Method and system for monitoring operation state of hydraulic valve

Abstract

The invention relates to the field of data processing, in particular to a hydraulic valve operation state monitoring method and system, and the method comprises the steps: sorting the flow data of a hydraulic valve collected within a set time according to a time sequence to obtain a data sequence; calculating a noise influence coefficient of each flow in the data sequence; obtaining an adjustment weight of each flow according to the noise influence coefficient; and filtering and denoising the data sequence by using the weight corresponding to each flow to obtain an optimized data sequence, and monitoring the operation state of the hydraulic valve based on the optimized data sequence. The improved filtering algorithm is used for filtering the data sequence, the filtering accuracy and efficiency are improved, the operation state of the hydraulic valve is monitored based on the optimized data sequence, and due to the fact that data are more accurate and reliable, the actual working condition of the hydraulic valve can be reflected more accurately.

Description

Technical Field

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

[0002] A hydraulic valve is a device that can control the movement and working state of the actuator by adjusting or changing the flow direction, pressure, flow rate and other parameters of the hydraulic fluid in the hydraulic system. The working principle of the hydraulic valve is basically to use the pressure difference or flow rate difference to control the hydraulic fluid in and out of the valve core, thereby realizing the control of the hydraulic system. When the hydraulic fluid is applied to the valve core, the connection state of the hydraulic channel is changed by the movement or rotation of the valve core, realizing the control of the hydraulic system under different working conditions.

[0003] Prior art, such as the patent application document with publication number CN116040519A, discloses a control system and control method for a lifting platform, the control method of the lifting platform comprising: according to the position detection instruction, the control box controls the position detector to perform timing detection on the stroke of the 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 cylinder; the control box receives the position information and flow information fed back by the position detector and the hydraulic oil flow detector, arranges the information in sequence based on the sampling timing, and binds the information with the same sampling timing as detection data, further uses PID control to calculate the detection data, and according to the calculation result, the control box controls the movement of the cylinder by adjusting the opening of the servo valve.

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

[0005] In order to solve the above-mentioned technical problem that the acquired 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 an operating state of a hydraulic valve comprises: 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 impact coefficient includes: Take any flow data in the data sequence as the target point, and construct a local range with the target point as the center; the target point and its two adjacent flow data form the data pattern 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 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.

[0007] Since the actually collected flow data often contains various noises that will mask the actual flow changes, 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, thereby more accurately reflecting the actual flow situation of the hydraulic valve; the operating status of the hydraulic valve is monitored based on the optimized data sequence, so that the monitoring result will be more accurate and reliable; wherein, by constructing the local range of the flow data and capturing the basic change trend of the flow data, the consistency between the data pattern corresponding to the flow data and the data pattern corresponding to other data in the local range is calculated, and which data points have similar change trends to the target points are identified, thereby distinguishing 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.

[0008] Preferably, the noise influence coefficient also includes correcting it 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.

[0009] The corrected noise influence coefficient not only takes into account the noise influence of a single target point, but also takes into account the difference in noise influence between the target point and other points in its local range, which helps to enhance the contrast between local data points, making individual noise anomalies more prominent in areas with similar or close noise levels.

[0010] Preferably, the filtering adopts iterative SG filtering.

[0011] The SG filter can retain the details and peaks of the data to the greatest extent while smoothing the data. The iterative SG filter can better maintain the original shape and characteristics of the signal while removing noise through multiple smoothing processes.

[0012] Preferably, 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.

[0013] By calculating the fitting error and combining it with 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.

[0014] Preferably, 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 .

[0015] Preferably, 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.

[0016] 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.

[0017] The beneficial effects of the present invention are: 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.

[0018] 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

[0019] 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: 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

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] The application scenario of the present invention is: using improved iterative SG filtering to denoise the collected flow data.

[0022] The embodiment of the present invention discloses a method for monitoring the operating status of a hydraulic valve, referring to Figure 1 , including steps S1 to S4, which are specifically as follows: S1: Arrange the flow data of the hydraulic valve collected within a set time in chronological order to obtain a data sequence.

[0023] A large amount of oily and granular debris may be deposited in the hydraulic valve, which may cause problems such as malfunction 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, the working efficiency of the hydraulic system may decrease; 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 status.

[0024] Specifically, a flow sensor is used to obtain flow data of the hydraulic valve, the data is collected once per second, and is sorted in chronological order to form a data sequence.

[0025] In the process of data collection, the flow sensor may malfunction or age, resulting in the inability to collect data normally at some times, or the collected data is unstable, which results in some missing values ​​in the final flow data sequence. In order to make the data sequence complete and facilitate subsequent analysis and processing, linear interpolation is used to fill these missing values.

[0026] S2: Calculate the noise impact coefficient of each flow in the data sequence.

[0027] Iterative SG filtering is based on ordinary SG filtering, and continuously optimizes the fitting results through iteration. In the iterative process, the fitting influence index is used to measure the contribution of each data point to the fitting result. The traditional calculation method adjusts the weight according to the change in the distance of the data points before and after fitting. The data points with closer distance are considered to be insufficiently fitted, so they will be given a larger weight for the next iteration. However, this method may mistakenly regard normal data changes as noise that requires more fitting, resulting in the algorithm continuously iterating fitting, which may lose the detailed information in the data and affect the final filtering effect. In order to solve this problem, in an embodiment of the present invention, the noise influence coefficient is calculated by analyzing the change characteristics of the hydraulic valve flow data, which means that before deciding which data points need more fitting, the change characteristics of the data must first be analyzed to distinguish which are normal data fluctuations and which are real noise. In this way, it is possible to more accurately determine which data points need more smoothing and which data points should retain their original detailed information.

[0028] 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 changes within a local range.

[0029] When the flow sensor collects 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 impact coefficient can be calculated by the mutation of the flow data point. The stronger the mutation, the greater the noise impact coefficient.

[0030] First, take any flow in the data sequence in S1 as the target point, and expand to the left and right sides of the target point as the center, until the difference between the added flow data and the flow mean in the current local range exceeds the set threshold (the threshold is set to 1 in the embodiment of the present invention). If the difference between a flow data to be added and the flow mean in the local range is less than 1, it will be added to the local range; otherwise, stop adding. This process continues until there are no more data points to be added, at which time the local range is considered to have converged.

[0031] According to the above method of constructing the local range, the local range corresponding to all other traffic data in the data sequence can be obtained in the same way.

[0032] Then, a data pattern is defined for each flow data, that is, a triple consisting of the current flow data and its two adjacent flow data. Then, the consistency between the data pattern corresponding to the target point and the data pattern corresponding to any flow data in the local range of the target point is calculated, that is, the relationship is satisfied:

[0033] In the formula, For the The data pattern of the target point is the same as that of the The local range of the target point The consistency between the data patterns of the traffic data For the The local range of the target point In the data mode of the traffic data elements, For the In the data pattern of the target point elements, For the The local range of the target point In the data mode of the traffic data elements, For the In the data pattern of the target point elements, It is an exponential function with the natural constant e as its base.

[0034] in, The part represents the average of the absolute differences of all corresponding elements between two data patterns; Partial representation data mode and In the The degree of deviation between the difference of an element and the average difference of all elements; The fraction represents the average level of all element position differences from the mean difference.

[0035] formula It measures the consistency of changes between data patterns rather than the direct consistency of data values. Specifically, by comparing the difference between the change of each element and the average change of all elements, the consistency of the change trend of two data patterns is evaluated. If this value is small, it means that the change trends of the two data patterns are relatively consistent.

[0036] Then, the negative exponential power of the mean of the consistency between the data pattern of the target point and the data pattern of other traffic data in the local range of the target point is taken as the noise influence coefficient of the target point, that is, the relationship is satisfied:

[0037] In the formula, For the The noise influence coefficient of each target point is For the The total amount of flow data within the local range of the target point, For the The data pattern of the target point is the same as that of the The local range of the target point The consistency between the data patterns of the traffic data It is an exponential function with the natural constant e as its base.

[0038] The above consistency mean is converted into a noise influence coefficient through negative exponential power and exponential function. The smaller the above consistency mean is, the greater the mutation of the target point is, that is, the more significant its noise influence is.

[0039] It should be noted that during the operation of the hydraulic valve, flow data may fluctuate due to flow regulation or system pressure changes. In order to more accurately identify the impact of noise, the noise impact coefficient needs to be corrected. By calculating the difference between the average value of the noise impact coefficient in 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, so the noise impact coefficient needs to be increased.

[0040] Then the corrected noise influence coefficient satisfies the relationship:

[0041] 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.

[0042] Indicates The average value of the noise influence coefficient in the local range of the target point is The difference in the noise influence coefficients of the target points represents the persistence of the target point.

[0043] According to the above S2, calculate the The noise influence coefficient of each target point can be calculated in the same way as the noise influence coefficient of all other traffic data in the data sequence.

[0044] S3: Obtaining an adjustment weight for each flow rate according to the noise impact coefficient.

[0045] The weight of each flow data in the data sequence during iterative SG filtering is determined according to the noise influence coefficient calculated by S2 above. If the noise influence coefficient of the flow data is larger, a larger weight is given when calculating the fitting influence index to increase the fitting influence index, thereby better denoising; conversely, if the noise influence coefficient is smaller, a smaller weight is given to reduce the fitting influence index to prevent overfitting of normal data.

[0046] Then the adjustment weight of each flow data satisfies the relationship:

[0047] 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 weights used when filtering this traffic data.

[0048] In another embodiment, the adjustment weight of each flow data further satisfies the relationship:

[0049] 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 Both are constants used to adjust the scale and offset.

[0050] S4: Filtering and denoising the data sequence using weights corresponding to each flow rate to obtain an optimized data sequence, and monitoring the operating status of the hydraulic valve based on the optimized data sequence.

[0051] In the embodiment of the present invention, iterative SG filtering is adopted, and the general steps are as follows: The first step is to initialize the parameters.

[0052] First, the data sequence of step S1 is processed using the SG (Savitzky-Golay) filtering algorithm. SG filtering is a digital filtering method that smoothes data by fitting a polynomial. In the embodiment of the present invention, the window width is 2×m+1 (m=4 is set), so the window width is 9 data points; the polynomial order is r (r=3 is set), that is, a cubic polynomial is used for fitting, thereby generating a new flow data sequence.

[0053] The second step is to calculate the weight.

[0054] According to the operations of the above steps S2 to S3, the adjustment weight of each flow data in the data sequence of the above step S1 is calculated.

[0055] The third step is iterative fitting.

[0056] The SG filter set in the first step is used to refit the new traffic data sequence to generate a new traffic data sequence. This is an iterative process, and each iteration will generate a new traffic data sequence.

[0057] Step 4: Calculate the fitting error index between the generated new flow data series and the original data series.

[0058] The fitting error index of the generated new flow data series and the original data series satisfies the relationship:

[0059] 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.

[0060] Step 5: Iteration termination condition.

[0061] when , it means that the current fitting effect is good enough and further iteration will not significantly improve the fitting quality, so the iteration can be terminated.

[0062] When the iteration termination condition is reached, it can be considered that accurate filtering and denoising of the hydraulic flow data has been completed. At this time, relatively accurate hydraulic flow data can be obtained, and the operating status of the hydraulic valve can be monitored based on these data.

[0063] The embodiment of the present invention further discloses a hydraulic valve operation status monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a hydraulic valve operation status monitoring method according to the present invention is implemented.

[0064] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0065] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0066] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0067] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

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 construct a local range with the target point as the center; the target point and its two adjacent flow data form the data pattern 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 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 noise influence coefficient also includes correcting it 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.

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

4. A method for monitoring the operating status of a hydraulic valve according to claim 3, 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.

5. A method for monitoring the operating status of a hydraulic valve according to claim 4, 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 When the fitting error index corresponding to the next iteration is , the iteration is stopped.

6. 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.

7. 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 6 is implemented.

Images