Operation Detection Method for Self-Propelled Crawler Multi-Circuit Hydraulic Power Station

Abstract

This application relates to the technical field of fault detection of hydraulic systems, specifically to an operation detection method for a self-propelled crawler multi-circuit hydraulic power station. The method includes: collecting the rotational speed data of the engine in the hydraulic power station, the oil temperature data at the outlet of the hydraulic oil tank, the output flow data at the outlet of the hydraulic pump, and the oil cleanliness data at the oil return port of the hydraulic oil tank; obtaining each flow subsequence and each cleanliness subsequence; obtaining the fluctuation consistency index between the cleanliness subsequence and the flow subsequence in each same time period; obtaining the overall change coefficient of the oil cleanliness data; obtaining the trend difference degree of the hydraulic power station; obtaining the temperature drop coefficient of the oil temperature data; and then obtaining the fault confidence level of the hydraulic power station to determine whether the hydraulic power station has a fault. The purpose of this application is to ensure the stable operation of the hydraulic power station by improving the fault detection accuracy of the hydraulic power station.

Description

Technical Field

[0001] This application relates to the technical field of fault detection for hydraulic systems, and particularly to an operation detection method for a self-propelled crawler multi-loop hydraulic power station. Background Art

[0002] In modern industrial and engineering fields, self-propelled crawler multi-loop hydraulic power stations are widely used in various complex environments due to their high efficiency and flexibility. To ensure the long-term stable operation of self-propelled crawler multi-loop hydraulic power stations, it is crucial to detect and solve potential faults in a timely manner.

[0003] Among the existing methods for fault detection of hydraulic power stations, some methods directly use data comparison to detect whether a hydraulic power station has a fault. However, the working conditions of the hydraulic power station need to change according to the actual situation, resulting in changes in relevant parameters of the hydraulic power station and prone to false detection problems. Some methods, although considering the change of working conditions, focus on improving the cleaning ability of the hydraulic power station and switch the working state through set thresholds, without in-depth analysis of the fault situation of the hydraulic power station.

[0004] Therefore, the existing methods for fault detection of hydraulic power stations during operation still have limitations. They do not fully utilize the coupling relationship between internal parameters of the hydraulic power station during the fault detection process, resulting in problems such as low detection accuracy and easy occurrence of false detection. Summary of the Invention

[0005] In view of the above, it is necessary to provide an operation detection method for a self-propelled crawler multi-loop hydraulic power station, which improves the fault detection accuracy of the hydraulic power station compared with the traditional operation detection method of the self-propelled crawler multi-loop hydraulic power station and can ensure the stable operation of the hydraulic power station.

[0006] The operation detection method for a self-propelled crawler multi-loop hydraulic power station of this application adopts the following technical solutions:

[0007] An embodiment of this application provides an operation detection method for a self-propelled crawler multi-loop hydraulic power station, and the method includes the following steps:

[0008] Collect the rotational speed data of the engine, the oil temperature data at the outlet of the hydraulic oil tank, the output flow data at the outlet of the hydraulic pump, and the oil cleanliness data at the return port of the hydraulic oil tank in a preset time period;

[0009] Based on the change characteristics of the rotational speed data at all acquisition moments, divide the preset time period to obtain each flow subsequence and each cleanliness subsequence;

[0010] Based on the correlation between the cleanliness subsequences and the flow rate subsequences in each same time period, as well as the consistency of the data fluctuation conditions, obtain the fluctuation consistency index between the cleanliness subsequences and the flow rate subsequences in each same time period;

[0011] Based on the local growth conditions of the data in each cleanliness subsequence and the overall growth conditions of the data in all cleanliness subsequences, obtain the overall change coefficient of the oil cleanliness data;

[0012] Based on the similarity of the change trends between the rotational speed data and the output flow rate data at all acquisition times, and combining the similarity of the change trends between the rotational speed data and the oil temperature data at all acquisition times, obtain the trend difference degree of the hydraulic power station;

[0013] Based on the decrease of the oil temperature data at all acquisition times, obtain the temperature drop coefficient of the oil temperature data;

[0014] Based on the distribution of the fluctuation consistency index between the cleanliness subsequences and the flow rate subsequences in all same time periods, and combining the overall change coefficient, the trend difference degree and the temperature drop coefficient, obtain the fault confidence level of the hydraulic power station;

[0015] Based on the fault confidence level, determine whether the hydraulic power station fails.

[0016] In one embodiment, the process of obtaining each flow rate subsequence and each cleanliness subsequence is as follows:

[0017] Arrange all the collected rotational speed data in time sequence to form a rotational speed sequence; use a mutation point detection algorithm to obtain the mutation points in the rotational speed sequence, and according to the moments where the mutation points are located, divide the preset time period into each time interval;

[0018] Arrange all the output flow rate data and the oil cleanliness data at all acquisition times in each time interval in time sequence to form each flow rate subsequence and each cleanliness subsequence.

[0019] In one embodiment, the process of obtaining the fluctuation consistency index is as follows:

[0020] For each cleanliness subsequence, calculate the dispersion of each oil cleanliness data and the oil cleanliness data at its multiple neighboring moments; arrange all the dispersions corresponding to the cleanliness subsequence in time sequence to form the local dispersion sequence of the cleanliness subsequence; obtain all the peaks in the local dispersion sequence, and take the dispersion of all the peaks in the local dispersion sequence as the peak fluctuation index of the cleanliness subsequence;

[0021] Adopt the same calculation method as the local dispersion sequence and the peak fluctuation index of each cleanliness subsequence, and calculate the local dispersion sequence and the peak fluctuation index of each flow rate subsequence respectively;

[0022] The fluctuation consistency index is further determined jointly by the correlation, the similarity of the local discrete sequences between the cleanliness subsequences and the flow rate subsequences in each same time period, and the peak fluctuation index of the cleanliness subsequences and the peak fluctuation index of the flow rate subsequences in each same time period; the consistency of the data fluctuation situation is reflected by the similarity.

[0023] In one embodiment, the expression of the fluctuation consistency index is:

[0024] ; where is the fluctuation consistency index between the i-th cleanliness subsequence and the i-th flow rate subsequence; is the mean value of the peak fluctuation indexes of the i-th cleanliness subsequence and the i-th flow rate subsequence; is the correlation coefficient between the i-th cleanliness subsequence and the i-th flow rate subsequence; exp( ) is the exponential function with the natural constant as the base; N is the number of data in the i-th cleanliness subsequence; are respectively the t-th data in the local discrete sequences of the i-th cleanliness subsequence and the i-th flow rate subsequence; is a preset value greater than 0; where all the flow rate subsequences and cleanliness subsequences are numbered according to the time sequence.

[0025] In one embodiment, the process of obtaining the overall change coefficient is as follows:

[0026] Obtain the fitting lines of all data points in each cleanliness subsequence, and calculate the mean value of the distances between all data points in each cleanliness subsequence and the fitting lines;

[0027] Map the slope of each fitting line to a positive number, denoted as the first positive number, and calculate the product of the mean value of the distances of each cleanliness subsequence and the first positive number, and the local growth situation is reflected by the product;

[0028] Calculate the average value of all data in each cleanliness subsequence, arrange the average values of all cleanliness subsequences in time sequence to form a stage change sequence, and map the sum of all elements in the first-order difference sequence of the stage change sequence to a positive number, denoted as the second positive number; the overall growth situation is reflected by the second positive number;

[0029] The overall change coefficient is further determined jointly by the second positive number and the distribution situation of the products of all cleanliness subsequences.

[0030] In one embodiment, the overall change coefficient is the product of the mean value of the products of all cleanliness subsequences and the second positive number.

[0031] In one embodiment, the process of obtaining the trend difference degree is as follows:

[0032] Arrange all the collected oil temperature data and output flow data in time sequence to form an oil temperature sequence and a flow sequence;

[0033] Respectively, record the DTW distance between the rotation speed sequence and the flow sequence, and the DTW distance between the rotation speed sequence and the oil temperature sequence as the first distance and the second distance;

[0034] The trend difference degree is the mean value of the first distance and the second distance.

[0035] In one embodiment, the process of obtaining the temperature drop coefficient is as follows:

[0036] Adopt a mutation point detection algorithm to obtain each mutation point in the oil temperature sequence, and divide the oil temperature sequence into each oil temperature subsequence according to the mutation points;

[0037] Obtain the range of all data in each oil temperature subsequence, and adopt a threshold segmentation algorithm to obtain the segmentation threshold of the range of all oil temperature subsequences; obtain the fitting straight line of all data points in each oil temperature subsequence; use the temperature change subsequence whose range is greater than the segmentation threshold and the slope of the fitting straight line is less than or equal to 0 as the temperature drop subsequence;

[0038] Calculate the dispersion degree of all elements in the first-order difference sequence of each temperature drop subsequence;

[0039] The temperature drop coefficient is the product of the mean value of the dispersion degrees of all temperature drop subsequences and the dispersion degree.

[0040] In one embodiment, the expression of the fault confidence level is:

[0041] ; where F is the fault confidence level of the hydraulic power station; C is the overall change coefficient of the oil cleanliness data; D is the trend difference degree of the hydraulic power station; W is the temperature drop coefficient of the oil temperature data; is the mean value of the fluctuation consistency indexes between the cleanliness subsequences and the flow subsequences in all the same time periods.

[0042] In one embodiment, the method for judging whether the hydraulic power station has a fault is:

[0043] If the normalized value of the fault confidence level is greater than the preset threshold, it is determined that the hydraulic power station has a fault; otherwise, it is determined that the hydraulic power station has no fault.

[0044] This application has at least the following beneficial effects:

[0045] Based on the changing characteristics of rotational speed data, this application obtains the output flow rate data and oil cleanliness data under the same working condition, analyzes the output flow rate data and oil cleanliness data under the same working condition, and obtains the fluctuation consistency index to reflect the degree of fluctuation consistency between the oil cleanliness and the oil delivery volume under the same working condition, and preliminarily evaluates the operating state of the hydraulic power station;

[0046] Furthermore, by analyzing the local growth and overall growth of the oil cleanliness, the overall change coefficient is obtained to reflect the degree of change of the oil cleanliness over time, and the operating state of the hydraulic power station is evaluated from the perspective of the change of the oil cleanliness;

[0047] Furthermore, by analyzing the change relationship between the rotational speed, output flow rate, and oil temperature when the working condition changes, the trend difference degree is obtained to reflect whether the change trends of the rotational speed, output flow rate, and oil temperature are similar, and it can accurately evaluate the possibility that the change relationship between the parameters conforms to the normal operating state;

[0048] Furthermore, by comprehensively considering the change rate of the oil temperature drop, the fluctuation consistency index, the overall change coefficient, and the trend difference degree, the fault confidence level of the hydraulic power station is obtained. Analyzing from multiple perspectives and utilizing the coupling relationship between the internal parameters of the hydraulic power station can more comprehensively and accurately evaluate the fault situation of the hydraulic power station, improve the fault detection accuracy of the hydraulic power station, and ensure the stable operation of the hydraulic power station. Description of the Drawings

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0050] Figure 1 It is the step flow chart of the operation detection method for the self-propelled crawler multi-circuit hydraulic power station provided by this application;

[0051] Figure 2 It is the schematic diagram of the acquisition process of the fault confidence level. Detailed Embodiments

[0052] In the description of the embodiments of this application, words such as "exemplary", "or", "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary", "or", "for example" aims to present relevant concepts in a specific manner.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that unless otherwise specified in this application, " / " means "or".

[0054] In addition, it should be noted that the terms "first" and "second" in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0055] The following specifically describes the specific solution of the operation detection method for the self-propelled tracked multi-circuit hydraulic power station provided by this application in conjunction with the accompanying drawings.

[0056] An operation detection method for a self-propelled tracked multi-circuit hydraulic power station provided by an embodiment of this application. Specifically, the following operation detection method for a self-propelled tracked multi-circuit hydraulic power station is provided. Please refer to Figure 1 , and this method includes the following steps:

[0057] Step 1, collect the rotational speed data of the engine in the hydraulic power station, the oil temperature data at the outlet of the hydraulic oil tank, the output flow data at the outlet of the hydraulic pump, and the oil cleanliness data at the oil return port of the hydraulic oil tank within a preset time period.

[0058] A hydraulic power station is an independent hydraulic device, also known as a hydraulic pump station, which is applicable to various hydraulic machines where the main engine and the hydraulic device can be separated. It can supply oil according to the requirements of the driving device and can well control the flow rate, direction, and pressure of the oil flow. The hydraulic power station provides power for the load equipment through the circulation of the oil. After the work is completed, the hydraulic oil returns to the oil tank through the hydraulic valve to form a cyclic use.

[0059] There are an engine, a hydraulic oil tank, and a hydraulic pump inside the hydraulic power station. In this application, a rotational speed sensor is installed at the crankshaft of the engine to collect the rotational speed data of the engine; a temperature sensor is installed at the outlet of the hydraulic oil tank to collect the oil temperature data; a flow sensor is installed at the outlet of the hydraulic pump to collect the output flow data; and a nephelometric particle counter is installed at the oil return port of the hydraulic oil tank to collect the oil cleanliness data.

[0060] Collect the rotational speed data of the engine in the hydraulic power station, the oil temperature data at the outlet of the hydraulic oil tank, the output flow data at the outlet of the hydraulic pump, and the oil cleanliness data at the oil return port of the hydraulic oil tank within a preset time period.

[0061] In this embodiment, the acquisition frequencies of the rotational speed data, oil temperature data, output flow rate data, and oil cleanliness data are all 1 s, and the length of the preset time period is 30 min. The values of the acquisition frequency and acquisition duration are preset manually, and the implementer can set them according to the actual situation without special restrictions in this application.

[0062] Arrange all the acquired rotational speed data, oil temperature data, output flow rate data, and oil cleanliness data in chronological order respectively to form a rotational speed sequence, an oil temperature sequence, a flow rate sequence, and a cleanliness sequence. In order to eliminate the influence of the dimension between different data, normalize the data in the rotational speed sequence, oil temperature sequence, flow rate sequence, and cleanliness sequence respectively. In this embodiment, the Z-Score normalization method is used to normalize the data in the rotational speed sequence, oil temperature sequence, flow rate sequence, and cleanliness sequence respectively.

[0063] Step 2: Obtain the fluctuation consistency index to evaluate the fluctuation consistency degree between the oil cleanliness and the output flow rate; obtain the overall change coefficient to reflect the overall change degree of the oil cleanliness; obtain the trend difference degree to analyze the change relationship between the rotational speed and the output flow rate and oil temperature when the working condition changes; obtain the temperature drop coefficient to reflect the temperature drop situation of the oil temperature; and then obtain the fault confidence level to comprehensively evaluate the operating state of the hydraulic power station.

[0064] When the load equipment demand of the hydraulic power station does not change, that is, when the working condition of the hydraulic power station is stable, the rotational speed of the engine in the hydraulic power station has always been relatively stable, and oil is supplied to the load equipment stably. At this time, since the rotational speed does not change greatly, the oil supply volume, oil temperature, and oil cleanliness of the hydraulic power station are also relatively stable. Considering that if the demand of the load equipment changes, the hydraulic power station will adjust the engine rotational speed to change the oil supply volume in order to respond to the load demand. If the rotational speed is greater, the oil supply volume is greater, and the flow velocity of the oil increases accordingly, resulting in an increase in the friction between the oil and the inner wall of the pipeline, causing the oil temperature to rise.

[0065] Based on the above analysis, the operating data under the same working condition can be extracted through the change characteristics of the rotational speed data, and by deeply analyzing the operating data under different working conditions, the purpose of accurately detecting the operating state of the hydraulic power station can be achieved.

[0066] Step 2.1: Based on the change characteristics of the rotational speed data at all acquisition moments, divide the preset time period to obtain each flow rate subsequence and each cleanliness subsequence.

[0067] The mutation point detection algorithm is used to obtain the mutation points in the rotational speed sequence. The moments where the mutation points are located are used as segmentation points, and the preset time period is segmented into each time interval. The output flow data and oil cleanliness data at all acquisition moments within each time interval are arranged in time sequence to form each flow subsequence and each cleanliness subsequence. Among them, each time interval corresponds to an operating condition of the hydraulic power station.

[0068] In this embodiment, the Bernaola Galvan segmentation algorithm is used to obtain the mutation points in the rotational speed sequence. The Bernaola Galvan segmentation algorithm is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to obtain the mutation points in the rotational speed sequence, implementers can adopt other existing technologies, such as the Mann-Kendall mutation point detection algorithm, the Pettitt mutation point detection algorithm, etc. This application does not make special restrictions.

[0069] Take the i-th flow subsequence and the i-th cleanliness subsequence as examples.

[0070] Under the condition of stable working conditions, the fluctuations of the delivery flow and oil cleanliness of the hydraulic power station are relatively stable. However, if faults such as wear of the hydraulic pump and aging of the seals occur inside the hydraulic power station, then even under the premise of stable working conditions, due to the reduction of the volumetric efficiency of the hydraulic pump, the system will not be able to reach the predetermined working pressure, resulting in unstable oil delivery volume of the hydraulic pump and large fluctuations in the output flow; at the same time, the damage of the hydraulic pump, especially the aging of the seals and internal wear, will also cause metal particles or other wear substances to enter the oil, thereby increasing the pollutants in the oil. Since the oil is flowing, it will also cause large fluctuations in the oil cleanliness; therefore, the operating state of the hydraulic power station can be reflected by analyzing the stability of the output flow and the oil cleanliness.

[0071] Taking the oil cleanliness data in the i-th cleanliness subsequence as the center, a window with a preset size is constructed, and the dispersion of all data within the window is calculated. The corresponding all the dispersions of the i-th cleanliness subsequence are arranged in time sequence to form the local dispersion sequence of the i-th cleanliness subsequence, which is used to reflect the local fluctuations of the data in the i-th cleanliness subsequence and the data at multiple adjacent moments. It should be noted that: when the window center is located at both ends of the cleanliness subsequence, resulting in the window exceeding the range of the cleanliness subsequence, the data in the window is filled by the mean filling method according to the data in the cleanliness subsequence.

[0072] In this embodiment, the size of the window is 1 , and the size of the window is preset by humans. Implementers can set it according to the actual situation by themselves. This application does not make special restrictions.

[0073] In this embodiment, the dispersion of all data within the window is the coefficient of variation, which is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to measure the uneven degree of distribution of all data within the window, implementers can adopt other existing technologies, such as standard deviation, variance, etc., and this application does not make special restrictions.

[0074] All peaks in the local discrete sequence of the i-th cleanliness subsequence are obtained through an automatic multi-scale peak search algorithm, and the dispersion of all peaks in the local discrete sequence of the i-th cleanliness subsequence is used as the peak fluctuation index of the i-th cleanliness subsequence; the larger the peak fluctuation index, the more inconsistent the instantaneous mutation degree of the peaks in the local fluctuation sequence of the i-th cleanliness subsequence, and the more intense the fluctuation degree within the i-th cleanliness subsequence.

[0075] In this embodiment, the dispersion of all peaks is the coefficient of variation, which is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to measure the uneven degree of distribution of all peaks, implementers can adopt other existing technologies, such as standard deviation, variance, etc., and this application does not make special restrictions.

[0076] Using the same calculation method as the local discrete sequence and peak fluctuation index of the i-th cleanliness subsequence, calculate the local discrete sequence and peak fluctuation index of the i-th flow subsequence respectively.

[0077] Based on the similarity of the local discrete sequences between the cleanliness subsequence and the flow subsequence in each same time period, and combining the peak fluctuation index of the cleanliness subsequence, the peak fluctuation index of the flow subsequence, and the correlation between the cleanliness subsequence and the flow subsequence in each same time period, obtain the fluctuation consistency index between the cleanliness subsequence and the flow subsequence in each same time period. The expression is:

[0078] ; where is the fluctuation consistency index between the i-th cleanliness subsequence and the i-th flow subsequence; is the average value of the peak fluctuation indices of the i-th cleanliness subsequence and the i-th flow subsequence; is the correlation coefficient between the i-th cleanliness subsequence and the i-th flow subsequence; exp( ) is the exponential function with the natural constant as the base, and the purpose is to map the correlation coefficient to a positive number; N is the number of data in the i-th cleanliness subsequence; are the t-th data in the local discrete sequences of the i-th cleanliness subsequence and the i-th flow subsequence respectively; is a preset value greater than 0, used to avoid the denominator being 0, The value of is preset manually, and implementers can set it by themselves. In this embodiment The value of

[0079] In this embodiment, the correlation coefficient between the i-th cleanliness subsequence and the i-th flow rate subsequence is the Pearson correlation coefficient. As other implementation manners, on the basis of being able to measure the correlation between the i-th cleanliness subsequence and the i-th flow rate subsequence, implementers can adopt other existing technologies, such as the Spearman correlation coefficient, the Kendall rank correlation coefficient, etc. This application does not make special restrictions.

[0080] It should be noted that when the hydraulic power station is operating normally and the working conditions are stable, the output flow rate and the oil cleanliness of the hydraulic power station are both relatively stable, with small fluctuations and strong synchronism. At this time, the difference between the data at the same positions in the local discrete sequences of the i-th flow rate subsequence and the i-th cleanliness subsequence is small;

[0081] However, if the hydraulic power station has failures such as wear and seal aging, even under stable working conditions, it will cause large fluctuations in the output flow rate and the oil cleanliness of the hydraulic power station; and because the degrees of influence of the failures on the output flow rate and the oil cleanliness are not the same, the fluctuations between the output flow rate and the oil cleanliness at the same moment are no longer synchronous;

[0082] Therefore, if the average fluctuation difference is larger, it means that the change degrees of the peaks of the two local discrete sequences are both larger, reflecting that the internal fluctuations of the i-th cleanliness subsequence and the i-th flow rate subsequence may be more intense; if is smaller, it means that the data change trends between the i-th cleanliness subsequence and the i-th flow rate subsequence are more inconsistent; if is larger, it means that the fluctuation difference between the output flow rate and the oil cleanliness of the hydraulic power station is larger, and the possibility that the output flow rate and the oil cleanliness no longer have the same stable characteristics is greater. Therefore, if the fluctuation consistency index is smaller, it means that the internal data fluctuations of the i-th cleanliness subsequence and the i-th flow rate subsequence are both more intense, and the possibility that they no longer have stable characteristics is greater.

[0083] According to the calculation method of the fluctuation consistency index between the i-th cleanliness subsequence and the i-th flow rate subsequence, calculate the fluctuation consistency index between the cleanliness subsequence and the flow rate subsequence in each same time period.

[0084] Step 2.2, based on the local growth situation of the data in each cleanliness subsequence and the overall growth situation of the data in all cleanliness subsequences, obtain the overall change coefficient of the oil cleanliness data.

[0085] Further, if the hydraulic power station is in good condition, even if the engine speed is increased, the cleanliness of the oil will be relatively high and stable. The reason is that in the absence of wear, there are fewer metal particles and other contaminants in the oil, and generally no additional wear particles will be generated due to the increase in flow rate. If the hydraulic power station suffers from wear or seal aging and other faults, then as the hydraulic power station continues to operate, more metal particles and other wear products will gradually accumulate in the oil. These contaminants not only come from the wear of internal components but also from the fact that the aging seals cannot effectively block the intrusion of external impurities, resulting in a further decline in the oil cleanliness. At the same time, since the damage is irreversible, if the hydraulic power station fails, over time, the cleanliness of its oil will gradually decline and the concentration of particles in the oil will gradually increase. Therefore, the operating state of the hydraulic power station can be further detected by analyzing the change in the oil cleanliness.

[0086] Still taking the i-th cleanliness subsequence as an example, perform a linear fitting on all data points in the i-th cleanliness subsequence, calculate the average distance between all data points in the i-th cleanliness subsequence and the fitting line, map the slope of the fitting line to a positive number, denoted as the first positive number, and calculate the product of the average distance of the i-th cleanliness subsequence and the first positive number; the larger the product, the greater the overall fluctuation of the i-th cleanliness subsequence, the greater the rising rate of the oil concentration, and the faster the decline degree of the oil cleanliness. Among them, the distance between the data point and the fitting line is the Euclidean distance, and the data points in the cleanliness subsequence are composed of each acquisition time and its oil cleanliness data. The calculation of the slope of the fitting line is a well-known technology and will not be elaborated in this application.

[0087] In this embodiment, the least squares method is used to perform a linear fitting on all data points in the cleanliness subsequence. The least squares method is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to perform a linear fitting on all data points in the cleanliness subsequence, the implementer can adopt other existing technologies, such as linear regression analysis, weighted least squares method, etc. This application does not make special restrictions.

[0088] In this embodiment, the method of mapping the slope of the fitting line to a positive number is: taking the slope of the fitting line as the exponent of an exponential function with the natural constant as the base.

[0089] Calculate the average value of all data in each cleanliness subsequence, arrange the average values of all cleanliness subsequences in chronological order to form a stage change sequence, and map the sum of all elements in the first-order difference sequence of the stage change sequence to a positive number, denoted as the second positive number; if the hydraulic power station does not malfunction, the average values of all data in each cleanliness subsequence are relatively consistent, the elements in the first-order difference sequence have small differences and are positive and negative, then the second positive number is small. If a malfunction occurs and the concentration of the oil fluid gradually increases, then all elements in the first-order difference sequence are positive, so the second positive number is large, and the larger the second positive number, the greater the degree of decrease in the cleanliness of the oil fluid, and the greater the likelihood of a malfunction in the hydraulic power station.

[0090] In this embodiment, the method of mapping the sum of all elements in the first-order difference sequence to a positive number is: taking the sum of all elements in the first-order difference sequence as the exponent of an exponential function with the natural constant as the base.

[0091] Furthermore, take the product of the mean value of the products of all cleanliness subsequences and the second positive number as the overall change coefficient of the oil fluid cleanliness data, and the expression is:

[0092] ; where C is the overall change coefficient of the oil fluid cleanliness data; is the mean value of the products of all cleanliness subsequences; Z is the second positive number.

[0093] It should be noted that: the larger the overall change coefficient C, the greater the degree of decrease in the cleanliness of the oil fluid, and it gradually decreases over time, and the greater the likelihood of a malfunction in the hydraulic power station.

[0094] Step 2.3, based on the similarity of the change trends between the rotational speed data and the output flow rate data at all acquisition times, and combining the similarity of the change trends between the rotational speed data and the oil temperature data at all acquisition times, obtain the trend difference degree of the hydraulic power station.

[0095] Furthermore, when the rotational speed of the hydraulic power station increases, it causes an increase in the oil delivery volume, and at the same time, due to the faster output flow rate, the oil temperature rises; when the rotational speed decreases, the output flow rate also immediately decreases, and the oil temperature gradually decreases accordingly. Therefore, by analyzing the change relationships between the rotational speed and the output flow rate and the oil temperature when the working conditions change, the operating state of the hydraulic power station can be more accurately reflected.

[0096] Considering that the change in temperature has a lag compared to the change in rotational speed, the correlation between the oil temperature sequence and the rotational speed sequence is analyzed through DTW (Dynamic Time Warping) distance to overcome the problem caused by the temperature response lag.

[0097] The DTW distances between the rotational speed sequence and the flow rate sequence, and between the rotational speed sequence and the oil temperature sequence are respectively denoted as the first distance and the second distance. The mean value of the first distance and the second distance is used as the trend difference degree of the hydraulic power station. The smaller the trend difference degree, the stronger the correlation between the rotational speed and the output flow rate, and between the rotational speed and the oil temperature, the more consistent the data change trends, and the greater the possibility that the hydraulic power station has not failed.

[0098] Step 2.4: Based on the decrease of the oil temperature data at all acquisition moments, obtain the temperature drop coefficient of the oil temperature data.

[0099] Furthermore, if the hydraulic power station fails, due to the increase in the concentration of the oil fluid, the process of heat transfer and dissipation becomes slow. As a result, when the rotational speed decreases, the temperature drops slowly and the drop rate is no longer stable.

[0100] Use the mutation point detection algorithm to obtain each mutation point in the oil temperature sequence, take each mutation point as each segmentation point, and divide the oil temperature sequence into each oil temperature subsequence.

[0101] Obtain the range of all data in each oil temperature subsequence, and use the threshold segmentation algorithm to obtain the segmentation threshold of the range of all oil temperature subsequences; obtain the fitting straight line of all data points in each oil temperature subsequence; take the temperature change subsequence whose range is greater than the segmentation threshold and the slope of the fitting straight line is less than or equal to 0 as the temperature drop subsequence; calculate the dispersion degree of all elements in the first-order difference sequence of each temperature drop subsequence, and take the product of the mean value of the dispersion degrees of all temperature drop subsequences and the dispersion degree as the temperature drop coefficient of the oil temperature data. The larger the temperature drop coefficient, the more inconsistent the temperature change degree of each temperature drop subsequence and the greater the change in the drop rate. It should be noted that when the temperature drop subsequence cannot be obtained, 0 is used as the temperature drop coefficient of the oil temperature data. Among them, the data points in the oil temperature subsequence are composed of each acquisition moment and its oil temperature data. The calculation of the slope of the fitting straight line is a well-known technology and will not be elaborated in this application.

[0102] In this embodiment, the Bernaola Galvan segmentation algorithm is used to obtain each mutation point in the oil temperature sequence. The Bernaola Galvan segmentation algorithm is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to obtain each mutation point in the oil temperature sequence, the implementer can use other existing technologies, such as the Mann-Kendall mutation point detection algorithm, the Pettitt mutation point detection algorithm, etc. This application does not make special restrictions.

[0103] In this embodiment, the Otsu threshold segmentation algorithm is used to obtain the segmentation threshold of the range of all oil temperature subsequences. The Otsu threshold segmentation algorithm is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to obtain the segmentation threshold of the range of all oil temperature subsequences, implementers can adopt other existing technologies, such as global threshold segmentation, iterative threshold segmentation, etc., and this application does not make special restrictions.

[0104] In this embodiment, the least squares method is used to obtain the fitting line of all data points in each oil temperature subsequence. The least squares method is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to obtain the fitting line of all data points in each oil temperature subsequence, implementers can adopt other existing technologies, such as linear regression analysis, weighted least squares method, etc., and this application does not make special restrictions.

[0105] In this embodiment, the degree of dispersion of all elements in the first-order difference sequence is variance, and the degree of dispersion of the degree of dispersion of all temperature drop subsequences is variance. As other implementation manners, on the basis of being able to measure the degree of uneven distribution of all elements in the first-order difference sequence and the degree of uneven distribution of the degree of dispersion of all temperature drop subsequences, implementers can adopt other existing technologies, such as standard deviation, coefficient of variation, etc., and this application does not make special restrictions.

[0106] Step 2.5, based on the distribution of the fluctuation consistency index between the cleanliness subsequences and the flow rate subsequences of all the same time periods, and in combination with the overall change coefficient, trend difference degree and temperature drop coefficient, obtain the fault confidence level of the hydraulic power station.

[0107] Furthermore, by comprehensively considering the distribution of the fluctuation consistency index between the cleanliness subsequences and the flow rate subsequences of all the same time periods, the overall change coefficient, trend difference degree and temperature drop coefficient, obtain the fault confidence level of the hydraulic power station. The expression is:

[0108] ; where F is the fault confidence level of the hydraulic power station; C is the overall change coefficient of the oil cleanliness data; D is the trend difference degree of the hydraulic power station; W is the temperature drop coefficient of the oil temperature data; is the mean value of the fluctuation consistency index between the cleanliness subsequences and the flow rate subsequences of all the same time periods.

[0109] It should be noted that: if the mean value The smaller it is, the more likely it is that the data fluctuations between the cleanliness subsequence and the flow rate subsequence in the same time period are no longer synchronized; if the overall change coefficient C is larger, it indicates that the overall degree of decline in oil cleanliness is greater; if the trend difference degree D is larger, it indicates that the change trends between the rotational speed and the output flow rate, and between the rotational speed and the oil temperature are more likely to be inconsistent; if the temperature drop coefficient W is larger, it indicates that the fluctuation of the oil temperature drop rate is greater when the oil temperature is decreasing. Therefore, the greater the fault confidence level, the greater the likelihood of a fault occurring in the hydraulic power station. The schematic diagram of the acquisition process of the fault confidence level is as shown in Figure 2 shown.

[0110] Step 3, based on the fault confidence level, determine whether a fault has occurred in the hydraulic power station.

[0111] Perform normalization processing on the fault confidence level of the hydraulic power station. If the normalized value of the fault confidence level is greater than the preset threshold, it is determined that the hydraulic power station has suffered wear or seal aging failure, and the hydraulic power station needs to be repaired in time; if the normalized value of the fault confidence level is less than or equal to the preset threshold, it is determined that no fault has occurred in the hydraulic power station, and it can continue to be operated and used.

[0112] In this embodiment, the hyperbolic tangent function is used to perform normalization processing on the fault confidence level of the hydraulic power station.

[0113] In this embodiment, the value of the preset threshold is 0.5. The value of the preset threshold is preset manually, and the implementer can set it by himself / herself. This application does not make special restrictions.

[0114] In summary, this application obtains the output flow rate data and oil cleanliness data under the same working conditions through the change characteristics of the rotational speed data, analyzes the output flow rate data and oil cleanliness data under the same working conditions, and obtains the fluctuation consistency index to reflect the fluctuation consistency degree between the oil cleanliness and the oil delivery volume under the same working conditions, and preliminarily evaluates the operating state of the hydraulic power station;

[0115] Furthermore, by analyzing the local growth situation and overall growth situation of the oil cleanliness, the overall change coefficient is obtained to reflect the change degree of the oil cleanliness over time, and the operating state of the hydraulic power station is evaluated from the perspective of the change of the oil cleanliness;

[0116] Furthermore, by analyzing the change relationship between the rotational speed and the output flow rate, and the oil temperature when the working condition changes, the trend difference degree is obtained to reflect whether the change trends between the rotational speed and the output flow rate, and the oil temperature are similar, and it can accurately evaluate the possibility that the change relationship between the parameters conforms to the normal operating state;

[0117] Furthermore, by comprehensively considering the change rate of the oil temperature drop, the fluctuation consistency index, the overall change coefficient, and the trend difference degree, the fault confidence level of the hydraulic power station is obtained. Analyzing from multiple perspectives and making use of the coupling relationship between the internal parameters of the hydraulic power station can more comprehensively and accurately evaluate the fault situation of the hydraulic power station, improve the fault detection accuracy of the hydraulic power station, and ensure the stable operation of the hydraulic power station.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order from that disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0119] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-limiting.

Claims

1. An operation detection method for a self-propelled crawler multi-circuit hydraulic power station, characterized in that: The method comprises the following steps: Collect the speed data of the engine in the hydraulic power station, the oil temperature data at the outlet of the hydraulic oil tank, the output flow data at the outlet of the hydraulic pump, and the oil cleanliness data at the oil return port of the hydraulic oil tank within a preset time period; Based on the change characteristics of the rotation speed data at all acquisition moments, the preset time period is divided to obtain each flow subsequence and each cleanliness subsequence; Based on the correlation between the cleanliness subsequence and the flow subsequence in each same time period and the consistency of data fluctuation, the fluctuation consistency index between the cleanliness subsequence and the flow subsequence in each same time period is obtained; Based on the local growth of the data in each cleanliness subsequence and the overall growth of the data in all cleanliness subsequences, the overall variation coefficient of the oil cleanliness data is obtained; Based on the similarity of the changing trends between the speed data and the output flow data at all the collection moments, combined with the similarity of the changing trends between the speed data and the oil temperature data at all the collection moments, the trend difference of the hydraulic power station is obtained; Based on the decrease of the oil temperature data at all the acquisition moments, the temperature drop coefficient of the oil temperature data is obtained; Based on the distribution of the fluctuation consistency index between the cleanliness subsequences and the flow subsequences in all the same time periods, and in combination with the overall variation coefficient, trend difference and temperature drop coefficient, the fault confidence of the hydraulic power station is obtained; Based on the fault confidence, it is determined whether a fault occurs in the hydraulic power station.

2. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 1, characterized in that: The acquisition process of each flow subsequence and each cleanliness subsequence is as follows: Arrange all the collected speed data in time sequence to form a speed sequence; use a mutation point detection algorithm to obtain the mutation point in the speed sequence, and divide the preset time period into various time intervals according to the time of the mutation point; The output flow data and oil cleanliness data of all acquisition moments in each time interval are arranged in time series to form flow subsequences and cleanliness subsequences.

3. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 1, characterized in that: The process of obtaining the volatility consistency index is as follows: For each cleanliness subsequence, the discreteness of each oil cleanliness data and the oil cleanliness data at multiple adjacent moments are calculated; all the discreteness corresponding to the cleanliness subsequence are arranged in time sequence to form a local discrete sequence of the cleanliness subsequence; all peaks in the local discrete sequence are obtained, and the discreteness of all peaks in the local discrete sequence is used as the peak fluctuation index of the cleanliness subsequence; The local discrete sequence and peak fluctuation index of each flow subsequence are calculated respectively by using the same calculation method as the local discrete sequence and peak fluctuation index of each cleanliness subsequence; The fluctuation consistency index is further determined by the correlation and the similarity of the local discrete sequences between the cleanliness subsequences and the flow subsequences in the same time period, combined with the peak fluctuation index of the cleanliness subsequences and the peak fluctuation index of the flow subsequences in the same time period; the consistency of the data fluctuation is reflected by the similarity.

4. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 3, characterized in that: The expression of the volatility consistency index is: ; In the formula, is the fluctuation consistency index between the i-th cleanliness subsequence and the i-th flow subsequence; is the mean of the peak fluctuation index of the i-th cleanliness subsequence and the i-th flow subsequence; is the correlation coefficient between the ith cleanliness subsequence and the ith flow rate subsequence; exp( ) is an exponential function with a natural constant as the base; N is the number of data in the ith cleanliness subsequence; are the tth data in the local discrete sequence of the i-th cleanliness subsequence and the i-th flow subsequence respectively; is a preset value greater than 0; wherein all flow subsequences and cleanliness subsequences are numbered in chronological order.

5. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 1, characterized in that: The process of obtaining the overall variation coefficient is as follows: Obtain the fitted straight line of all data points in each cleanliness subsequence, and calculate the mean distance between all data points in each cleanliness subsequence and the fitted straight line; Mapping the slope of each fitting straight line to a positive number, recorded as a first positive number, calculating the product of the distance mean of each cleanliness subsequence and the first positive number, and the local growth situation is reflected by the product; Calculate the average value of all data in each cleanliness subsequence, arrange the average values ​​of all cleanliness subsequences in time sequence to form a stage change sequence, map the sum of all elements in the first-order difference sequence of the stage change sequence to a positive number, recorded as the second positive number; the overall growth situation is reflected by the second positive number; The overall variation coefficient is further determined by the second positive number and the distribution of the products of all cleanliness subsequences.

6. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 5, characterized in that: The overall variation coefficient is the product of the mean of the products of all cleanliness subsequences and the second positive number.

7. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 2, characterized in that: The process of obtaining the trend difference is as follows: Arrange all the collected oil temperature data and output flow data in time sequence to form oil temperature sequence and flow sequence; The DTW distance between the speed sequence and the flow sequence, and the DTW distance between the speed sequence and the oil temperature sequence are recorded as the first distance and the second distance respectively; The trend difference is the average of the first distance and the second distance.

8. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 7, characterized in that: The process of obtaining the temperature drop coefficient is as follows: The mutation point detection algorithm is used to obtain the mutation points in the oil temperature sequence, and the oil temperature sequence is divided into various oil temperature subsequences according to the mutation points; Obtain the range of all data in each oil temperature subsequence, and use the threshold segmentation algorithm to obtain the segmentation threshold of the range of all oil temperature subsequences; obtain the fitting straight line of all data points in each oil temperature subsequence; and take the temperature change subsequence whose range is greater than the segmentation threshold and the slope of the fitting straight line is less than or equal to 0 as the temperature drop subsequence; Calculate the discreteness of all elements in the first-order difference sequence of each temperature drop subsequence; The temperature drop coefficient is the product of the mean of the discrete degrees of all temperature drop subsequences and the discrete degree.

9. The operation detection method for a self-propelled crawler-type multi-circuit hydraulic power station according to claim 1, characterized in that: The expression of the fault confidence is: ; In the formula, F is the fault confidence of the hydraulic power station; C is the overall variation coefficient of the oil cleanliness data; D is the trend difference of the hydraulic power station; W is the temperature drop coefficient of the oil temperature data; It is the mean of the fluctuation consistency index between the cleanliness subsequences and the flow subsequences in all the same time periods.

10. The operation detection method for a self-propelled crawler type multi-circuit hydraulic power station according to claim 1, characterized in that: The method for determining whether a hydraulic power station fails is as follows: If the normalized value of the fault confidence is greater than a preset threshold, it is determined that the hydraulic power station has failed; otherwise, it is determined that the hydraulic power station has not failed.

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