A method and system for monitoring the operating state of a hydraulic valve

Abstract

The present invention relates to the field of data processing, and particularly relates to a method and system for monitoring the operating state of a hydraulic valve. The method includes: sorting the flow rate data of the hydraulic valve collected within a set time in chronological order to obtain a data sequence; calculating the noise influence coefficient of each flow rate in the data sequence; obtaining the adjustment weight of each flow rate according to the noise influence coefficient; using the weight corresponding to each flow rate to filter and denoise the data sequence to obtain an optimized data sequence, and monitoring the operating state of the hydraulic valve based on the optimized data sequence. The present invention filters the data sequence by using an improved filtering algorithm, improves the accuracy and efficiency of filtering, and monitors the operating state of the hydraulic valve based on the optimized data sequence. Since the data is more accurate and reliable, it can more accurately reflect the actual working conditions of the hydraulic valve.

Description

Technical Field

[0001] The present invention relates to the field of data processing. More specifically, the present invention relates to a method and system for monitoring the operating state of a hydraulic valve. Background Art

[0002] A hydraulic valve is a device that can control the movement and working state of an actuator by adjusting or changing parameters such as the flow direction, pressure, and flow rate of hydraulic fluid in a hydraulic system. The working principle of a hydraulic valve is basically to use the pressure difference or flow rate difference to control the inlet and outlet of hydraulic fluid to the valve core, thereby achieving the control of the hydraulic system. When hydraulic fluid acts on the valve core, the connection state of the hydraulic passage is changed by the movement or rotation of the valve core to achieve the control of the hydraulic system under different working conditions.

[0003] The prior art, such as the patent application document with the publication number CN116040519A, discloses a control system and control method for a lifting platform. The control method of the lifting platform includes: according to the position detection instruction, the control box controls the position detector to perform timing detection on the stroke of the oil cylinder; according to the flow detection instruction, the control box controls the hydraulic oil flow detector to perform timing detection on the hydraulic oil flow of the oil cylinder; the control box receives the position information and flow information feedback by the position detector and the hydraulic oil flow detector, the control box arranges this information in order based on the sampling timing, binds the information of the same sampling timing as detection data, and further calculates the detection data using PID control. According to the calculation result, the control box controls the action of the oil cylinder by adjusting the opening of the servo valve.

[0004] The above patent application document realizes the control of the oil cylinder action through position and flow detection, combined with control operations such as PID. However, during the process of collecting flow data, due to external interference factors such as noise interference and real-time data collection, the obtained flow data is inaccurate, and the servo valve cannot be accurately controlled based on the flow data. Summary of the Invention

[0005] To solve the above technical problem that the obtained flow data is inaccurate due to external interference factors and the servo valve cannot be accurately controlled, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for monitoring the operating state of a hydraulic valve includes:

[0007] Sorting the flow data of the hydraulic valve collected within a set time in chronological order to obtain a data sequence;

[0008] Calculating the noise influence coefficient of each flow in the data sequence; the obtaining process of the noise influence coefficient includes:

[0009] Taking any one of the flow data in the data sequence as the target point, a local range is constructed with the target point as the center; the target point and its two adjacent flow data are combined to form the data pattern of the target point;

[0010] Calculate the consistency between the data pattern of the target point and the data patterns of other flow data within the local range of the target point;

[0011] Taking the negative exponential power of the mean of the consistency between the data pattern of the target point and the data patterns of other flow data within the local range of the target point as the noise influence coefficient of the target point;

[0012] Obtain the adjustment weights of each flow according to the noise influence coefficient; the adjustment weight is:

[0013] ; where, is the adjustment weight of the th flow data, is the corrected noise influence coefficient of the th flow data, is the original weight used when filtering this flow data;

[0014] Filter and denoise the data sequence using the weights corresponding to each flow to obtain an optimized data sequence, and monitor the operating state of the hydraulic valve based on the optimized data sequence.

[0015] Since the actually collected flow data often contains various noises, which will mask the real flow change situation, the present invention determines the adjustment weight by calculating the noise influence coefficient of the flow data, which can effectively filter the original flow data. After filtering and denoising using the adjustment weight, the real flow signal can be highlighted, so as to more accurately reflect the actual flow situation of the hydraulic valve; monitoring the operating state of the hydraulic valve based on the optimized data sequence makes the monitoring result more accurate and reliable; among them, by constructing the local range of the flow data and capturing the basic change trend of the flow data, calculating the consistency between the data pattern corresponding to the flow data and the data patterns corresponding to other data within the local range, identifying which data points have a similar change trend to the target point, so as to distinguish normal data from noise data; converting the consistency measure into a noise influence coefficient helps to more easily identify and process noise in subsequent analysis.

[0016] Preferably, the noise influence coefficient is further corrected to obtain a corrected noise influence coefficient, and the corrected noise influence coefficient satisfies the relational expression:

[0017] ; where, is the corrected noise influence coefficient of the th target point, is the noise influence coefficient of the th target point, is the mean value of all the noise influence coefficients within the local range of the th target point.

[0018] The corrected noise influence coefficient not only considers the noise influence of a single target point, but also takes into account the difference in noise influence between this target point and other points within its local range, which helps to enhance the contrast between local data points, making individual noise outliers more prominent in areas with similar or close noise levels.

[0019] Preferably, the filtering adopts iterative SG filtering.

[0020] While smoothing the data, the SG filter can retain the details and peaks of the data to the greatest extent. Iterative SG filtering can better maintain the original shape and features of the signal while removing noise through multiple smoothing processes.

[0021] Preferably, the fitting influence index in the iterative SG filtering satisfies the relational expression:

[0022] ; where is the fitting error index between the new flow data sequence generated in the th iteration and the said data sequence, is the th flow data value in the new flow data sequence generated in the th iteration, is the th flow data value in the said data sequence, is the adjustment weight of the th flow data, is the total number of flow data in the said data sequence.

[0023] By calculating the fitting error and combining weight adjustment, the generated data sequence can be continuously optimized to make it closer to the original data, thereby improving the accuracy and reliability of data processing and analysis.

[0024] Preferably, when the fitting error index corresponding to the th iteration is greater than or equal to the fitting error index corresponding to the previous iteration of the th iteration, and the fitting error index corresponding to the th iteration is less than or equal to the fitting error index corresponding to the next iteration of the th iteration, stop the iteration.

[0025] Preferably, the adjustment weight also satisfies the relational expression:

[0026] ; In the formula, For the The adjustment weight of traffic data is For the The noise influence coefficient after the flow data is corrected is: The original weight used when filtering the traffic data, and are all constants.

[0027] In a second aspect, a hydraulic valve operation status monitoring system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned hydraulic valve operation status monitoring method is implemented.

[0028] The beneficial effects of the present invention are:

[0029] The noise influence coefficient is calculated according to the changing characteristics of the hydraulic valve flow data, and the weight of each data point in the iterative SG filtering is obtained accordingly, thereby improving the deficiency of the traditional method that only relies on the distance between the data points before and after fitting to determine the weight, avoiding over-fitting of normally changing data, and retaining more detailed information. This not only prevents the loss of details caused by filtering, but also avoids the residual noise caused by under-filtering, thereby improving the accuracy and efficiency of filtering.

[0030] The operating status of the hydraulic valve is monitored based on the optimized data sequence. Since the data is more accurate and reliable, it can more accurately reflect the actual working conditions of the hydraulic valve, help to timely discover potential faults or abnormalities, improve the safety and reliability of the equipment, and thus improve the operating efficiency of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0032] Figure 1 It is a method flow chart of steps S1 to S4 in a method for monitoring the operating status of a hydraulic valve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0034] The application scenario of the present invention is: using improved iterative SG filtering to denoise the collected flow data.

[0035] The embodiments of the present invention disclose a method for monitoring the operating state of a hydraulic valve. Refer to Figure 1 , including steps S1 - S4, specifically as follows:

[0036] S1: Sort the flow data of the hydraulic valve collected within a set time in chronological order to obtain a data sequence.

[0037] A large amount of oil stain particulate debris may accumulate in the hydraulic valve, and these debris may cause problems such as abnormal operation and jamming of the filter element. Among them, the flow characteristics directly affect the overall performance of the hydraulic system. For example, if the flow is unstable or the deviation is too large, it may lead to a decrease in the working efficiency of the hydraulic system; inaccurate flow control may also affect the accuracy and reliability of the equipment. Therefore, real-time monitoring of the flow data of the hydraulic valve can quickly and accurately reflect its operating state.

[0038] Specifically, a flow sensor is used to obtain the flow data of the hydraulic valve, and data is collected once per second and sorted in chronological order to form a data sequence.

[0039] During the data collection process, due to possible failures or aging of the flow sensor, etc., data cannot be normally collected at some moments, or the collected data is unstable, which results in some missing values in the finally obtained flow data sequence. In order to make the data sequence complete and facilitate subsequent analysis and processing, the linear interpolation method is used to fill these missing values.

[0040] S2: Calculate the noise influence coefficient of each flow in the data sequence.

[0041] Iterative SG filtering is based on ordinary SG filtering and continuously optimizes the fitting result through an iterative method. During the iterative process, the fitting influence index is used to measure the contribution degree of each data point to the fitting result. The traditional calculation method adjusts the weight according to the distance change of the data point before and after fitting. The data points with a closer distance are considered to have insufficient fitting degree, so a larger weight will be given for the next iteration. However, this method may wrongly regard normal data changes as noise that requires more fitting, resulting in continuous iterative fitting of the algorithm, and thus may lose the detailed information in the data, affecting the final filtering effect. To solve this problem, in the embodiments of the present invention, the noise influence coefficient is calculated by analyzing the change characteristics of the hydraulic valve flow data, that is, it means that before determining which data points need more fitting, the change characteristics of the data should be analyzed first to distinguish which are normal data fluctuations and which are real noises. In this way, it is possible to more accurately determine which data points need more smoothing processing and which data points should retain their original detailed information.

[0042] Specifically, in data analysis, a single data point may be greatly affected by random fluctuations and cannot accurately reflect the overall trend. Therefore, it is necessary to examine the data change situation within a local range.

[0043] When the flow sensor collects the hydraulic valve flow data, cavitation noise may occur. This noise is manifested as a mutation point of the flow data point within its local range. Therefore, the noise influence coefficient can be calculated through the mutability of the flow data point. The stronger the mutability, the greater the noise influence coefficient.

[0044] First, take any one flow in the data sequence in S1 above as the target point. Centering on the target point, expand to its left and right sides respectively until the difference between the added flow data and the flow mean value within the current local range exceeds the set threshold (the threshold is set to 1 in the embodiments of the present invention). If the difference between a flow data to be added and the flow mean value within the local range is less than 1, then add it to the local range; otherwise, stop adding. This process continues until no more data points can be added, and at this time, the local range is considered to have converged.

[0045] According to the above method of constructing the local range, the local ranges corresponding to all other flow data in the data sequence can be obtained in the same way.

[0046] Then, define a data pattern for each flow data, that is, a triple composed of the current flow data and its adjacent two flow data. Furthermore, calculate the consistency between the data pattern corresponding to the target point and the data pattern corresponding to any flow data within the local range of the target point, that is, the satisfaction relationship is:

[0047]

[0048] In the formula, is the consistency between the data pattern of the -th target point and the data pattern of the -th flow data within the local range of the -th target point. is the -th element in the data pattern of the -th flow data within the local range of the -th target point. is the -th element in the data pattern of the -th target point. is the -th element in the data pattern of the -th flow data within the local range of the -th target point. is the -th element in the data pattern of the -th target point. is the exponential function with the natural constant e as the base.

[0049] Among them, represents the average of the absolute differences of all corresponding elements between the two data patterns; represents the deviation degree between the difference of the -th element of the data pattern and and the average difference of all elements; represents the average level of the deviation of the position differences of all elements from the average difference.

[0050] Formula measures the change consistency between data patterns, rather than the direct consistency of data values. Specifically, by comparing the difference between the change amount of each element and the average change amount of all elements, the consistency of the change trends of the two data patterns is evaluated. If this value is small, it indicates that the change trends of the two data patterns are relatively consistent.

[0051] Furthermore, the negative exponential power of the mean of the consistency between the data pattern of the target point and the data patterns of other flow data within the local range of the target point is used as the noise influence coefficient of the target point, that is, the relational expression is satisfied as:

[0052]

[0053] In the formula, is the noise influence coefficient of the -th target point, is the -th total number of flow data within the local range of the For the data pattern of the th target point and the th flow data within the local range of the th target point, the consistency is

[0054] an exponential function with the natural constant e as the base.

[0055] It should be noted that during the operation of the hydraulic valve, due to flow regulation or system pressure changes, the flow data may fluctuate. To more accurately identify the noise impact, the noise impact coefficient needs to be corrected. By calculating the difference between the average value of the noise impact coefficients within the local range and the noise impact coefficient of the current flow data point, the persistence of the flow data point can be evaluated. The larger the difference, the weaker the persistence and the higher the possibility of noise impact. Therefore, the noise impact coefficient needs to be increased.

[0056] Then the corrected noise impact coefficient satisfies the following relationship:

[0057]

[0058] In the formula, is the corrected noise impact coefficient of the th target point, is the noise impact coefficient of the th target point, is the average value of all noise impact coefficients within the local range of the th target point.

[0059] represents the difference between the average value of the noise impact coefficients within the local range of the th target point and the noise impact coefficient of the th target point, characterizing the persistence of this target point.

[0060] According to the above S2, the method for calculating the noise impact coefficient of the th target point can be used to calculate the noise impact coefficients of all other flow data in the data sequence in the same way.

[0061] S3: Obtain the adjustment weights of each flow according to the noise impact coefficient.

[0062] Determine the weight of each flow data in the data sequence when performing iterative SG filtering according to the noise influence coefficient calculated in the above S2. If the noise influence coefficient of the flow data is larger, a greater weight is given when calculating the fitting influence index to increase the fitting influence index, so as to better denoise; conversely, if the noise influence coefficient is smaller, a smaller weight is given to reduce the fitting influence index and prevent overfitting of normal data.

[0063] Then the adjusted weights of each flow data satisfy the relational expression as follows:

[0064]

[0065] In the formula, is the adjusted weight of the th flow data, is the noise influence coefficient after correction of the th flow data, is the original weight used when filtering this flow data.

[0066] In another embodiment, the adjusted weights of each flow data also satisfy the relational expression as follows:

[0067]

[0068] In the formula, is the adjusted weight of the th flow data, is the noise influence coefficient after correction of the th flow data, is the original weight used when filtering this flow data, and are both constants used to adjust the ratio and offset.

[0069] S4: Use the weights corresponding to each flow to filter and denoise the data sequence, obtain the optimized data sequence, and monitor the operating state of the hydraulic valve based on the optimized data sequence.

[0070] In the embodiment of the present invention, iterative SG filtering is adopted, and the general steps are as follows:

[0071] First step, initialize parameters.

[0072] First, use the SG (Savitzky-Golay) filtering algorithm to process the data sequence in the above step S1. SG filtering is a digital filtering method that smooths data by fitting a polynomial. In the embodiment of the present invention, the window width is 2×m + 1 (set m = 4), so the window width is 9 data points; the polynomial order is r (set r = 3), that is, a cubic polynomial is used for fitting, and then a new flow data sequence is generated.

[0073] Second step, calculate the weights.

[0074] According to the operations in the above step S2 - step S3, calculate the adjustment weights of each flow data in the data sequence in the above step S1.

[0075] Third step, iterative fitting.

[0076] Use the SG filter set in the first step above to refit the new flow data sequence, and generate a new flow data sequence again. This is an iterative process, and a new flow data sequence is generated each time.

[0077] Fourth step, calculate the fitting error index between the generated new flow data sequence and the original data sequence.

[0078] The fitting error index between the generated new flow data sequence and the original data sequence satisfies the relationship:

[0079]

[0080] In the formula, is the fitting error index between the new flow data sequence generated in the th iteration and the data sequence, is the th value of the th flow data in the new flow data sequence generated in the th iteration, is the th value of the th flow data in the data sequence, is the adjustment weight of the

[0081] th flow data,

[0082] When it indicates that the current fitting effect is already good enough, and continuing the iteration will not significantly improve the fitting quality, so the iteration can be terminated.

[0083] When the iteration termination condition is reached, it can be considered that the accurate filtering and denoising of the hydraulic flow data have been completed. At this time, relatively accurate hydraulic flow data can be obtained, and the operating state of the hydraulic valve can be monitored based on these data.

[0084] An embodiment of the present invention also discloses a monitoring system for the operating state of a hydraulic valve, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating state of the hydraulic valve according to the present invention is implemented.

[0085] The system also includes other components well-known to those skilled in the art, such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be described in detail here.

[0086] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0087] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three, or more, unless otherwise specifically defined.

[0088] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for monitoring the operating status of a hydraulic valve, characterized in that: include: Arrange the flow data of the hydraulic valve collected within a set time in chronological order to obtain a data sequence; Calculate the noise impact coefficient of each flow in the data sequence; The process of obtaining the noise influence coefficient includes: Take any flow data in the data sequence as the target point, and expand to the left and right sides with the target point as the center, until the difference between the added flow data and the mean of the flow data in the current local range exceeds the set threshold; the target point and its two adjacent flow data form the data mode of the target point; Calculate the consistency between the data pattern of the target point and the data patterns of other flow data in the local area of ​​the target point; The negative exponential power of the mean of the consistency between the data pattern of the target point and the data patterns of other flow data in the local range of the target point is used as the noise influence coefficient of the target point; The noise influence coefficient is corrected to obtain a corrected noise influence coefficient, and the corrected noise influence coefficient satisfies the relationship: ; In the formula, For the The corrected noise influence coefficient of each target point is: For the The noise influence coefficient of each target point is For the The mean value of all noise influence coefficients within the local range of the target point; The adjustment weight of each flow is obtained according to the noise influence coefficient; the adjustment weight is: ; In the formula, For the The adjustment weight of traffic data is For the The noise influence coefficient after the flow data is corrected is: The original weight used when filtering the traffic data; The data sequence is filtered and denoised using the weights corresponding to the respective flow rates to obtain an optimized data sequence, and the operating status of the hydraulic valve is monitored based on the optimized data sequence.

2. A method for monitoring the operating status of a hydraulic valve according to claim 1, characterized in that: The filtering adopts iterative SG filtering.

3. A method for monitoring the operating status of a hydraulic valve according to claim 2, characterized in that: The fitting influence index in the iterative SG filtering satisfies the relationship: ; In the formula, For the The fitting error index between the new flow data series generated by the iteration and the data series, For the The new traffic data sequence generated by the iteration flow data values, is the first flow data values, For the The adjustment weight of traffic data is is the total number of flow data in the data sequence.

4. A method for monitoring the operating status of a hydraulic valve according to claim 3, characterized in that: When The corresponding fitting error index is greater than or equal to The fitting error index corresponding to the previous iteration, and The corresponding fitting error index is less than or equal to The iteration stops when the fitting error index corresponding to the next iteration is .

5. A method for monitoring the operating status of a hydraulic valve according to claim 1, characterized in that: The adjustment weight also satisfies the relationship: ; In the formula, For the The adjustment weight of traffic data is For the The noise influence coefficient after the flow data is corrected is: The original weight used when filtering the traffic data, and are all constants.

6. A hydraulic valve operation status monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the method for monitoring the operating status of a hydraulic valve according to any one of claims 1 to 5 is implemented.

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