to different equipment materials. The cumulative damage obtained in this way is more scientific and effective.

[0059] S5. The overall health status of the equipment is obtained by combining instantaneous health status and cumulative damage: In this application, the physical meaning of comprehensive health is defined as the coupled influence of instantaneous health and cumulative damage on equipment health, specifically including the following steps: S51. Obtain the corresponding preset fault contribution factor according to the type of sensor data: , indicating the first Fault contribution factor of sensor data.

[0060] In some embodiments, the fault contribution factor is obtained from laboratory data.

[0061] In some embodiments, the fault contribution factor is determined by expert scoring.

[0062] S52. Under the influence of cumulative damage, the trend of equipment health is as follows: Figure 2 As shown: Therefore, this application describes the exponentially accelerating decline in the health of the device under the cumulative damage using the exponential of the natural constant:

[0063] in, Indicates the first Sensor data in Health level under the cumulative damage over time. Represents the natural constant. This represents the preset cumulative damage impact factor. Indicates the first Sensor data in Cumulative damage over time.

[0064] S53. The comprehensive health level is obtained by multiplying the fault contribution factor, the health level under cumulative damage, and the instantaneous health level.

[0065] The above formula represents the first Sensor data in The overall health status of the equipment at any given time.

[0066] S54. Accumulate the overall health score obtained from the sensor data of each sensor on the device to obtain the final overall health score of the device:

[0067] in, Indicates that the device is in The final overall health status at any given moment, where N represents the total number of sensors.

[0068] S6. Perform equipment maintenance based on the final comprehensive health status of the equipment. For the specific maintenance model, please refer to [link / reference]. Figure 3 visible: In this embodiment, based on the device's final overall health status, the device status is set to three levels: If the final overall health score of the equipment is greater than or equal to 80, the equipment is considered healthy and operating normally, and no intervention is required.

[0069] If the final overall health score of the equipment is between 50 and 80, the equipment is considered to be in a sub-healthy state, and the equipment maintenance procedure should be initiated at this time.

[0070] If the final overall health status of the equipment is less than or equal to 50, the equipment is considered to be in a faulty state, and the equipment shutdown and maintenance procedure must be initiated.

[0071] This application further discloses a specific embodiment of a health assessment of equipment to illustrate the electromechanical equipment health assessment method of this application: Select centrifugal fan as the equipment target; Vibration data of the rotary bearing were collected using sensors. (Sampling frequency 10KHz); The fault tolerance factor corresponding to the sensor data is:

[0072] The damage rate coefficient corresponding to the sensor data is:

[0073] The fault contribution factor corresponding to the sensor data is:

[0074] Cumulative damage impact factor corresponding to sensor data It is 0.01; time, Historical average Historical standard deviation ; Calculate the overall health status of the centrifugal fan at this moment: Instant health ; Cumulative damage ; Overall health If the value is less than 50% of the fault assessment standard, a fault alarm will be triggered, and a shutdown and maintenance procedure will be initiated.

[0075] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0076] Based on the methods in the above embodiments, this application provides an electronic device, such as... Figure 4 The electronic device shown may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions stored in the memory to execute the methods described in the above embodiments.

[0077] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0078] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0079] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0080] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0081] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0082] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0083] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0084] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for health assessment of electromechanical equipment based on multi-parameter fusion, characterized in that, include: Sensor data acquisition and preprocessing; A dynamic safety zone is constructed based on historical values ​​of sensor data, and the mapping of the deviation between the real-time value and the historical average value of the sensor data in the dynamic safety zone is used as the instantaneous health of the device. The sub-health threshold is set according to the dynamic safety zone, and the cumulative damage to the equipment is obtained by accumulating the deviations that exceed the sub-health threshold. The overall health of the device is obtained by combining the instantaneous health status and the cumulative damage.

2. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, A dynamic safety zone is constructed based on historical values ​​of sensor data. The instantaneous health of the device is represented by the mapping of the deviation between the real-time value and the historical average value of the sensor data within the dynamic safety zone. Specifically: A dynamic safety zone is constructed based on the type of sensor data and the historical standard deviation of the sensor data; The instantaneous fluctuation level can be obtained from the ratio of the deviation to the dynamic safety zone; The instantaneous health level is inversely proportional to the instantaneous fluctuation level.

3. The method for health assessment of electromechanical equipment according to claim 2, characterized in that, A dynamic safety zone is constructed based on the type of sensor data and the historical standard deviation of the sensor data, specifically as follows: Obtain the corresponding preset fault tolerance factor based on the type of sensor data; The dynamic safety zone is obtained by multiplying the historical standard deviation of the sensor data and the fault tolerance factor.

4. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, The cumulative damage to the equipment is calculated by accumulating deviations exceeding the sub-health threshold, specifically: The cumulative deviation is obtained by accumulating deviations that exceed the sub-health threshold. Obtain the corresponding preset damage rate coefficient based on the type of sensor data; The cumulative damage is obtained by multiplying the cumulative deviation and the damage rate coefficient.

5. The method for health assessment of electromechanical equipment according to claim 1 or 4, characterized in that, The sub-health threshold is set according to the aforementioned dynamic safety zone, specifically as follows: Obtain the corresponding preset sub-health coefficient based on the type of sensor data; The sub-health threshold is obtained by multiplying the dynamic safety zone and the sub-health coefficient.

6. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, The overall health of the device is obtained by combining the instantaneous health status and the cumulative damage, specifically as follows: Obtain the corresponding preset fault contribution factor based on the type of sensor data; The health of the equipment decreases exponentially under the cumulative damage, as described by the natural constant. The comprehensive health level is obtained by multiplying the fault contribution factor, the health level under cumulative damage, and the instantaneous health level.

7. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, The health of equipment decreases exponentially under cumulative damage, as described by the natural constant; specifically: in, Indicates the first Sensor data in Health level under the cumulative damage over time. Represents the natural constant. This represents the preset cumulative damage impact factor. Indicates the first Sensor data in Cumulative damage over time.

8. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, Also includes: The overall health score of the device is obtained by summing the sensor data of each sensor on the device.

9. The method for health assessment of electromechanical equipment according to claim 1, characterized in that, The preprocessing of the sensor data specifically involves setting a sliding window and performing sliding filtering on the collected sensor data.

10. An electronic device, characterized in that, include: At least one memory for storing computer programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-9.

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