Hydraulic hoist performance monitoring and analyzing method and system based on time-frequency analysis

Abstract

The invention discloses a hydraulic hoist performance monitoring and analysis method and system based on time-frequency analysis, and relates to the technical field of hydraulic system state monitoring and fault diagnosis, and the method comprises the steps: carrying out the data collection of performance parameters through an intelligent sensor network under different operation states of a hydraulic hoist, and adjusting the data collection frequency and type; processing the collected data through a time-frequency analysis technology to reduce information loss and separate inherent modes in the signals, and extracting time-frequency features; performing potential performance degradation trend analysis on the time-frequency characteristics according to a failure development rule and failure physics; and identifying a fault diffusion trend according to a result of the potential performance degradation trend analysis and a monitoring result of temperature and vibration. Through the intelligent sensor network and the time-frequency analysis technology, early recognition and early warning of potential faults of the hydraulic hoist are achieved, the accuracy and comprehensiveness of fault diagnosis are improved, the fault response speed is optimized, and stable operation of the system is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic system status monitoring and fault diagnosis, and in particular to a hydraulic hoist performance monitoring and analysis method and system based on time-frequency analysis. Background Art

[0002] In the field of hydraulic gate hoist performance monitoring and analysis, the development of related technologies has undergone a transformation from simple mechanical monitoring to complex sensor networks and data processing. Early monitoring methods mainly relied on manual observation and simple mechanical indicators, which often failed to capture subtle changes in the hydraulic system in real time, resulting in insufficient accuracy and timeliness of fault diagnosis. With the development of sensor technology and data processing technology, modern hydraulic gate hoist monitoring systems have begun to use intelligent sensor networks for data collection and process data through various algorithms in order to improve the efficiency and accuracy of fault detection.

[0003] However, the existing technology still has many shortcomings. First, the existing monitoring system lacks flexibility in data collection and fails to dynamically adjust the frequency and type of data collection according to the real-time operating status of the hydraulic gate hoist, which may result in missing important information during critical periods or excessive data collection during non-critical periods. Secondly, there is serious information loss during data processing, and traditional time-frequency analysis techniques are difficult to effectively separate the inherent patterns in the signal, thus affecting the quality of time-frequency feature extraction. Furthermore, the existing technology lacks an effective fault prediction mechanism, making it difficult to identify and analyze the potential performance degradation trend of the hydraulic gate hoist at an early stage. In particular, in terms of correlation analysis of multiple performance parameters, the existing technology fails to effectively identify the fault diffusion trend, which to a certain extent limits the efficiency of fault prevention and maintenance.

[0004] The hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis proposed in the present invention addresses the deficiencies of the above-mentioned prior art and, through intelligent sensor networks, dynamic data acquisition strategies, improved time-frequency analysis technology and fault prediction technology, achieves real-time and accurate monitoring of the hydraulic gate hoist performance and early prediction of potential faults, thereby significantly improving the safety and reliability of the hydraulic gate hoist operation and achieving the technical effect of optimizing the maintenance of the hydraulic system and extending its service life. Summary of the invention

[0005] In view of the above-mentioned existing problems, the present invention aims to solve the problems in the prior art of untimely monitoring, inaccurate diagnosis and difficult prediction of hydraulic gate hoists during operation. Through the combination of intelligent sensor networks, time-frequency analysis technology and fault prediction technology, real-time monitoring of the performance of the hydraulic gate hoist, accurate fault diagnosis and early prediction of potential faults are achieved, thereby significantly improving the safety and reliability of the operation of the hydraulic gate hoist.

[0006] In order to solve the above technical problems, a hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis is proposed, including:

[0007] Under different operating conditions of the hydraulic gate hoist, data on performance parameters are collected through an intelligent sensor network, and the frequency and type of data collection are adjusted according to the real-time operating status; the collected data are processed through time-frequency analysis technology to reduce information loss in the analysis process, separate the inherent patterns in the signal, and extract time-frequency features; according to the failure development law and failure physics fault prediction technology, the time-frequency features are analyzed for potential performance degradation trends; based on the results of the potential performance degradation trend analysis combined with the temperature and vibration monitoring results, the correlation between multiple performance parameters of the hydraulic gate hoist is analyzed to identify the fault diffusion trend.

[0008] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in the present invention, the data collection of the performance parameters includes real-time monitoring of key components of the hydraulic gate hoist through an intelligent sensor network to generate multi-level sensor data;

[0009] The multi-level sensor data includes hydraulic oil temperature, oil condition, hydraulic cylinder displacement and oil pump pressure, and various types of data are fused to form a performance parameter data set;

[0010] According to the real-time operating status, including normal operation, full load, and light load, the normal operating pressure range and rated load value are set, and it is stipulated that the rated load value less than 50% is light load, and the rated load value greater than 80% is full load. Dynamic adjustment is made based on the status. When the status is normal operation and the load is ≥80%, the data acquisition frequency is set to high frequency, that is, more than 10Hz; when the status is light load and the load is <50%, the data acquisition frequency is set to low frequency, that is, 1Hz; when an abnormal status is detected, it is immediately adjusted to high frequency and the sensor type is increased;

[0011] When an abnormality is detected in the pressure sensor data, it is automatically marked as a fault state, the collection frequency is increased to high frequency, and all monitoring types are enabled.

[0012] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in the present invention, the time-frequency analysis technology includes: using an improved short-time Fourier transform in combination with a Hilbert-Huang transform to denoise the collected data, using an improved short-time Fourier transform in combination with an adaptive window to calculate the distribution of the signal in time and frequency, and combining the obtained improved short-time Fourier transform results with multi-resolution wavelet analysis to enhance the time-frequency distribution and reduce information loss during the analysis process.

[0013] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in the present invention, the extraction of time-frequency features includes extracting the inherent mode in the time-frequency domain through wavelet analysis. In the analysis, an improved wavelet transform is applied, and a bandpass filter is used to separate the intrinsic modes and enhance the characteristics of the monitoring signal. k (t) is the kth intrinsic mode, indicating a specific mode at time t; f low and f high The upper and lower limits of the frequency range for extracting the inherent mode; after separating the inherent mode, the time-frequency characteristics of the energy density of the inherent mode are calculated, and the time-frequency characteristics of the instantaneous frequency are extracted by combining the envelope analysis method. All the obtained time-frequency characteristics are sent to the trend analysis module for analysis of potential performance degradation trends.

[0014] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in the present invention, the potential performance degradation trend analysis includes monitoring the early weak faults of each key component of the hydraulic gate hoist system based on the failure development law and failure physics, and converting them into a fault feature vector D = [d1(t), d2(t), ..., d n (t)] T , obtain time-frequency features through time-frequency analysis technology, combine all time-frequency features into time-frequency feature space T(f,t), and combine the time-frequency feature space and fault feature vector into fault indication feature Y(t);

[0015] Combining the fault indication features with the loss function, the optimization objective is:

[0016]

[0017] Where L is the loss function, which measures the error between the model prediction and the actual fault state; N is the number of fault features, Y i (t) is the i-th fault indication feature, g(d i (t)) represents the expected result of the i-th fault feature, η is the adjustment parameter for balancing the fitting degree and complexity of the model, and W i is the weight part of the i-th feature.

[0018] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in the present invention, the potential performance degradation trend analysis also includes detecting faults and evaluating fault indication characteristics by optimizing the loss function L(D, Y(t)), calculating the performance trend P(t) and obtaining the corresponding performance evolution by integral calculation:

[0019]

[0020] where, Δt is the audit time window; l(t) is a dynamic adjustment function that feeds back over time and can be adjusted by time feedback; G represents the influence value of external load or environmental factors on the performance of the hydraulic system, h is an activation function that converts the performance index into a value suitable for analysis; e -α(t-τ) is the attenuation factor, simulating the influence of attenuation over time on historical performance values; α is the attenuation coefficient;

[0021] The potential performance degradation trend is judged by the evaluation index Z: when Z < Z th , it indicates that the system is within the normal range and there is no potential performance degradation;

[0022] When Z = Z th , the system shows a preliminary performance degradation trend and full - process monitoring is carried out;

[0023] When Z > Z th , it is confirmed that there is a risk of performance degradation or failure, and maintenance and repair are carried out immediately.

[0024] As a preferred solution of the hydraulic hoist performance monitoring and analysis method based on time - frequency analysis described in the present invention, wherein: the fault diffusion trend includes analyzing the correlation of multiple performance parameters of the hydraulic hoist according to the results of the analysis of the potential performance degradation trend combined with the monitoring results of temperature and vibration. The performance parameters of the hydraulic hoist are quantitatively evaluated through Pearson correlation analysis. When the correlation coefficient R > 0.7, it is regarded as a strong correlation; when the correlation coefficient 0.4 < R ≤ 0.7, it is a medium correlation; when the correlation coefficient R ≤ 0.4, it is a weak correlation; the multiple performance parameters with weak correlation are not considered to identify the fault diffusion trend;

[0025] The temperatures of multiple performance parameters with medium correlation are monitored respectively. When any parameter fails, but the corresponding parameter with medium correlation does not fail, the fault diffusion trend is not identified. When any parameter fails and the corresponding parameter with medium correlation also fails, the two are re - defined as strongly correlated performance parameters to identify the fault diffusion trend;

[0026] The fault diffusion trend of multiple strongly correlated performance parameters is identified. When the average temperature of the multiple performance parameters is monitored to exceed 80 °C, further vibration monitoring is started; when the acceleration exceeds 5 m / s², it is determined as potential fault diffusion. All performance parameters are re - evaluated and compared with historical data. When the temperature continuously rises by more than 2 °C and the vibration acceleration exceeds the set threshold, it enters the fault alarm mode, automatically records all performance parameters for 10 minutes, and at the same time issues an alarm to notify the staff to carry out fault repair and diffusion control.

[0027] Another object of the present invention is to provide a hydraulic gate hoist performance monitoring and analysis system based on time-frequency analysis. The present invention solves the technical problems in the performance monitoring and analysis of the hydraulic gate hoist by real-time collection of performance parameters of key components and dynamic adjustment of data collection strategies, application of time-frequency analysis technology to process signals to reduce information loss and extract key features, and use of fault prediction technology to analyze potential performance degradation trends, thereby achieving accurate monitoring of the operating status of the hydraulic gate hoist and early warning of faults, ensuring the reliability and safety of the system, and guiding maintenance decisions to control the spread of faults and reduce maintenance costs.

[0028] As a preferred solution of the hydraulic gate hoist performance monitoring and analysis system based on time-frequency analysis described in the present invention, it is characterized by comprising a data acquisition and preprocessing module, a time-frequency analysis and feature extraction module, and a fault prediction and performance evaluation module;

[0029] The data acquisition and preprocessing module includes a performance parameter acquisition unit and a data preprocessing unit. The performance parameter acquisition unit monitors the key components of the hydraulic gate hoist in real time through an intelligent sensor network, generates multi-level sensor data and transmits it to the data preprocessing unit, fuses the collected multi-level sensor data to form a performance parameter data set, and adjusts the data acquisition frequency and type according to the real-time operating status;

[0030] The time-frequency analysis and feature extraction module includes a time-frequency analysis unit and a feature extraction unit. The time-frequency analysis unit receives the preprocessed data, applies the time-frequency analysis technology to perform denoising on the data, and calculates the distribution of the signal in time and frequency. The feature extraction unit uses the wavelet analysis technology to extract the inherent mode of the signal in the time-frequency domain and extract the time-frequency features.

[0031] The fault prediction and performance evaluation module includes a fault prediction unit and a performance evaluation unit. The fault prediction unit combines time-frequency features into fault indication features and uses an optimized loss function to detect and evaluate faults. The performance evaluation unit evaluates the performance evolution of the hydraulic gate hoist and determines the potential performance degradation trend based on the fault indication features and potential performance degradation trend analysis results.

[0032] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the hydraulic hoist performance monitoring and analysis method based on time-frequency analysis are implemented.

[0033] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis are implemented.

[0034] Beneficial effects of the present invention: The present invention realizes the flexibility and adaptability of data collection and improves the efficiency and accuracy of data collection by using an intelligent sensor network to collect data on performance parameters under different operating conditions of the hydraulic gate hoist, and adjusting the frequency and type of data collection according to the real-time operating status; then, the collected data is processed by time-frequency analysis technology to reduce information loss, separate the inherent patterns in the signal, and extract time-frequency features, thereby reducing information loss and improving the accuracy of signal analysis; then, according to the failure development law and the fault prediction technology of fault physics, the potential performance degradation trend of the time-frequency features is analyzed, and early identification and early warning of potential faults are realized, which makes preventive maintenance possible; First, combined with the monitoring results of temperature and vibration, the correlation of multiple performance parameters of the hydraulic gate hoist is analyzed, the fault diffusion trend is identified, and the comprehensiveness and accuracy of fault diagnosis are improved; in addition, the identifiability of signal characteristics is enhanced by combining the improved short-time Fourier transform with the Hilbert-Huang transform for denoising, and multi-resolution wavelet analysis to enhance the time-frequency distribution; finally, by optimizing the loss function to detect faults and evaluate the fault indication characteristics, the performance trend is calculated and the corresponding performance evolution is obtained by integral calculation, which realizes the accurate evaluation of the fault state and the prediction of the performance trend, and combines the fault diffusion trend analysis results for fault alarm and diffusion control, which improves the speed and efficiency of fault response and ensures the continuous and stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0036] Figure 1 An overall flow chart of a method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis provided in accordance with an embodiment of the present invention.

[0037] Figure 2 A system solution flow chart of a hydraulic gate hoist performance monitoring and analysis system based on time-frequency analysis provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0041] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0042] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0043] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis, comprising:

[0045] S1: Under different operating conditions of the hydraulic gate hoist, data collection of performance parameters is carried out through the intelligent sensor network, and the frequency and type of data collection are adjusted according to the real-time operating status.

[0046] Furthermore, the key components of the hydraulic gate hoist are monitored in real time through an intelligent sensor network to generate multi-level sensor data;

[0047] The multi-level sensor data includes hydraulic oil temperature, oil condition, hydraulic cylinder displacement and oil pump pressure, and various types of data are fused to form a performance parameter data set;

[0048] According to the real-time operating status, including normal operation, full load, and light load, the normal operating pressure range and rated load value are set, and it is stipulated that the rated load value less than 50% is light load, and the rated load value greater than 80% is full load. Dynamic adjustment is made based on the status. When the status is normal operation and the load is ≥80%, the data acquisition frequency is set to high frequency, that is, more than 10Hz; when the status is light load and the load is <50%, the data acquisition frequency is set to low frequency, that is, 1Hz; when an abnormal status is detected, it is immediately adjusted to high frequency and the sensor type is increased;

[0049] When an abnormality is detected in the pressure sensor data, it is automatically marked as a fault state, the collection frequency is increased to high frequency, and all monitoring types are enabled.

[0050] S2: The collected data is processed through time-frequency analysis technology to reduce information loss during the analysis process, separate the inherent patterns in the signal, and extract time-frequency features.

[0051] Furthermore, the improved short-time Fourier transform is combined with the Hilbert-Huang transform to denoise the collected data to obtain the denoised data x(τ);

[0052] The improved short-time Fourier transform is combined with an adaptive window to calculate the distribution of the signal in time and frequency. Based on the obtained improved short-time Fourier transform results, multi-resolution wavelet analysis is combined to enhance the time-frequency distribution and reduce information loss during the analysis process.

[0053] It should be noted that the intrinsic mode is extracted in the time-frequency domain through wavelet analysis. In the analysis, an improved wavelet transform is applied, and a bandpass filter is used to separate the intrinsic modes and enhance the characteristics of the monitoring signal. k (t) is the kth intrinsic mode, indicating a specific mode at time t; f low and f high are the upper and lower limits of the frequency range for extracting the intrinsic mode; after separating the intrinsic mode, the time-frequency characteristics of the energy density of the intrinsic mode are calculated: Among them, E k (t is the intrinsic mode P k (t) Energy density at time t, w energy (t,τ) is the energy window function, which emphasizes the energy influence of the signal at time t and the time variable τ;

[0054] Combined with the envelope analysis method, the time-frequency characteristics of the instantaneous frequency are extracted:

[0055]

[0056] A(t)=max(|P k (t,τ)|)τ∈[t-δ,t+δ]

[0057] Among them, ω k (t) is the intrinsic mode P k (t) The instantaneous frequency at time t, arg(P k (t)) is the phase of the natural mode, β is the dynamic adjustment coefficient for optimizing the instantaneous frequency calculation, A(t) is the envelope at time t, max(|P k (t,τ)||) represents the maximum value of the intrinsic mode amplitude in the time interval [t-δ,t+δ], δ is a dynamically adjusted time window used to determine the time range used when calculating the envelope;

[0058] All the obtained time-frequency features are sent to the trend analysis module for analysis of potential performance degradation trends.

[0059] S3: Based on the failure development law and failure physics fault prediction technology, the potential performance degradation trend of time-frequency characteristics is analyzed.

[0060] Furthermore, based on the failure development law and failure physics, the early weak faults of the key components of the hydraulic hoist system are monitored and converted into the fault feature vector D = [d1(t), d2(t),…, d n (t)] T , the time-frequency features are obtained through time-frequency analysis technology, all time-frequency features are combined into the time-frequency feature space T(f, t), and the time-frequency feature space and the fault feature vector are combined into the fault indication feature Y(t) = a·T(f, t)+b+∈, where Y(t) is the fault indication feature vector, which represents the overall fault state monitored at time t; a represents the influence of each time-frequency feature on the fault indication feature, b represents the constant deviation of the model, and ∈ represents the random error in data acquisition;

[0061] Combining the fault indication features with the loss function, the optimization objective is:

[0062]

[0063] Where L is the loss function, which measures the error between the model prediction and the actual fault state; N is the number of fault features, Y i (t) is the i-th fault indication feature, g(d i(t)) represents the expected result of the i-th fault feature, η is the adjustment parameter for balancing the fitting degree and complexity of the model, and W i is the weight part of the i-th feature.

[0064] By optimizing the loss function L(D,Y(t)), faults are detected and fault indication features are evaluated. The performance trend P(t) is calculated and the corresponding performance evolution is obtained by integral calculation:

[0065]

[0066] Among them, Δt is the audit time window; l(t) is a dynamic adjustment function that can be adjusted with time feedback; G represents the impact of external load or environmental factors on the performance of the hydraulic system, h is the activation function, which converts the performance index into a value suitable for analysis; e -α(t-τ) is the attenuation factor, which simulates the effect of attenuation over time on the historical performance value; α is the attenuation coefficient;

[0067] The potential performance degradation trend is determined by evaluating the index Z, and the performance trend index P(t) is divided by the maximum value P of the normal performance of the system. max The performance trend index is standardized to be within the range of 0 to 1, and the performance volatility σ(P(t)) is combined with the sensitivity parameter ξ to quantify the instability of the current performance data. The two parts are combined to obtain a comprehensive performance evaluation index Z, which reflects the current health status of the hydraulic gate hoist;

[0068] When Z < Z th When , it indicates that the system is within the normal range and there is no potential performance degradation;

[0069] When Z = Z th When the system shows a preliminary performance degradation trend, it is monitored throughout the process;

[0070] When Z>Z th When performance degradation or failure risk is confirmed, maintenance and repair should be carried out immediately.

[0071] S4: Based on the results of the potential performance degradation trend analysis combined with the temperature and vibration monitoring results, the correlation between multiple performance parameters of the hydraulic gate hoist is analyzed to identify the fault diffusion trend.

[0072] Further, based on the results of the analysis of the potential performance degradation trend in combination with the monitoring results of temperature and vibration, the correlation of multiple performance parameters of the hydraulic hoist is analyzed. The performance parameters of the hydraulic hoist are quantitatively evaluated through Pearson correlation analysis. When the correlation coefficient R > 0.7, it is regarded as a strong correlation; when the correlation coefficient 0.4 < R ≤ 0.7, it is a medium correlation; when the correlation coefficient R ≤ 0.4, it is a weak correlation. The multiple performance parameters with weak correlations are not considered for identifying the trend of fault spread.

[0073] For the multiple performance parameters with medium correlations, monitor their respective temperatures. When a fault occurs in any parameter, but the corresponding parameter with medium correlation does not have a fault, the trend of fault spread is not identified. When a fault occurs in any parameter and the corresponding parameter with medium correlation also has a fault, the two are re-defined as performance parameters with strong correlations for identifying the trend of fault spread.

[0074] For the multiple performance parameters with strong correlations, identify the trend of fault spread. When the average temperature of the multiple performance parameters is monitored to exceed 80 °C, further vibration monitoring is started. When the acceleration exceeds 5 m / s², it is determined as a potential fault spread. All performance parameters are re-evaluated and compared with historical data. When the temperature continuously rises by more than 2 °C and the vibration acceleration exceeds the set threshold, it enters the fault alarm mode, automatically records all performance parameters for 10 minutes, and at the same time issues an alarm to notify the staff to perform fault repair and spread control.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0076] Embodiment 2, the second embodiment of the present invention, which is different from the previous two embodiments in that:

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] Example 3, reference Figure 2 , which is the third embodiment of the present invention, and provides a hydraulic gate hoist performance monitoring and analysis system based on time-frequency analysis, including a data acquisition and preprocessing module 10, a time-frequency analysis and feature extraction module 20, and a fault prediction and performance evaluation module 30;

[0082] The data acquisition and preprocessing module 10 includes a performance parameter acquisition unit 101 and a data preprocessing unit 102. The performance parameter acquisition unit 101 monitors the key components of the hydraulic gate hoist in real time through an intelligent sensor network, generates multi-level sensor data and transmits it to the data preprocessing unit 102, fuses the collected multi-level sensor data to form a performance parameter data set, and adjusts the data acquisition frequency and type according to the real-time operating status;

[0083] The time-frequency analysis and feature extraction module 20 comprises a time-frequency analysis unit 201 and a feature extraction unit 202. The time-frequency analysis unit 201 receives the pre-processed data, applies the time-frequency analysis technology to perform denoising on the data, and calculates the distribution of the signal in time and frequency. The feature extraction unit 202 uses the wavelet analysis technology to extract the inherent mode of the signal in the time-frequency domain and extract the time-frequency features.

[0084] The fault prediction and performance evaluation module 30 includes a fault prediction unit 301 and a performance evaluation unit 302. The fault prediction unit 301 combines time-frequency features into fault indication features and uses an optimized loss function to detect and evaluate faults. The performance evaluation unit 302 evaluates the performance evolution of the hydraulic gate hoist and determines the potential performance degradation trend based on the fault indication features and potential performance degradation trend analysis results.

[0085] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis, characterized in that: Including, Under different operating states of the hydraulic hoist, data collection of performance parameters is carried out through an intelligent sensor network, and the data collection frequency and type are adjusted according to the real-time operating state; The collected data is processed by time-frequency analysis technology to reduce information loss during the analysis process, separate the inherent modes in the signal, and extract time-frequency features; According to the failure development law and the fault prediction technology of failure physics, the time-frequency features are analyzed for the potential performance degradation trend; According to the results of the potential performance degradation trend analysis combined with the monitoring results of temperature and vibration, the correlation of multiple performance parameters of the hydraulic hoist is analyzed to identify the fault diffusion trend.

2. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 1 is characterized in that: The data collection of the performance parameters includes real-time monitoring of the key components of the hydraulic hoist through an intelligent sensor network to generate multi-level sensor data; The multi-level sensor data includes hydraulic oil temperature, oil product state, hydraulic cylinder displacement, and oil pump pressure, and various types of data are fused to form a performance parameter data set; According to the real-time operating state including normal operation, full load, and light load, the normal working pressure range and the rated load value are set, and it is stipulated that less than 50% of the rated load value is light load, and more than 80% of the rated load value is full load. Based on the state, dynamic adjustment is carried out. When the state is normal operation and the load ≥ 80%, the data collection frequency is set to high frequency, that is, more than 10Hz; when the state is light load and the load < 50%, the data collection frequency is set to low frequency, that is, 1Hz; when an abnormal state is detected, it is immediately adjusted to high frequency and the sensor type is increased; When the pressure sensor data detects an abnormality, it is automatically marked as a fault state, the collection frequency is increased to high frequency, and all monitoring types are enabled.

3. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 2 is characterized in that: The time-frequency analysis technology includes using the combination of improved short-time Fourier transform and Hilbert-Huang transform to denoise the collected data, using the improved short-time Fourier transform combined with an adaptive window to calculate the distribution of the signal in time and frequency, and on the obtained result of the improved short-time Fourier transform, combining multi-resolution wavelet analysis to enhance the time-frequency distribution and reduce information loss during the analysis process.

4. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 3 is characterized in that: The extraction of the time-frequency features includes, Extracting intrinsic modes in the time-frequency domain through wavelet analysis In the analysis, an improved wavelet transform is applied, and a bandpass filter is used to separate the intrinsic modes and enhance the characteristics of the monitoring signal. k (t) is the kth intrinsic mode, indicating a specific mode at time t; f low and f high The upper and lower limits of the frequency range for extracting the inherent mode; after separating the inherent mode, the time-frequency characteristics of the energy density of the inherent mode are calculated, and the time-frequency characteristics of the instantaneous frequency are extracted by combining the envelope analysis method. All the obtained time-frequency characteristics are sent to the trend analysis module for analysis of potential performance degradation trends.

5. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 4 is characterized in that: The potential performance degradation trend analysis includes monitoring the early weak faults of each key component of the hydraulic hoist system based on the failure development law and failure physics, and converting them into a fault feature vector D = [d1(t), d2(t), ..., d n (t)] T , obtain time-frequency features through time-frequency analysis technology, combine all time-frequency features into time-frequency feature space T(f,t), and combine the time-frequency feature space and fault feature vector into fault indication feature Y(t); Combining the fault indication feature with the loss function, and the optimization objective is: Where L is the loss function, which measures the error between the model prediction and the actual fault state; N is the number of fault features, Y i (t) is the i-th fault indication feature, g(d i (t)) represents the expected result of the i-th fault feature, η is the adjustment parameter for balancing the fitting degree and complexity of the model, and W i is the weight part of the i-th feature.

6. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 5 is characterized in that: The potential performance degradation trend analysis also includes detecting faults and evaluating the fault indication feature by optimizing the loss function L(D, Y(t)), calculating the performance trend P(t) and obtaining the corresponding performance evolution by integral calculation; Among them, Δt is the audit time window; l(t) is a dynamic adjustment function that can be adjusted with time feedback; G represents the impact of external load or environmental factors on the performance of the hydraulic system, h is the activation function, which converts the performance index into a value suitable for analysis; e -α(t-τ) is the attenuation factor, which simulates the effect of attenuation over time on the historical performance value; α is the attenuation coefficient; Determine the potential performance degradation trend by evaluating the indicator Z: when Z<Z th When , it indicates that the system is within the normal range and there is no potential performance degradation; When Z = Z th When the system shows a preliminary performance degradation trend, it is monitored throughout the process; When Z>Z th When performance degradation or failure risk is confirmed, maintenance and repair should be carried out immediately.

7. The method for monitoring and analyzing the performance of a hydraulic gate hoist based on time-frequency analysis according to claim 6 is characterized in that: The fault diffusion trend includes, according to the results of the potential performance degradation trend analysis combined with the monitoring results of temperature and vibration, analyzing the correlation of multiple performance parameters of the hydraulic hoist. The performance parameters of the hydraulic hoist are quantitatively evaluated by Pearson correlation analysis. When the correlation coefficient R > 0.7, it is regarded as a strong correlation; when the correlation coefficient 0.4 < R ≤ 0.7, it is a medium correlation; when the correlation coefficient R ≤ 0.4, it is a weak correlation; the multi-performance parameters with weak correlation are not considered to identify the fault diffusion trend; Monitor the respective temperatures of multiple moderately correlated performance parameters. When any parameter fails but the corresponding moderately correlated parameter does not, the fault diffusion trend is not identified. When any parameter fails and the corresponding moderately correlated parameter also fails, the two are redefined as strongly correlated performance parameters to identify the fault diffusion trend. The fault diffusion trend is identified by using strongly correlated multiple performance parameters. When the average temperature of multiple performance parameters is monitored to be over 80°C, further vibration monitoring is initiated. When the acceleration exceeds 5 meters per second, it is judged as a potential fault diffusion, and all performance parameters are re-evaluated and compared with historical data. When the temperature rises continuously by more than 2°C and the vibration acceleration exceeds the set threshold, the fault alarm mode is entered, all performance parameters are automatically recorded for 10 minutes, and an alarm is issued to notify the staff to repair the fault and control the diffusion.

8. A system using the hydraulic hoist performance monitoring and analysis method based on time-frequency analysis as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition and preprocessing module, time-frequency analysis and feature extraction module, fault prediction and performance evaluation module; The data acquisition and preprocessing module includes a performance parameter acquisition unit and a data preprocessing unit. The performance parameter acquisition unit monitors the key components of the hydraulic gate hoist in real time through an intelligent sensor network, generates multi-level sensor data and transmits it to the data preprocessing unit, fuses the collected multi-level sensor data to form a performance parameter data set, and adjusts the data acquisition frequency and type according to the real-time operating status; The time-frequency analysis and feature extraction module includes a time-frequency analysis unit and a feature extraction unit. The time-frequency analysis unit receives the preprocessed data, applies the time-frequency analysis technology to perform denoising on the data, and calculates the distribution of the signal in time and frequency. The feature extraction unit uses the wavelet analysis technology to extract the inherent mode of the signal in the time-frequency domain and extract the time-frequency features. The fault prediction and performance evaluation module includes a fault prediction unit and a performance evaluation unit. The fault prediction unit combines time-frequency features into fault indication features and uses an optimized loss function to detect and evaluate faults. The performance evaluation unit evaluates the performance evolution of the hydraulic gate hoist and determines the potential performance degradation trend based on the fault indication features and potential performance degradation trend analysis results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the hydraulic gate hoist performance monitoring and analysis method based on time-frequency analysis described in any one of claims 1 to 7 are implemented.

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