Compressor health state determination method and device, vehicle and medium
By installing vibration sensors and operating status acquisition components in the compressor, vibration and operating data are collected and analyzed. Combined with environmental data, feature weights are dynamically adjusted, solving the problem of difficulty in detecting early compressor faults in existing technologies and achieving higher detection accuracy and adaptability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING JINKANG NEW ENERGY VEHICLE CO LTD
- Filing Date
- 2026-01-05
- Publication Date
- 2026-04-21
AI Technical Summary
Current compressor condition monitoring mainly focuses on monitoring operating parameters such as discharge pressure and flow rate, lacking the analysis of vibration data. This makes it difficult to detect potential problems such as early wear or cracks in the moving disc. Furthermore, traditional methods are difficult to adapt to dynamic operating conditions, reducing the accuracy and adaptability of the monitoring.
By installing vibration sensors and operating status acquisition components in the compressor, vibration status data and operating status data are collected, vibration energy entropy characteristics and operating status characteristics are calculated, and feature weight coefficients are dynamically determined in combination with operating environment data to calculate compressor health parameters, thereby achieving multi-dimensional health status assessment.
It can detect early compressor failures in a timely manner, improving the comprehensiveness and reliability of health status detection. It can dynamically adjust feature weights to adapt to different operating conditions, avoiding the limitations of traditional fixed weights and improving the accuracy and adaptability of detection.
Smart Images
Figure CN121897587A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of compressor control, and in particular to a method, apparatus, vehicle, and medium for determining the health status of a compressor. Background Technology
[0002] Currently, the turbo compressor is a key power device in the thermal management system of new energy vehicles. Its health status has a direct impact on the system's operating efficiency, reliability, and overall vehicle performance, and is a core component to ensure the efficient and safe operation of new energy vehicles.
[0003] However, current compressor condition monitoring primarily focuses on monitoring operating parameters such as discharge pressure and flow rate, lacking analysis of vibration data. This singular monitoring method struggles to detect potential problems like early wear or cracks in the moving disc, failing to comprehensively reflect the compressor's health status. Furthermore, condition monitoring using performance parameters typically requires pre-setting the weights of each parameter. However, fixed weights are ill-suited to the complex changes in turbo compressors under dynamic operating conditions, leading to reduced accuracy in compressor condition monitoring. Summary of the Invention
[0004] In view of the above problems, this application proposes a method, apparatus, vehicle and medium for determining the health status of a compressor.
[0005] In a first aspect of this application, a method for determining the health status of a compressor is provided, wherein a vibration sensor and an operating status acquisition component are installed in the compressor, and the method includes: The vibration state data of the compressor is collected by the vibration sensor, and the operating state data of the compressor is collected by the operating state acquisition component. The vibration energy entropy characteristics of the compressor are determined based on the vibration state data; The operating status characteristics of the compressor are determined based on the operating status data; Obtain the operating environment data of the compressor, and determine the feature weight coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data; The compressor health parameters are determined based on the vibration energy entropy characteristics, the operating state characteristics, and the characteristic weighting coefficients. The compressor's health status is determined based on the compressor health parameters.
[0006] Optionally, the vibration state data includes vibration signal data, and determining the vibration energy entropy characteristics of the compressor based on the vibration state data includes: The vibration signal data is decomposed using a preset filter function to obtain several vibration signal frequency bands; Based on a preset target vibration frequency band, select several target signal frequency bands corresponding to the target vibration frequency band from several vibration signal frequency bands; For any of the target signal frequency bands, determine the frequency band vibration energy corresponding to the target signal frequency band; The vibration energy distribution probability is determined based on the vibration energy of the frequency bands corresponding to the target signal frequency bands. The vibration energy entropy characteristic is determined based on the vibration energy distribution probability.
[0007] Optionally, the compressor includes a power supply circuit and an exhaust port, the operating status acquisition component includes a current harmonic analyzer, a compressor temperature sensor, and a pressure sensor, and the operating status data includes current harmonic data, compressor temperature data, and exhaust pressure data. The acquisition of the compressor's operating status data through the operating status acquisition component includes: The current harmonic data of the power supply circuit are collected by the current harmonic analyzer. The compressor temperature sensor collects several compressor temperature data at a preset frequency. The pressure sensor collects several exhaust pressure data from the exhaust port at a preset frequency.
[0008] Optionally, the operating status characteristics include current harmonic distortion characteristics, compressor temperature change characteristics, and discharge pressure change characteristics. Determining the operating status characteristics of the compressor based on the operating status data includes: The ratio between the current harmonic data and the preset basic current data is used as the current harmonic distortion feature. The compressor temperature change characteristics are determined based on several compressor temperature data collected within a preset time period; The exhaust pressure change characteristics are determined based on several exhaust pressure data collected within a preset time period.
[0009] Optionally, an ambient temperature sensor is installed externally on the compressor. The operating environment data includes actual speed data and ambient temperature data. The process of acquiring the compressor's operating environment data and determining feature weighting coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data includes: Obtain the actual rotational speed data of the compressor; The actual rotational speed data is normalized using a preset rotational speed normalization function to obtain the actual rotational speed characteristics; The ambient temperature data is collected by the ambient temperature sensor. The ambient temperature data is normalized using a preset temperature normalization function to obtain ambient temperature characteristics; The first feature weighting coefficient corresponding to the vibration energy entropy feature is determined based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature, and the vibration energy entropy feature; The second feature weighting coefficient corresponding to the current harmonic distortion feature is determined based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature, and the current harmonic distortion feature; The third feature weighting coefficient corresponding to the compressor temperature change feature is determined based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature, and the compressor temperature change feature; The fourth feature weighting coefficient corresponding to the exhaust pressure change feature is determined based on the preset feature sensitivity coefficient, the actual rotational speed feature, the ambient temperature feature, and the exhaust pressure change feature.
[0010] Optionally, determining the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the feature weighting coefficients includes: The compressor health parameters are determined based on the vibration energy entropy characteristics, the current harmonic distortion characteristics, the compressor temperature change characteristics, the exhaust pressure change characteristics, the first feature weighting coefficient, the second feature weighting coefficient, the third feature weighting coefficient, and the second feature weighting coefficient.
[0011] Optionally, determining the compressor health status based on the compressor health parameters includes: The compressor health range corresponding to the compressor health parameters is determined based on the compressor health parameters; The health status of the compressor is determined based on the compressor health range.
[0012] In a second aspect of this application, a device for determining the health status of a compressor is also provided. The compressor includes a vibration sensor and an operating status acquisition component. The device comprises: The data acquisition module is used to acquire vibration status data of the compressor through the vibration sensor and to acquire operating status data of the compressor through the operating status acquisition component. An energy entropy characteristic determination module is used to determine the vibration energy entropy characteristics of the compressor based on the vibration state data. An operation characteristic determination module is used to determine the operation status characteristics of the compressor based on the operation status data; The weighting coefficient determination module is used to acquire the operating environment data of the compressor and determine the feature weighting coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data. The health parameter determination module is used to determine the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the feature weighting coefficients. The health status determination module is used to determine the health status of the compressor based on the compressor health parameters.
[0013] In a third aspect of this application, a vehicle is also provided, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described above.
[0014] In a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the method described above.
[0015] The embodiments of this application have the following advantages: In this embodiment, vibration state data of the compressor is collected by a vibration sensor, and operating state data of the compressor is collected by an operating state acquisition component. The vibration energy entropy characteristics of the compressor are determined based on the vibration state data, and the operating state characteristics of the compressor are determined based on the operating state data. Operating environment data of the compressor is acquired, and feature weighting coefficients are determined based on the vibration energy entropy characteristics, operating state characteristics, and operating environment data. Compressor health parameters are determined based on the vibration energy entropy characteristics, operating state characteristics, and feature weighting coefficients, and the compressor health status is determined based on the compressor health parameters. The compressor health status determination method provided in this embodiment firstly achieves multi-dimensional monitoring of the compressor status by collecting vibration state data and operating state data through a vibration sensor and an operating state acquisition component, respectively. Vibration data reflects the health status of the internal mechanical components of the compressor, while operating state data reflects the overall working performance of the compressor. This multi-source data acquisition method provides a foundation for subsequent comprehensive health assessment. Secondly, calculating the vibration energy entropy characteristics through the vibration state data effectively extracts the complexity and uncertainty information in the vibration signal. The vibration energy entropy characteristics can reflect abnormal states of the internal mechanical components of the compressor, especially minute changes such as early wear or cracks. It can capture early fault signs that are difficult to detect using traditional single-parameter detection. Then, the operating status characteristics reflect the compressor's performance in actual operation. By extracting these characteristics, the compressor's efficiency and performance stability can be comprehensively evaluated. Furthermore, by introducing operating environment data and combining vibration energy entropy characteristics and operating status characteristics, feature weight coefficients are dynamically determined. This allows for dynamic adjustment of the weights of each feature based on the compressor's actual performance under different operating conditions, avoiding the limitations of traditional fixed-weight methods and improving the accuracy and adaptability of health assessment. Finally, by fusing vibration energy entropy characteristics, operating status characteristics, and dynamically adjusted feature weight coefficients, compressor health parameters are calculated to determine the compressor's health status. This multi-source data fusion approach comprehensively reflects the compressor's health status, enabling timely detection of early compressor failures and providing a basis for maintenance decisions, thus improving the comprehensiveness and reliability of compressor health status detection. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.
[0017] Figure 1 This is a flowchart illustrating the steps of a method for determining the health status of a compressor according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a device for determining the health status of a compressor, provided in an embodiment of this application. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been presented in the various embodiments of this application to enable readers to better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and updates based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.
[0019] Traditional vibration monitoring methods have a low detection rate in the early stages of crack propagation in moving discs, typically below 40%. Alarms are only triggered after the crack has propagated to a critical size, making it difficult to detect potential faults in the early stages and unable to provide active protection before fracture, resulting in a significant protection lag problem.
[0020] Secondly, in the high-voltage system of new energy vehicles, electromagnetic interference can overwhelm subtle mechanical fault characteristics, resulting in poor anti-interference capabilities.
[0021] In addition, existing methods do not fully consider different operating conditions, such as dynamic weight changes in frequent start-stop conditions, which leads to large biases in health assessment and a lack of adaptability to complex operating conditions.
[0022] Therefore, this application provides a method, apparatus, vehicle, and medium for determining the health status of a compressor. It can calculate vibration energy entropy characteristics using vibration state data, effectively extracting the complexity and uncertainty information from vibration signals. Vibration energy entropy characteristics can reflect abnormal states of internal mechanical components of the compressor, especially subtle changes such as early wear or cracks. It can capture early fault signs that are difficult to detect using traditional single-parameter detection. Furthermore, by introducing operating environment data and combining vibration energy entropy characteristics and operating state characteristics, the feature weight coefficients are dynamically determined. This allows for dynamic adjustment of the weights of each feature based on the actual performance of the compressor under different operating conditions, avoiding the limitations of traditional fixed-weight methods and improving the accuracy and adaptability of health assessment.
[0023] Reference Figure 1 The diagram shows a flowchart of the steps of a method for determining the health status of a compressor according to an embodiment of this application.
[0024] In this embodiment of the application, a vibration sensor and an operating status acquisition component may be installed in the compressor.
[0025] Specifically, the compressor can refer to the turbocharger in a vehicle, also known as a turbocharger, which is used to improve the engine's intake efficiency and power output. A turbocharger uses the exhaust gases from the engine to drive a turbine, which in turn compresses the air entering the engine, thereby achieving higher combustion efficiency and power performance.
[0026] Vibration sensors can be used to collect real-time vibration data from compressors, reflecting their mechanical operating status. They can be installed near the compressor casing or critical components to obtain the most sensitive vibration signals. Vibration sensors can detect subtle vibration anomalies in the early stages of a fault, enabling early warning.
[0027] The operating status acquisition component is used to collect the compressor's operating parameters, such as pressure, temperature, flow rate, and speed. It can be installed at key locations on the compressor as needed. Changes in operating parameters are related to mechanical faults and can serve as supplementary information to vibration signals. In this embodiment, the operating status acquisition component may include a current harmonic analyzer, a compressor temperature sensor, and a pressure sensor.
[0028] A current harmonic analyzer can refer to a device used to measure and analyze current harmonics in an electrical system. It can detect nonlinear components (i.e., harmonics) in current and provide information such as the amplitude, frequency, and distortion rate of the harmonics, helping to identify anomalies or potential faults in compressor-related electrical systems. In this embodiment, the current harmonic analyzer can be used as one of the components for acquiring operational status.
[0029] A compressor temperature sensor refers to a sensor used to monitor the temperature of critical components of a compressor. It can be installed at locations such as the lubricating oil outlet, exhaust port, and motor windings to collect temperature data in real time. This data is used to assess the compressor's heat dissipation performance, operating status, and whether overheating or other abnormalities exist. In this embodiment, the compressor temperature sensor can be used as one of the components for acquiring operating status data.
[0030] A pressure sensor can refer to a sensor used to measure the pressure of a gas or liquid. In a compressor system, a pressure sensor can be installed at the exhaust port or in the pipeline to monitor changes in exhaust pressure, helping to assess the compressor's workload, gas handling capacity, and the presence of pressure anomalies. In the embodiments of this application, the pressure sensor can be used as one of the components for acquiring operating status.
[0031] The method may specifically include the following steps: Step 101: Collect vibration status data of the compressor through the vibration sensor, and collect operating status data of the compressor through the operating status acquisition component.
[0032] In this embodiment of the application, vibration status data of the compressor can be collected by a vibration sensor, and operating status data of the compressor can be collected by an operating status acquisition component.
[0033] In practical implementation, vibration sensors are installed at key locations on the compressor to collect real-time vibration data. These sensors capture vibration signals generated during compressor operation, reflecting the compressor's mechanical health, such as the presence of abnormal vibrations, bearing wear, or blade damage. Simultaneously, operational status acquisition components are installed at various key locations on the compressor to collect operational data, reflecting changes in the compressor's efficiency and performance. By combining and analyzing the vibration and operational status data, a comprehensive assessment of the compressor's health can be achieved, enabling early fault warnings and performance optimization.
[0034] In this embodiment, after acquiring vibration state data and operational state data, one or more preprocessing steps can be performed on the vibration state data and operational state data, such as data cleaning, filtering, noise reduction, normalization, missing value imputation, and outlier removal. Furthermore, the acquired vibration state data and operational state data can be synchronously transmitted via an industrial IoT gateway.
[0035] Step 102: Determine the vibration energy entropy characteristics of the compressor based on the vibration state data.
[0036] In this embodiment of the application, the vibration energy entropy characteristics of the compressor can be determined based on vibration state data.
[0037] Vibration energy entropy is an indicator used to characterize the complexity and disorder of vibration signals. By analyzing the energy distribution of the vibration signal, the entropy value can be calculated, reflecting the stability of the compressor's operating state and potential fault characteristics. A higher vibration energy entropy indicates a more uneven energy distribution in the vibration signal, potentially indicating abnormal vibration or a fault; conversely, a lower vibration energy entropy indicates a more uniform energy distribution in the vibration signal, suggesting a relatively stable compressor operating state.
[0038] In practical implementation, vibration signal data can be divided into multiple target signal frequency bands, and the frequency band vibration energy within each target signal frequency band can be calculated. Then, the vibration energy distribution probability of all target signal frequency bands is comprehensively analyzed to form vibration energy entropy characteristics, which are used for subsequent fault diagnosis and condition assessment.
[0039] Step 103: Determine the operating status characteristics of the compressor based on the operating status data.
[0040] In this embodiment, the operating status characteristics of the compressor can be determined based on operating status data. This operating status data may include current harmonic data, compressor temperature data, and discharge pressure data.
[0041] Current harmonic data can be collected using a current harmonic analyzer and can be used to reflect the electrical operating status of the compressor motor. It can include characteristics such as the harmonic distortion rate of the current, helping to identify abnormalities in the motor windings, power quality problems, or electrical system faults.
[0042] Compressor temperature data can be collected by compressor temperature sensors and used to monitor temperature changes in key components of the compressor. This data can include the rate of temperature change and reflects the compressor's heat dissipation performance, operational stability, and the presence of abnormal conditions such as overheating.
[0043] Exhaust pressure data can be acquired through pressure sensors and used to monitor pressure changes at the compressor's exhaust port. This data can include the rate of pressure change, reflecting the compressor's workload, gas handling capacity, and the presence of pressure anomalies.
[0044] Step 104: Obtain the operating environment data of the compressor, and determine the feature weight coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data.
[0045] In this embodiment, compressor operating environment data can be acquired, and feature weighting coefficients can be determined based on vibration energy entropy characteristics, operating state characteristics, and operating environment data. The operating environment data includes data related to the compressor's operating environment and operating conditions, including but not limited to actual speed data and ambient temperature data.
[0046] In multi-feature analysis, the feature weight coefficient refers to the weight value assigned to each feature, representing the importance or contribution of each feature in the overall analysis. The magnitude of the feature weight coefficient reflects the degree of influence of each feature on the final result. In this embodiment, the feature weight coefficients corresponding to the vibration energy entropy feature and the operating state feature can be determined based on the vibration energy entropy feature, the operating state feature, and the operating environment data. Step 105: Determine the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the characteristic weighting coefficients.
[0047] In this embodiment, compressor health parameters can be determined based on vibration energy entropy characteristics, operating state characteristics, and feature weighting coefficients. The compressor health parameters can be a comprehensive health index (HI), which is a parameter used to assess the overall health status of the compressor.
[0048] In practical implementation, the health of a compressor can be quantified by integrating multiple key features (such as vibration energy entropy features, operating status features, and operating environment data) and their corresponding weighting coefficients. The compressor health parameter can be a numerical indicator, ranging from 0 to 1 (or 0% to 100%).
[0049] Step 106: Determine the compressor health status based on the compressor health parameters.
[0050] In this embodiment of the application, the health status of the compressor can be determined based on the compressor health parameters.
[0051] In practical implementation, since the compressor health parameter can be a numerical indicator, ranging from 0 to 1 (or 0% to 100%), representing the change in the compressor's health status from healthy to faulty, different compressor health intervals can be defined based on the compressor health parameter, and different compressor health intervals can be mapped to different compressor health states.
[0052] In this embodiment of the application, if the compressor health status indicates that the compressor is at risk or malfunctioning, a risk warning or malfunction alert can be issued.
[0053] In this embodiment, firstly, vibration state data and operating state data are collected by vibration sensors and operating state acquisition components, respectively, enabling multi-dimensional monitoring of the compressor's status. Vibration data reflects the health status of the compressor's internal mechanical components, while operating state data reflects the overall performance of the compressor. This multi-source data acquisition method provides a foundation for subsequent comprehensive health assessment. Secondly, by calculating vibration energy entropy characteristics from the vibration state data, the complexity and uncertainty information in the vibration signal can be effectively extracted. Vibration energy entropy characteristics can reflect abnormal states of the compressor's internal mechanical components, especially subtle changes such as early wear or cracks. It can capture early fault signs that are difficult to detect with traditional single-parameter detection. Then, the operating state characteristics reflect the compressor's performance in actual operation. By extracting operating state characteristics, the compressor's working efficiency and performance stability can be comprehensively evaluated. Furthermore, by introducing operating environment data and combining vibration energy entropy characteristics and operating state characteristics, the feature weight coefficients are dynamically determined. This allows for dynamic adjustment of the weights of each feature based on the compressor's actual performance under different operating conditions, avoiding the limitations of traditional fixed-weight methods and improving the accuracy and adaptability of health assessment. Finally, by fusing vibration energy entropy characteristics, operating state characteristics, and dynamically adjusted feature weighting coefficients, compressor health parameters are calculated to determine the compressor's health status. This multi-source data fusion approach comprehensively reflects the compressor's health status, enabling timely detection of early compressor failures and providing a basis for maintenance decisions, thus improving the comprehensiveness and reliability of compressor health status detection.
[0054] In one alternative embodiment of this application, the vibration state data includes vibration signal data.
[0055] Vibration signal data refers to time-domain signals reflecting the vibration state of mechanical equipment, collected by sensors during operation. Vibration signal data is represented in time-series form and includes vibration information generated during equipment operation. In this embodiment, vibration energy entropy characteristics can be determined using vibration signal data.
[0056] Step 102 includes the following steps: S11, The vibration signal data is decomposed using a preset filter function to obtain several vibration signal frequency bands; S12, select several target signal frequency bands corresponding to the target vibration frequency band from several vibration signal frequency bands according to the preset target vibration frequency band; S13, for any of the target signal frequency bands, determine the frequency band vibration energy corresponding to the target signal frequency band; S14, determine the vibration energy distribution probability based on the vibration energy of the frequency bands corresponding to the target signal frequency bands respectively; S15, determine the vibration energy entropy characteristic based on the vibration energy distribution probability.
[0057] In this embodiment, a preset filter function can be used to decompose the vibration signal data to obtain several vibration signal frequency bands. The preset filter function can be a Daubechies 10 (db10) wavelet function. A vibration signal frequency band refers to the signal components within a specific frequency range after the vibration signal has been decomposed or divided in the frequency domain.
[0058] Vibration sensors, when capturing high-frequency signals of wear on the moving disc in a compressor, can collect vibration changes caused by localized geometric deformation (such as thinning of the scroll wall) or surface microcracks due to wear. Furthermore, the decrease in stiffness at the wear point of the moving disc in the compressor will excite the inherent high-frequency resonance of the moving disc structure (which can be primarily collected in the 8-12kHz range). Vibration sensors can monitor these high-frequency vibration signals in real time and convert them into electrical signals. By analyzing the frequency, amplitude, and energy distribution of these signals, the wear condition of the moving disc can be identified, thus providing important information for equipment fault diagnosis and maintenance.
[0059] In a practical implementation, the number of decomposition layers can be set to 4, and the vibration signal data can be decomposed to finally obtain 16 vibration signal frequency bands.
[0060] In this embodiment, several target signal frequency bands corresponding to the target vibration frequency band can be selected from several vibration signal frequency bands according to the preset target vibration frequency band. The target signal frequency band refers to a specific frequency band signal selected based on the preset target vibration frequency band after the vibration signal has undergone frequency domain decomposition.
[0061] In practical implementation, the preset target vibration frequency band can be the 8-12kHz band. Several vibration signal frequency bands corresponding to the 8-12kHz band are selected from 16 vibration signal frequency bands.
[0062] In this embodiment of the application, for any target signal frequency band, the frequency band vibration energy corresponding to the target signal frequency band is determined. The frequency band vibration energy refers to the energy magnitude of the vibration signal within any target signal frequency band, which can be represented by the sum of the squares of the signal amplitudes within that frequency band.
[0063] In this embodiment, the vibration energy distribution probability is determined based on the vibration energy of several frequency bands corresponding to several target signal frequency bands. The vibration energy distribution probability refers to the proportion of vibration energy in each target signal frequency band within the total vibration energy. It describes the distribution of vibration energy across different frequency bands.
[0064] In this embodiment, the vibration energy entropy characteristics can be determined based on the probability distribution of vibration energy. Specifically, the vibration energy entropy characteristics can be determined using the Shannon entropy formula.
[0065] This application decomposes vibration signal data using a preset filter function to obtain several vibration signal frequency bands, and then selects target signal frequency bands based on the target vibration frequency band. By calculating the vibration energy and its distribution probability of each target signal frequency band, the vibration energy entropy characteristics are further determined using the Shannon entropy formula. This effectively extracts the energy distribution characteristics of vibration signals in different frequency bands, reflecting the vibration state and potential faults of the compressor. It has higher frequency band decomposition accuracy and can more accurately capture the fault characteristic frequencies of the compressor, providing more reliable technical support for compressor fault diagnosis and condition monitoring.
[0066] In one optional embodiment of this application, the compressor includes a power supply circuit and an exhaust port. The operating status acquisition component may include a current harmonic analyzer, a compressor temperature sensor and a pressure sensor. The operating status data may include current harmonic data, compressor temperature data and exhaust pressure data.
[0067] The power supply circuit is a crucial part of the compressor's normal operation, providing the necessary electrical energy. In this embodiment, current harmonic data can be collected from the power supply circuit.
[0068] The exhaust port is an important component of the compressor, used to discharge compressed gas. In this embodiment, exhaust pressure data can be collected from the exhaust port.
[0069] Step 101 includes the following steps: S21, The vibration status data of the compressor is collected by the vibration sensor; S22, The current harmonic data of the power supply circuit is collected by the current harmonic analyzer; S23, the compressor temperature sensor collects several compressor temperature data at a preset frequency; S24, the pressure sensor collects several exhaust pressure data from the exhaust port at a preset frequency.
[0070] In this embodiment, vibration state data of the compressor can be collected by a vibration sensor, current harmonic data of the power supply circuit can be collected by a current harmonic analyzer, several compressor temperature data of the compressor can be collected by a compressor temperature sensor at a preset frequency, and several exhaust pressure data of the exhaust port can be collected by a pressure sensor at a preset frequency.
[0071] Among them, the current harmonic analyzer can obtain current harmonic data that indirectly reflects changes in the resistance of the moving plate by detecting changes in motor current. Wear of the compressor's moving plate increases friction, which in turn changes the motor load torque, causing the current to increase with the load. After acquiring the motor current signal, the current harmonic analyzer performs spectral analysis using Fast Fourier Transform (FFT) to extract the amplitude characteristics of the current harmonics in the data. Changes in harmonic amplitude can reflect changes in the moving plate resistance. This effectively monitors the wear condition of the moving plate and provides a diagnostic basis for abnormal motor loads.
[0072] This application utilizes a multi-sensor collaborative acquisition system to collect compressor operating status data, including vibration data, current harmonic data, compressor temperature data, and discharge pressure data. The current harmonic analyzer detects changes in motor current and extracts the amplitude characteristics of current harmonics using FFT spectrum analysis, indirectly reflecting changes in the moving plate resistance and effectively monitoring the moving plate wear condition. Vibration sensors capture high-frequency vibration signals caused by moving plate wear, further revealing the wear status. Compressor temperature and pressure sensors collect temperature and discharge pressure data respectively, comprehensively monitoring the compressor's operating status. This multi-dimensional and high-precision acquisition of compressor status provides a reliable basis for compressor maintenance and optimized operation, significantly improving compressor operating efficiency and safety.
[0073] In one optional embodiment of this application, the operating state characteristics include current harmonic distortion characteristics, compressor temperature change characteristics, and exhaust pressure change characteristics.
[0074] Step 103 includes the following steps: S31, the ratio between the current harmonic data and the preset basic current data is used as the current harmonic distortion feature. S32, determine the compressor temperature change characteristics based on several compressor temperature data collected during a preset time period; S33, determine the exhaust pressure change characteristics based on several exhaust pressure data collected during a preset time period.
[0075] In this embodiment, the ratio between current harmonic data and preset fundamental current data can be used as the current harmonic distortion characteristic. The preset fundamental current data can be a fundamental current with an effective value of 50 / 60Hz. The current harmonic distortion characteristic can be the current harmonic distortion rate, which is the percentage of the ratio between the current harmonic data and the preset fundamental current data.
[0076] In this embodiment, the compressor temperature change characteristics can be determined based on several compressor temperature data collected within a preset time period. The compressor temperature change characteristics can be the compressor temperature change rate calculated from the compressor temperature data collected within the preset time period. The specific duration of the preset time period can be set according to actual conditions. Specifically, the compressor temperature change rate can be calculated based on the compressor temperature data collected at the start of the preset time period, the compressor temperature data collected at the end of the preset time period, and the preset time period itself, using the following formula:
[0077] in, This indicates the rate of change of compressor temperature. This indicates the compressor temperature data collected at the end of the preset time period. This indicates the compressor temperature data collected at the start of a preset time period. Indicates the preset duration.
[0078] In this embodiment, exhaust pressure change characteristics can be determined based on several exhaust pressure data collected over a preset time period. The exhaust pressure change characteristics can be the exhaust pressure change rate calculated from the exhaust pressure data collected over the preset time period. Specifically, the exhaust pressure change rate can be calculated based on exhaust pressure data collected at the start of the preset time period, exhaust pressure data collected at the end of the preset time period, and the preset time period itself, using the following formula:
[0079] in, Indicates the rate of change of exhaust pressure. This indicates the exhaust pressure data collected at the end of the preset time period. This indicates the exhaust pressure data collected at the start of a preset time period. Indicates the preset duration.
[0080] This application calculates the ratio of current harmonic data to preset baseline current data to obtain current harmonic distortion characteristics, which can effectively reflect changes in motor load and wear status of the moving plate. Simultaneously, based on compressor temperature and discharge pressure data collected within a preset time period, the temperature change rate and discharge pressure change rate are calculated respectively, serving as compressor temperature change characteristics and discharge pressure change characteristics. These characteristics comprehensively reflect changes in the compressor's operating status.
[0081] In one optional embodiment of this application, an ambient temperature sensor is provided outside the compressor, and the operating environment data includes actual speed data and ambient temperature data.
[0082] Step 104 includes the following steps: S41, Obtain the actual speed data of the compressor; S42, the actual rotational speed data is normalized using a preset rotational speed normalization function to obtain the actual rotational speed characteristics; S43, collect the ambient temperature data through the ambient temperature sensor; S44, The ambient temperature data is normalized using a preset temperature normalization function to obtain ambient temperature characteristics; S45, determine the first feature weighting coefficient corresponding to the vibration energy entropy feature based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature, and the vibration energy entropy feature; S46, determine the second feature weighting coefficient corresponding to the current harmonic distortion feature based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature and the current harmonic distortion feature; S47, determine the third feature weighting coefficient corresponding to the compressor temperature change feature based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature and the compressor temperature change feature; S48, determine the fourth feature weighting coefficient corresponding to the exhaust pressure change feature based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature, and the exhaust pressure change feature.
[0083] In this embodiment of the application, the actual speed data of the compressor can be obtained, and then the actual speed data can be normalized using a preset speed normalization function to obtain the actual speed characteristics.
[0084] In practical implementation, the following formula can be used to determine the actual rotational speed characteristics:
[0085] in, Indicates the actual rotational speed characteristics. This represents the actual rotational speed data. This indicates the minimum operating speed. Indicates the maximum operating speed. This indicates that the rotational speed affects the gain, and you can adjust it according to your actual needs.
[0086] In this embodiment, ambient temperature data can be collected by an ambient temperature sensor, and the ambient temperature data can be normalized using a preset temperature normalization function to obtain ambient temperature characteristics.
[0087] In practical implementation, the following formula can be used to determine the environmental temperature characteristics:
[0088] in, Indicates the characteristics of ambient temperature. This represents ambient temperature data. This represents the minimum ambient temperature. This indicates the maximum ambient temperature. This indicates that temperature affects the gain, and you can adjust the settings according to your specific needs.
[0089] In this embodiment, a first feature weighting coefficient corresponding to the vibration energy entropy feature can be determined based on a preset feature sensitivity coefficient, actual rotational speed feature, ambient temperature feature, and vibration energy entropy feature. The first feature weighting coefficient is the feature weighting coefficient of the vibration energy entropy feature. The feature sensitivity coefficient may include a temperature sensitivity coefficient and a rotational speed sensitivity coefficient.
[0090] The second feature weighting coefficient corresponding to the current harmonic distortion feature is determined based on the preset feature sensitivity coefficient, actual rotational speed feature, ambient temperature feature, and current harmonic distortion feature. The first feature weighting coefficient is the feature weighting coefficient of the current harmonic distortion feature.
[0091] The third feature weighting coefficient corresponding to the compressor temperature change feature is determined based on the preset feature sensitivity coefficient, actual speed feature, ambient temperature feature, and compressor temperature change feature. The first feature weighting coefficient is the feature weighting coefficient of the compressor temperature change feature.
[0092] The fourth feature weighting coefficient corresponding to the exhaust pressure change feature is determined based on the preset feature sensitivity coefficient, actual speed feature, ambient temperature feature, and exhaust pressure change feature. The first feature weighting coefficient is the feature weighting coefficient for the exhaust pressure change feature.
[0093] In practical implementation, the feature weight coefficients can be determined according to the following formula.
[0094]
[0095] in, This represents the feature weight coefficient corresponding to feature i. This represents the temperature sensitivity coefficient corresponding to feature i. This represents the rotational speed sensitivity coefficient corresponding to feature i. Indicates the actual rotational speed characteristics. The value represents the ambient temperature characteristic, and n represents the number of characteristic dimensions. In this embodiment, n can be set to 4.
[0096] In a practical implementation, the temperature sensitivity coefficient and rotation speed sensitivity coefficient can be obtained by referring to the characteristic sensitivity coefficient table. Referring to Table 1, a characteristic sensitivity coefficient table of an embodiment of this application is shown.
[0097] Table 1 Feature Sensitivity Coefficient Table
[0098] This application introduces ambient temperature sensor data and actual rotational speed data, combined with a normalization method, to obtain actual rotational speed characteristics and ambient temperature characteristics, respectively. Furthermore, it incorporates vibration energy entropy characteristics, current harmonic distortion characteristics, compressor temperature change characteristics, and exhaust pressure change characteristics to calculate the weighting coefficients of each characteristic. This allows for dynamic adjustment of characteristic weights based on changes in ambient temperature and rotational speed, enabling multi-dimensional and refined analysis of the compressor's operating status.
[0099] In one optional embodiment of this application, step 105 includes the following steps: S51, the compressor health parameters are determined based on the vibration energy entropy characteristics, the current harmonic distortion characteristics, the compressor temperature change characteristics, the exhaust pressure change characteristics, the first feature weighting coefficient, the second feature weighting coefficient, the third feature weighting coefficient, and the second feature weighting coefficient.
[0100] In this embodiment, the compressor health parameters can be determined based on the vibration energy entropy characteristics, current harmonic distortion characteristics, compressor temperature change characteristics, exhaust pressure change characteristics, first feature weighting coefficient, third feature weighting coefficient, and second feature weighting coefficient.
[0101] In practical implementation, the compressor health parameters can be determined according to the following formula.
[0102]
[0103] in, This indicates the compressor's health status parameter. This represents the feature weight coefficient corresponding to feature i. This represents the feature value corresponding to feature i. This represents the lower bound of the eigenvalue corresponding to feature i. The upper limit of the feature value corresponding to feature i is represented by n, which represents the number of feature dimensions. In this embodiment, n can be set to 4.
[0104] In a practical implementation, the lower bound of the feature value can be the 5th percentile of the health status of feature i in the historical data corresponding to feature i.
[0105] In a practical implementation, the upper limit of the feature value can be the 95th percentile of the fault state corresponding to feature i in the historical data corresponding to feature i.
[0106] This application calculates compressor health parameters by comprehensively considering vibration energy entropy characteristics, current harmonic distortion characteristics, compressor temperature change characteristics, and discharge pressure change characteristics, combined with the weighting coefficients of each characteristic. It can determine the upper and lower limits of characteristic values based on historical data for each characteristic, thereby achieving a quantitative assessment of the compressor's health status and providing a scientific basis for compressor fault early warning and maintenance decisions.
[0107] In one optional embodiment of this application, step 106 includes the following steps: S61, determine the compressor health range corresponding to the compressor health parameter based on the compressor health parameter; S62, determine the health status of the compressor based on the compressor health range.
[0108] In this embodiment of the application, the compressor health range corresponding to the compressor health parameters can be determined according to the compressor health parameters, and then the compressor health status can be determined according to the compressor health range.
[0109] In the specific implementation, the compressor health status corresponding to a pre-defined compressor health status range can be obtained. When the compressor health status parameter is within the compressor health status range [0.8, 1.0], the current compressor is considered to be in a healthy state. When the compressor health status parameter is within the compressor health status range [0.5, 0.8), the current compressor is considered to be in a slightly degraded state. When the compressor health status parameter is within the compressor health status range [0, 0.5), the current compressor is considered to be in a severely degraded state.
[0110] This application calculates compressor health parameters, divides these parameters into different ranges, and determines the compressor's health status based on these ranges. This provides a clear and quantitative reflection of the compressor's operating status, offering a straightforward health assessment that facilitates timely detection of potential faults and the implementation of appropriate measures. This effectively extends the compressor's lifespan and improves its operational stability and reliability.
[0111] Reference Figure 2 The diagram shows a structural schematic of a compressor health status determination device according to an embodiment of this application. The compressor is equipped with a vibration sensor and an operating status acquisition component. The device includes: The data acquisition module 201 is used to acquire vibration status data of the compressor through the vibration sensor and to acquire operating status data of the compressor through the operating status acquisition component. The energy entropy characteristic determination module 202 is used to determine the vibration energy entropy characteristics of the compressor based on the vibration state data. The operating characteristic determination module 203 is used to determine the operating status characteristics of the compressor based on the operating status data; The weight coefficient determination module 204 is used to acquire the operating environment data of the compressor and determine the feature weight coefficients based on the vibration energy entropy characteristics, the operating state characteristics and the operating environment data. The health parameter determination module 205 is used to determine the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the feature weighting coefficients. The health status determination module 206 is used to determine the health status of the compressor based on the compressor health parameters.
[0112] In one optional embodiment of this application, the vibration state data includes vibration signal data, and the energy entropy feature determination module 202 includes: The signal decomposition submodule is used to decompose the vibration signal data using a preset filter function to obtain several vibration signal frequency bands; The target frequency band determination submodule is used to select several target signal frequency bands corresponding to the target vibration frequency band from several vibration signal frequency bands according to the preset target vibration frequency band; The vibration energy determination submodule is used to determine the frequency band vibration energy corresponding to any of the target signal frequency bands. The energy distribution determination submodule is used to determine the vibration energy distribution probability based on the vibration energy of the frequency bands corresponding to the target signal frequency bands respectively. The vibration energy entropy determination submodule is used to determine the vibration energy entropy characteristics based on the vibration energy distribution probability.
[0113] In one optional embodiment of this application, the compressor includes a power supply circuit and an exhaust port; the operating status acquisition component includes a current harmonic analyzer, a compressor temperature sensor, and a pressure sensor; the operating status data includes current harmonic data, compressor temperature data, and exhaust pressure data; and the data acquisition module 201 includes: A current harmonic acquisition submodule is used to acquire the current harmonic data of the power supply circuit through the current harmonic analyzer. The compressor temperature acquisition submodule is used to acquire several compressor temperature data of the compressor at a preset frequency through the compressor temperature sensor; The exhaust pressure acquisition submodule is used to acquire several exhaust pressure data from the exhaust port at a preset frequency using the pressure sensor.
[0114] In one optional embodiment of this application, the operating state characteristics include current harmonic distortion characteristics, compressor temperature change characteristics, and discharge pressure change characteristics. The operating characteristic determination module 203 includes: The current characteristic determination submodule is used to take the ratio between the current harmonic data and the preset basic current data as the current harmonic distortion characteristic. The temperature characteristic determination submodule is used to determine the temperature change characteristics of the compressor based on several compressor temperature data collected within a preset time period. The pressure characteristic determination submodule is used to determine the exhaust pressure change characteristics based on several exhaust pressure data collected within a preset time period.
[0115] In one optional embodiment of this application, an ambient temperature sensor is installed outside the compressor, and the operating environment data includes actual speed data and ambient temperature data. The weighting coefficient determination module 204 includes: The speed acquisition submodule is used to acquire the actual speed data of the compressor; The rotational speed characteristic determination submodule is used to normalize the actual rotational speed data using a preset rotational speed normalization function to obtain the actual rotational speed characteristics; An ambient temperature acquisition submodule is used to collect ambient temperature data through the ambient temperature sensor. The temperature feature determination submodule is used to normalize the ambient temperature data using a preset temperature normalization function to obtain the ambient temperature features. The first weight determination submodule is used to determine the first feature weight coefficient corresponding to the vibration energy entropy feature based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature and the vibration energy entropy feature; The second weight determination submodule is used to determine the second feature weight coefficient corresponding to the current harmonic distortion feature based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature and the current harmonic distortion feature; The third weight determination submodule is used to determine the third feature weight coefficient corresponding to the compressor temperature change feature based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature and the compressor temperature change feature; The fourth weight determination submodule is used to determine the fourth feature weight coefficient corresponding to the exhaust pressure change feature based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature, and the exhaust pressure change feature.
[0116] In one optional embodiment of this application, the health parameter determination module 205 includes: The health parameter determination submodule is used to determine the compressor health parameters based on the vibration energy entropy characteristics, the current harmonic distortion characteristics, the compressor temperature change characteristics, the exhaust pressure change characteristics, the first feature weighting coefficient, the second feature weighting coefficient, the third feature weighting coefficient, and the second feature weighting coefficient.
[0117] In one optional embodiment of this application, the health status determination module 206 includes: The health range determination submodule is used to determine the compressor health range corresponding to the compressor health parameters based on the compressor health parameters. The compressor health status determination submodule is used to determine the compressor health status based on the compressor health range.
[0118] As the apparatus embodiment is basically similar to the method embodiment, it is described in a relatively simple manner. For relevant details, please refer to the description of the method embodiment.
[0119] One embodiment of this application also provides a vehicle that may include a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described above.
[0120] An embodiment of this application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the method described above.
[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.
[0122] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0123] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0124] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0127] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other modifications and updates to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all modifications and updates falling within the scope of the embodiments of the present application.
[0128] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the aforementioned element.
[0129] The above provides a detailed description of the method, apparatus, vehicle, and medium for determining the health status of a compressor. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for determining the health status of a compressor, characterized in that, The method includes installing a vibration sensor and an operating status acquisition component in the compressor. The vibration state data of the compressor is collected by the vibration sensor, and the operating state data of the compressor is collected by the operating state acquisition component. The vibration energy entropy characteristics of the compressor are determined based on the vibration state data; The operating status characteristics of the compressor are determined based on the operating status data; Obtain the operating environment data of the compressor, and determine the feature weight coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data; The compressor health parameters are determined based on the vibration energy entropy characteristics, the operating state characteristics, and the characteristic weighting coefficients. The compressor's health status is determined based on the compressor health parameters.
2. The method according to claim 1, characterized in that, The vibration state data includes vibration signal data, and determining the vibration energy entropy characteristics of the compressor based on the vibration state data includes: The vibration signal data is decomposed using a preset filter function to obtain several vibration signal frequency bands; Based on a preset target vibration frequency band, select several target signal frequency bands corresponding to the target vibration frequency band from several vibration signal frequency bands; For any of the target signal frequency bands, determine the frequency band vibration energy corresponding to the target signal frequency band; The vibration energy distribution probability is determined based on the vibration energy of the frequency bands corresponding to the target signal frequency bands. The vibration energy entropy characteristic is determined based on the vibration energy distribution probability.
3. The method according to claim 1, characterized in that, The compressor includes a power supply circuit and an exhaust port. The operating status acquisition component includes a current harmonic analyzer, a compressor temperature sensor, and a pressure sensor. The operating status data includes current harmonic data, compressor temperature data, and exhaust pressure data. The acquisition of the compressor's operating status data through the operating status acquisition component includes: The current harmonic data of the power supply circuit are collected by the current harmonic analyzer. The compressor temperature sensor collects several compressor temperature data at a preset frequency. The pressure sensor collects several exhaust pressure data from the exhaust port at a preset frequency.
4. The method according to claim 3, characterized in that, The operating status characteristics include current harmonic distortion characteristics, compressor temperature change characteristics, and discharge pressure change characteristics. Determining the operating status characteristics of the compressor based on the operating status data includes: The ratio between the current harmonic data and the preset basic current data is used as the current harmonic distortion feature. The compressor temperature change characteristics are determined based on several compressor temperature data collected within a preset time period; The exhaust pressure change characteristics are determined based on several exhaust pressure data collected within a preset time period.
5. The method according to claim 4, characterized in that, An ambient temperature sensor is installed externally on the compressor. The operating environment data includes actual speed data and ambient temperature data. The process of acquiring the compressor's operating environment data and determining feature weighting coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data includes: Obtain the actual rotational speed data of the compressor; The actual rotational speed data is normalized using a preset rotational speed normalization function to obtain the actual rotational speed characteristics; The ambient temperature data is collected by the ambient temperature sensor. The ambient temperature data is normalized using a preset temperature normalization function to obtain ambient temperature characteristics; The first feature weighting coefficient corresponding to the vibration energy entropy feature is determined based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature, and the vibration energy entropy feature; The second feature weighting coefficient corresponding to the current harmonic distortion feature is determined based on the preset feature sensitivity coefficient, the actual rotation speed feature, the ambient temperature feature, and the current harmonic distortion feature; The third feature weighting coefficient corresponding to the compressor temperature change feature is determined based on the preset feature sensitivity coefficient, the actual speed feature, the ambient temperature feature, and the compressor temperature change feature; The fourth feature weighting coefficient corresponding to the exhaust pressure change feature is determined based on the preset feature sensitivity coefficient, the actual rotational speed feature, the ambient temperature feature, and the exhaust pressure change feature.
6. The method according to claim 5, characterized in that, The step of determining the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the characteristic weighting coefficients includes: The compressor health parameters are determined based on the vibration energy entropy characteristics, the current harmonic distortion characteristics, the compressor temperature change characteristics, the exhaust pressure change characteristics, the first feature weighting coefficient, the second feature weighting coefficient, the third feature weighting coefficient, and the second feature weighting coefficient.
7. The method according to claim 1, characterized in that, Determining the compressor health status based on the compressor health parameters includes: The compressor health range corresponding to the compressor health parameters is determined based on the compressor health parameters; The health status of the compressor is determined based on the compressor health range.
8. A device for determining the health status of a compressor, characterized in that, The compressor is equipped with a vibration sensor and an operating status acquisition component. The device includes: The data acquisition module is used to acquire vibration status data of the compressor through the vibration sensor and to acquire operating status data of the compressor through the operating status acquisition component. An energy entropy characteristic determination module is used to determine the vibration energy entropy characteristics of the compressor based on the vibration state data. An operation characteristic determination module is used to determine the operation status characteristics of the compressor based on the operation status data; The weighting coefficient determination module is used to acquire the operating environment data of the compressor and determine the feature weighting coefficients based on the vibration energy entropy characteristics, the operating state characteristics, and the operating environment data. The health parameter determination module is used to determine the compressor health parameters based on the vibration energy entropy characteristics, the operating state characteristics, and the feature weighting coefficients. The health status determination module is used to determine the health status of the compressor based on the compressor health parameters.
9. A vehicle, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1-7.