A servo oil rail pressure sensor intelligent diagnosis method and system

Abstract

The application discloses a kind of servo oil rail pressure sensor intelligent diagnosis method and system, include: data preprocessing module: original environmental parameter and performance output data are aligned synchronization, filtering denoising, and extract mean, variance and other statistical characteristics;Multivariate correlation analysis module: calculate pearson correlation coefficient matrix and time lag correlation coefficient, identify key association and core influencing factor;Attribution modeling module: build multiple linear regression model, calculate regression coefficient and significance, quantize each factor contribution rate and sort;Performance trend prediction module: input future working environment profile, predict performance index change based on model, judge whether to exceed tolerance, finally output diagnosis conclusion and prediction report.The unique stress caused by specific application scenario to sensor is fully considered, accurate prediction close to real working condition is realized.

Description

Technical Field

[0001] This invention relates to the field of sensor performance testing and fault diagnosis technology, and in particular to a stability evaluation and intelligent diagnosis system and method for a servo oil rail pressure sensor used in a solenoid valve test bench. It is especially suitable for evaluating the overall performance of sensors operating in complex, dynamic and multi-physics coupled environments, such as the hydraulic servo system of a marine engine. Background Technology

[0002] In solenoid valve test benches, the stability of the servo hydraulic rail pressure sensor is crucial to the accuracy and reliability of the entire system. However, existing sensor stability assessment methods often lack comprehensiveness and cannot fully guarantee the sensor's stable performance under various operating conditions. Especially in environments with extreme temperatures, humidity, vibration, and shock, sensor performance may be affected, leading to system instability. Currently, the evaluation of such sensors largely relies on standardized single-performance tests, such as accuracy tests at constant temperatures and simple vibration tolerance tests. Existing technologies suffer from problems such as limited testing dimensions, static evaluation methods, disconnect between testing and real-world scenarios, and fragmented analysis and diagnosis.

[0003] Existing methods often examine the effects of factors such as temperature, vibration, and humidity on sensors in isolation, lacking simulation and evaluation of the synergistic effects (coupling effects) of multiple environmental stresses. As a result, the true performance of sensors under actual operating conditions (such as the simultaneous presence of high temperature, high humidity, and strong vibration in an engine compartment) is difficult to predict accurately.

[0004] Most existing assessment technologies rely on comparing measurement results with fixed thresholds to provide a binary conclusion of "pass" or "fail". This approach fails to reveal trends in performance degradation, identify early signs of potential failures, or quantify the contribution of different environmental factors to performance degradation, resulting in a lack of foresight and specificity in maintenance decisions.

[0005] Existing test conditions are usually set according to general standards, which fail to fully consider the unique stresses on sensors caused by specific application scenarios (such as high-frequency pressure pulsation and changes in the characteristics of oil media unique to hydraulic servo systems), resulting in discrepancies between laboratory test results and field service performance.

[0006] In existing diagnostic processes, data acquisition, performance evaluation, and fault diagnosis are usually completed in segments by different tools or personnel, resulting in lengthy processes and a lack of a unified knowledge model to transform test data into direct improvement suggestions, leading to low levels of intelligence and automation. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides an intelligent diagnostic method and system for servo oil rail pressure sensors, which fully considers the unique stresses exerted on the sensor by specific application scenarios, thereby achieving accurate predictions that closely approximate real-world operating conditions.

[0008] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows: A method for intelligent diagnosis of a servo hydraulic rail pressure sensor includes the following steps: S1: Appearance inspection and screening: Collect sensor surface images, automatically detect dimensional deviations, structural deformations, surface cracks and dirty / corroded areas, and screen qualified products for subsequent testing; S2: Multi-environmental factor coupled extreme working condition test: Simulate high temperature and high humidity and low temperature and high humidity environments for multiple cycles, and record the zero drift, sensitivity change and recovery characteristics of the sensor under damp heat combined stress; S3: Data Analysis and Root Cause Localization: Synchronously filter and extract features from time-series data, construct a time-series relationship matrix between environmental factors and performance indicators, calculate the Pearson correlation coefficient to quantify the correlation strength; and / or establish a multiple regression model, sort the failure contribution of each environmental factor by the size of the regression coefficient, and achieve intelligent diagnosis.

[0009] In the above technical solution, in step S1, the sensor surface image is acquired, and the image processing algorithm is used to automatically detect dimensional deviations, structural deformation, surface crack parameters, and dirty or rusted areas and their distribution density; sensors with detection results lower than a preset threshold are selected for subsequent testing.

[0010] In the above technical solution, if the detection result in step S1 is higher than the preset threshold, it is considered a serious defect, and it is recommended to terminate the subsequent test.

[0011] In the above technical solution, the surface crack detection parameters in step S1 are set as crack length, width, depth, and cases exceeding a preset threshold.

[0012] In the above technical solution, step S2: simulate extreme combination conditions of high temperature and high humidity and low temperature and high humidity, perform multiple cycles, and record the zero drift, sensitivity change and recovery characteristics of the sensor under the combined stress of humidity and heat.

[0013] In the above technical solution, step S2 includes vibration temperature timing test, post-impact dynamic response test, and long-term pressure and temperature alternation durability test.

[0014] In the above technical solution, step S2 includes vibration temperature timing test: first, a fixed frequency and fixed amplitude vibration test is performed at the reference temperature, and then the sensor is immediately transferred to a high temperature environment to monitor the stability of the sensor output after thermal stress superimposed on mechanical fatigue. After the test, the accuracy is calibrated again to evaluate the sensor performance recovery capability.

[0015] In the above technical solution, step S2 includes a post-impact dynamic response test: after applying a specified number of mechanical impacts, without recalibration, the sensor is immediately connected to the solenoid valve test bench to test the sensor's response speed, overshoot, and steady-state error to high-frequency pressure step or pulsating signals, and to evaluate the potential damage of the impact to the dynamic performance.

[0016] In the above technical solution, step S2 includes a long-term pressure and temperature alternation durability test: on a pressure cycle test bench, pressure cycles and temperature cycles simulating actual oil rail pressure changes are applied simultaneously for a test lasting hundreds of hours; the long-term drift of the output signal is monitored, and special attention is paid to the phase relationship between the temperature change cycle and the output drift trend.

[0017] In the above technical solution, step S3 involves synchronizing, filtering, and extracting features from the time-series data of all test items, based on the environmental factor parameter variable X. i Performance index parameter variable Y j Construct a time series relationship matrix and calculate the relationship between each pair of variables (X). i Y j The Pearson correlation coefficient r) ij Quantify the strength and direction of the linear correlation; and / or, for the key performance indicator Y, use the regression coefficient a i A regression fitting model is constructed by combining multiple environmental factor parameters, and the regression coefficients a are used to determine the model. i The magnitude of the value is used to quantify the contribution of each environmental factor parameter to key performance and to achieve root cause ranking of failures.

[0018] In the above technical solution, the environmental factors in step S3 include: temperature T, humidity H, vibration acceleration V, number of impacts S, and running time t.

[0019] In the above technical solution, the performance index parameters in step S3 include: zero-point output Z, full-scale output F, linearity L, and repeatability R.

[0020] In the above technical solution, the regression fitting model in step S3 is Y = a0 + a1T + a2H + a3V + a4t. Where a0, a1, a2, a3, and a4 are the regression coefficients a. i， i=0, 1, 2, 3, 4, ...

[0021] In the above technical solution, step S3 uses the time lag correlation coefficient between changes in environmental factors and changes in performance indicators to characterize the relationship that "zero drift, sensitivity changes and recovery characteristics appear after a lag of N hours after exposure to high or low temperatures".

[0022] Based on this, the present invention also provides: A multi-dimensional time-series data analysis and performance diagnosis system includes the following modules: Data preprocessing module: Aligns and synchronizes raw environmental parameters and performance output data, filters and denoises them, and extracts statistical features such as mean and variance; Multivariate correlation analysis module: Calculates Pearson correlation coefficient matrix and time-lag correlation coefficient, identifies key correlation pairs and core influencing factors; Attribution modeling module: Constructs a multiple linear regression model, calculates regression coefficients and significance, quantifies and ranks the contribution rate of each factor; Performance trend prediction module: Input a profile of the future working environment, predict changes in performance indicators based on the model, determine whether the tolerance is exceeded, and finally output diagnostic conclusions and prediction reports.

[0023] The entire system's process, from data cleaning to intelligent prediction, forms a closed-loop analysis system.

[0024] Based on hardware implementation, this invention also provides: A smart diagnostic system for a servo oil rail pressure sensor on a solenoid valve test bench, comprising at least: The preliminary appearance and structure screening module is equipped with a high-resolution industrial camera to acquire images of the sensor surface and determine if they exceed a preset threshold. Sensors with detection results below the preset threshold are selected for subsequent testing. The multi-environmental factor coupled testing system includes: a programmable temperature and humidity chamber; a vibration table for fixed-frequency and fixed-amplitude vibration testing; an impact table for applying a specified number of mechanical impacts; a solenoid valve test bench for testing the response speed, overshoot, and steady-state error of high-frequency pressure step or pulsation signals; and a pressure cycle test bench equipped with a pressure standard to simulate actual oil rail pressure change cycles and temperature cycles. The intelligent diagnostic module based on multivariate correlation analysis includes: a data preprocessing module that synchronizes, filters, and extracts features from the time-series data of all test items; an extended Pearson correlation analysis and attribution model for processing data; and a knowledge base that stores typical failure modes and test experience of various sensors.

[0025] The above system can be implemented based on LabVIEW or other industrial software; hardware integration integrates temperature and humidity chambers, vibration tables, impact tables, pressure standards, data acquisition cards and other equipment through a unified measurement and control bus (such as PXI, LXI) to realize a one-click start of a fully automatic testing process.

[0026] Compared with the prior art, the beneficial effects of this invention are: This invention constructs a multi-environmental factor coupling test and interaction effect analysis system, breaking the limitations of traditional single-factor testing. By designing combined test items such as temperature and humidity coupling and vibration-temperature time series, it actively simulates and quantifies the superposition and interaction effects of multi-physical field coupling stress on sensor performance, and the evaluation results are closer to complex real working conditions.

[0027] Furthermore, through multi-factor coupling testing, potential defects and performance shortcomings of sensors in complex environments can be exposed earlier and more accurately, moving the problem discovery stage from "user site" to "factory testing" and improving the reliability of sensor products before they leave the factory.

[0028] This invention constructs an intelligent diagnostic method based on multivariate time-delay correlation analysis and attribution modeling, which goes beyond simple threshold alarms. By calculating Pearson correlation coefficient and time-delay correlation coefficient and establishing a multivariate regression model, it can not only detect anomalies, but also quantitatively identify the key environmental factors that cause the anomalies and their contribution, achieving a leap from "phenomenon detection" to "root cause tracing".

[0029] Based on the diagnostic results of correlation analysis and regression models, it can not only indicate whether the sensor is "qualified," but also clarify "under what operating conditions it may fail" and "which design or process aspects are weak points," providing direct and quantitative decision-making basis for product optimization and user maintenance. This enables predictive maintenance and precise improvement.

[0030] Predictive maintenance recommendations allow for more efficient scheduling of repairs, preventing unexpected downtime and extending the effective lifespan of sensors, thereby reducing overall procurement, maintenance, and operating costs.

[0031] This invention employs a scenario-driven and adaptive testing process design: test parameters (such as temperature range, vibration spectrum, and pressure cycle rate) are directly derived from the actual measured working profile of the target application scenario (such as a marine engine hydraulic servo system). Combined with an adaptive testing strategy engine, the test path can be dynamically optimized based on real-time data, achieving efficient and accurate "targeted" testing.

[0032] This invention realizes an integrated platform for testing, analysis, diagnosis, and decision-making: it constructs a fully automated and intelligent platform from hardware control, data acquisition, intelligent analysis to report generation. Leveraging the powerful capabilities of tools such as LabVIEW, it encapsulates complex multi-device collaboration, big data analysis, and professional diagnostic knowledge into an easy-to-use software system, significantly reducing the professional threshold and operating costs.

[0033] On the other hand, the integrated platform achieves full automation of testing, and adaptive testing strategies reduce unnecessary testing time. The intelligent diagnostic module replaces a large amount of manual data analysis work, allowing a single engineer to manage multiple test tasks simultaneously. Testing efficiency is significantly improved. Attached Figure Description

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the intelligent diagnostic process of the servo oil rail pressure sensor on the electromagnetic valve test bench according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0036] Example 1 like Figure 1 As shown, the multi-factor coupling stability assessment and intelligent diagnosis system for servo oil rail pressure sensors used in solenoid valve test benches according to the present invention includes the following core processes: preliminary screening, multi-factor coupling testing, multi-variable intelligent analysis, comprehensive diagnosis, and report generation. The system includes the following steps: S1: Acquire sensor surface images and use image processing algorithms to automatically detect dimensional deviations, structural deformations, surface crack parameters, and areas of dirt or corrosion and their distribution density; select sensors with detection results below a preset threshold for subsequent testing; S2: Simulate extreme combinations of high temperature and high humidity and low temperature and high humidity respectively, perform multiple cycles, and record the zero drift, sensitivity change and recovery characteristics of the sensor under the combined stress of humidity and heat. S3: Synchronize, filter, and extract features from time-series data from all test items, based on environmental factor parameter variable X. i Performance index parameter variable Y j Construct a time series relationship matrix and calculate the relationship between each pair of variables (X). i Y j The Pearson correlation coefficient r) ij Quantify the strength and direction of the linear correlation; and / or, for the key performance indicator Y, use the regression coefficient a i A regression fitting model is constructed by combining multiple environmental factor parameters, and the regression coefficients a are used to determine the model. i The magnitude of the value is used to quantify the contribution of each environmental factor parameter to key performance and to achieve root cause ranking of failures.

[0037] The following sections describe each module and its workflow: 1. Preliminary appearance and structure screening module and its workflow; High-resolution industrial cameras acquire images of the sensor surface. Image processing algorithms automatically detect dimensional deviations, structural deformations, surface cracks (length, width, and depth exceeding preset thresholds), and areas of dirt / rust and their distribution density. If a serious defect is detected, the system automatically generates a "test termination suggestion" to avoid invalid testing and save resources.

[0038] 2. Multi-environmental factor coupled testing system; This system does not simply list individual tests, but emphasizes the logical connections and coupling simulation between tests: Temperature and humidity coupled cycle test: In a programmable temperature and humidity chamber, extreme combinations of high temperature and high humidity (e.g., 85°C, 85% RH) and low temperature and high humidity are simulated and subjected to multiple cycles. The sensor output is recorded, and its zero-point drift, sensitivity changes, and recovery characteristics under this combined heat and humidity stress are analyzed.

[0039] Vibration-temperature timing test: First, conduct a constant frequency and amplitude vibration test at a reference temperature. Immediately afterward, transfer the sensor to a high-temperature environment and monitor the stability of its output after thermal stress combined with mechanical fatigue. After the test, perform a second accuracy calibration to assess its performance recovery capability.

[0040] Post-impact dynamic response test: After applying a specified number of mechanical impacts, without recalibration, immediately connect to the solenoid valve test bench to test its response speed, overshoot, and steady-state error to high-frequency pressure step or pulsating signals, and evaluate the potential damage of impact to dynamic performance.

[0041] Long-term pressure-temperature alternating durability test: Pressure cycling (simulating actual oil rail pressure changes) and temperature cycling are applied simultaneously on a pressure cycling test bench for hundreds of hours. The long-term drift of the output signal is monitored, with particular attention paid to the phase relationship between the temperature change cycle and the output drift trend.

[0042] 3. Intelligent diagnostic module based on multivariate correlation analysis; 1.1.5 This is the core algorithmic innovation of this invention: Data preprocessing: Time-series data from all test items are synchronized, filtered, and feature extracted (e.g., mean, variance, peak value, rise time, drift, etc.). Constructing an "environment-performance" relationship matrix: Environmental factors (temperature T, humidity H, vibration acceleration V, number of shocks S, running time t) are used as variables X. i The performance indicators (zero-point output Z, full-scale output F, linearity L, repeatability R) are used as variables Y. j .

[0043] Extended Pearson correlation analysis and attribution model: Calculate each pair (X)i Y j The Pearson correlation coefficient r) ij The strength and direction of their linear correlation are quantified. Time-delay correlation analysis is introduced: the time-delay correlation coefficient between changes in environmental factors and changes in performance indicators is calculated to discover hidden relationships such as "zero-point drift occurs only after N hours following high-temperature exposure".

[0044] Establish a multiple linear regression model: For the key performance indicator Y, attempt to fit it using multiple environmental factors, for example: Y = a0 + a1T + a2H + a3V + a4t. Analyze the regression coefficients a... i The magnitude and significance of each environmental factor are used to quantify its contribution to performance and achieve root cause ranking of failures.

[0045] Performance degradation trend prediction: Based on long-term test data, the above regression model or time series prediction algorithm (such as ARIMA) is used to extrapolate the performance changes of the sensor under a specific working profile in the future, so as to achieve predictive maintenance.

[0046] 4. Adaptive Testing Strategy Engine The system has a built-in knowledge base that stores typical failure modes and testing experience for various types of sensors.

[0047] During testing, the system analyzes interim data in real time. If a performance indicator is found to be abnormally deteriorating, the system can dynamically adjust the subsequent test plan. For example, it can increase the intensity or duration of tests for abnormal factors (such as vibrations at a specific frequency), or skip certain tests that are not expected to have abnormalities, in order to focus on troubleshooting and improve testing efficiency.

[0048] 5. Integrated testing and diagnostic platform (based on LabVIEW / other industrial software) Hardware integration: Through a unified measurement and control bus (such as PXI, LXI), devices such as temperature and humidity chambers, vibration tables, impact tables, pressure standards, and data acquisition cards are integrated to achieve one-click start of a fully automatic testing process.

[0049] Software features: Visualized test dashboard: Real-time display of the status of each test item, sensor output curves, and environmental parameter curves.

[0050] Automated report generation: After the test is completed, a comprehensive report is automatically generated, which includes raw data, analytical charts (such as correlation heatmaps), diagnostic conclusions, and improvement suggestions.

[0051] Knowledge base management: Allows users to input the diagnostic results and effective improvement measures of this test into the knowledge base, which can be used to optimize the evaluation strategy for the same type of sensor in the future, forming a closed-loop learning.

[0052] Example 2 This embodiment uses a specific model of servo oil rail pressure sensor (model: Kistler4067A500A2R010V02) as the sample test object to conduct coupling stability evaluation and intelligent diagnosis.

[0053] 1. Test Subjects and Environmental Conditions Setup Test object: Servo oil rail pressure sensor (model Kistler 4067A500A2R010V02), measuring range: 0–50MPa, operating temperature range: -40°C ~ 150°C.

[0054] Target application scenario: Hydraulic servo system for marine engines, operating in environments characterized by high temperature, high humidity, strong vibration, and high-frequency pressure pulsation.

[0055] Test environment parameter settings (based on actual scene measurement profile): Temperature and humidity coupled cycle: 85°C / 85% RH → -20°C / 90% RH, 4 hours per cycle, for a total of 5 cycles.

[0056] Vibration-temperature timing test: First, a vibration test with a frequency of 50 Hz and an amplitude of 2 g was performed for 30 minutes, and then immediately placed in an environment of 100°C for 1 hour.

[0057] Impact test: Half-sine wave impact, peak acceleration 50 g, duration 11 ms, 10 consecutive impacts.

[0058] Long-term pressure-temperature cycling: pressure cycle range 5–50 MPa, temperature cycle range 30–110°C, cycle 2 hours, total test duration 500 hours.

[0059] 2. Test input data Initial data: Sensor factory calibration data (zero point, full scale, linearity, repeatability).

[0060] Environmental parameter time series data: temperature, humidity, vibration acceleration, number of impacts, pressure value, sampling frequency is 10Hz.

[0061] Sensor output timing data: Voltage output (corresponding pressure value), synchronously acquired in each test stage.

[0062] 3. Test Execution Process The system controls equipment such as temperature and humidity chambers, vibration tables, and pressure cycling test benches through the LabVIEW platform, and automatically executes the following steps according to the preset test profile: Initial screening: The industrial camera inspection did not detect any structural deformation or surface cracks, and the system determined that "testing can continue".

[0063] Temperature and humidity coupled cycle test: Record sensor output and calculate the zero shift rate (ZS) and sensitivity change rate (SD) at the end of each cycle.

[0064] Vibration-temperature timing test: Record the output fluctuations during the vibration phase and the high temperature phase, recalibrate after the test, and evaluate the recovery error.

[0065] Dynamic response test after impact: Connect to the solenoid valve test bench, apply a pressure pulsation signal with a frequency of 100 Hz and an amplitude of ±5 MPa, and record the response time and steady-state error.

[0066] Long-term endurance test: Zero drift and full-scale output changes are recorded every 24 hours.

[0067] 4. Test Results and Intelligent Analysis Output 4.1 Data Preprocessing and Feature Extraction The system extracts the following feature sequences: Zero-point output Z(t), full-scale output F(t), linearity error L(t), temperature T(t), humidity H(t), vibration acceleration V(t), pressure P(t) 4.2 Correlation Analysis and Attribution Modeling The system calculates the "environment-performance" correlation matrix, and some key results are shown in Table 1 below: Table 1 Correlation Matrix

[0068] The "Environment-Performance" correlation matrix shown in Table 1 provides a basis for variable selection and time-lag structure priors for the multiple linear regression model. The system first calculates the Pearson correlation coefficients between each environmental variable and the performance index, and selects significantly correlated variables as candidate independent variables for the regression model.

[0069] Furthermore, time-delay correlation analysis determined the time lag in the impact of environmental variable changes on performance, which was used to time-shift and align the independent variable data, thereby more accurately reflecting causal relationships in the regression model.

[0070] The final established multiple linear regression model has coefficients whose magnitude and direction are highly consistent with the aforementioned correlation matrix, and further quantifies the unit influence and statistical significance of each environmental variable, achieving an intelligent diagnostic leap from "qualitative association" to "quantitative attribution".

[0071] Example of a multiple linear regression model (zero-point drift Z-attribution analysis): Z=0.012+0.085T+0.005H+0.008V+0.003t Model display: Temperature contributes the most to zero-point drift (coefficient 0.085), followed by humidity.

[0072] Time lag analysis showed that the effects of high temperature became significant after about 8.2 minutes, while the effects of humidity lagged behind by about 45 minutes.

[0073] 4.3 Adaptive Testing Adjustment During the third cycle of temperature and humidity testing, the system detected an abnormal increase in zero-point drift (30% more than expected). The adaptive engine automatically triggered the "enhanced vibration test" branch, adding a sweep frequency vibration test (20–200 Hz), which further exposed the sensor's resonance sensitivity at a specific frequency (80 Hz).

[0074] 5. Diagnostic Conclusions and Report Output The system automatically generates a diagnostic report, including: Overall score: Stability index 78 / 100 (above average).

[0075] Key findings: The sensor exhibits insufficient zero-point stability under high-temperature and high-humidity coupling environments; optimization of sealing materials or internal compensation circuitry is recommended. Resonance sensitivity exists around 80 Hz; structural reinforcement or avoidance of prolonged operation in this frequency band is suggested.

[0076] Under long-term pressure-temperature alternation, the full-scale output shows a slow degradation trend. It is recommended to strengthen the fatigue life design of the welding points.

[0077] Maintenance recommendations: It is recommended to perform a zero-point calibration every 2000 hours of operation.

[0078] Avoid prolonged use in environments with consistently high temperature and humidity.

[0079] Knowledge base update: The test data and diagnostic conclusions have been entered into the system for future optimization of testing strategies for the same type of sensor.

[0080] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for intelligent diagnosis of a servo hydraulic rail pressure sensor, characterized in that... Includes the following steps: S1: Appearance inspection and screening: Collect sensor surface images, automatically detect dimensional deviations, structural deformations, surface cracks and dirty / corroded areas, and screen qualified products for subsequent testing; S2: Multi-environmental factor coupled extreme working condition test: Simulate high temperature and high humidity and low temperature and high humidity environments for multiple cycles, and record the zero drift, sensitivity change and recovery characteristics of the sensor under damp heat combined stress; S3: Data Analysis and Root Cause Localization: Synchronously filter and extract features from time-series data, construct a time-series relationship matrix between environmental factors and performance indicators, calculate the Pearson correlation coefficient to quantify the correlation strength; and / or establish a multiple regression model, sort the failure contribution of each environmental factor by the size of the regression coefficient, and achieve intelligent diagnosis.

2. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... In step S1, images of the sensor surface are acquired, and image processing algorithms are used to automatically detect dimensional deviations, structural deformations, surface crack parameters, and areas of dirt or corrosion and their distribution density; sensors with detection results below a preset threshold are selected for subsequent testing.

3. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... Step S2: Simulate extreme combinations of high temperature and high humidity, and low temperature and high humidity, respectively, and perform multiple cycles.

4. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... Step S2 includes vibration temperature timing test, post-impact dynamic response test, and long-term pressure and temperature alternation durability test.

5. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... In step S3, the time-series data from all test items are synchronized, filtered, and feature extracted, based on the environmental factor parameter variable X. i Performance index parameter variable Y j Construct a time series relationship matrix and calculate the relationship between each pair of variables (X). i , Y j The Pearson correlation coefficient r) ij Quantify the strength and direction of the linear correlation; and / or, for the key performance indicator Y, use the regression coefficient a i A regression fitting model is constructed by combining multiple environmental factor parameters, and the regression coefficients a are used to determine the model. i The magnitude of the value is used to quantify the contribution of each environmental factor parameter to key performance and to achieve root cause ranking of failures.

6. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... The environmental factors in step S3 include: temperature T, humidity H, vibration acceleration V, number of impacts S, and running time t.

7. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... In step S3, the regression fitting model is Y = a0 + a1T + a2H + a3V + a4t.

8. The intelligent diagnostic method for servo oil rail pressure sensor according to claim 1, characterized in that... In step S3, the time lag correlation coefficient between changes in environmental factors and changes in performance indicators is used to characterize the relationship that "zero drift, and / or sensitivity changes, and / or recovery characteristics occur after a lag of N hours after exposure to high or low temperatures".

9. A multi-dimensional time-series data analysis and performance diagnosis system, characterized in that... Include: Data preprocessing module: Aligns and synchronizes raw environmental parameters and performance output data, filters and denoises them, and extracts statistical features such as mean and variance; Multivariate correlation analysis module: Calculates Pearson correlation coefficient matrix and time-lag correlation coefficient, identifies key correlation pairs and core influencing factors; Attribution modeling module: Constructs a multiple linear regression model, calculates regression coefficients and significance, quantifies and ranks the contribution rate of each factor; Performance trend prediction module: Input a profile of the future working environment, predict changes in performance indicators based on the model, determine whether the tolerance is exceeded, and finally output diagnostic conclusions and prediction reports.

10. A servo oil rail pressure sensor intelligent diagnostic system for a solenoid valve test bench, characterized in that... At least including: The preliminary appearance and structure screening module is equipped with a high-resolution industrial camera to acquire images of the sensor surface and determine if they exceed a preset threshold. Sensors with detection results below the preset threshold are selected for subsequent testing. The multi-environmental factor coupled testing system includes: a programmable temperature and humidity chamber; a vibration table for fixed-frequency and fixed-amplitude vibration testing; an impact table for applying a specified number of mechanical impacts; a solenoid valve test bench for testing the response speed, overshoot, and steady-state error of high-frequency pressure step or pulsation signals; and a pressure cycle test bench equipped with a pressure standard to simulate actual oil rail pressure change cycles and temperature cycles. The intelligent diagnostic module based on multivariate correlation analysis includes: a data preprocessing module that synchronizes, filters, and extracts features from the time-series data of all test items; an extended Pearson correlation analysis and attribution model for processing data; and a knowledge base that stores typical failure modes and test experience of various sensors.

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