Method, device, equipment and storage medium for determining remaining life of components

Abstract

The present disclosure relates to a method, device, equipment and storage medium for determining the remaining life of a component, and relates to the field of data processing technology. The method includes: obtaining a test data set corresponding to a target component, and performing feature extraction on the test data set to obtain a feature indicator set. When the feature indicators in the feature indicator set meet the alarm conditions, the remaining life of the target component is determined based on the test data set. Feature extraction can avoid the interference of a single test data on the alarm prediction. If the feature indicators in the feature indicator set meet the alarm conditions, the remaining life of the target component is determined based on the test data set, so that the remaining life of the target component can be warned in time before the target component fails, and then replaced before it fails, thereby avoiding the failure of the target component and preventing the target component from being replaced prematurely.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a method, device, equipment, and storage medium for determining the remaining life of a component. Background Art

[0002] For some components, performance degradation may occur during long-term use. For example, wear and tear may cause performance degradation, which may lead to component failure.

[0003] Related technologies employ both post-event maintenance and preventive maintenance. Post-event maintenance inevitably leads to production stoppages and other damage. Preventive maintenance relies on the lifespan statistics of numerous failed components to determine their service life. Furthermore, the service life of the same component varies depending on its operating conditions, making it difficult to accurately determine the service life of a single component. Summary of the Invention

[0004] In order to overcome the problems existing in the related art, the present disclosure provides a method, device, equipment and storage medium for determining the remaining life of a component. For a certain target component, a test data set for the target component can be obtained, and features can be extracted from the test data set to obtain characteristic indicators corresponding to multiple monitoring moments, avoiding interference of a single test data on the alarm prediction, and then the characteristic indicators are processed. If the characteristic indicators in the characteristic indicator set meet the alarm conditions, the remaining life of the target component is determined based on the test data set, so that the remaining life of the target component can be warned in time before the target component fails, and then it can be replaced before it fails, avoiding failure of the target component and avoiding premature replacement of the target component.

[0005] According to a first aspect of an embodiment of the present disclosure, a method for determining the remaining life of a component is provided, comprising:

[0006] Obtain the test data set corresponding to the target component;

[0007] Extracting features from the test data set to obtain a feature index set, wherein the feature index set includes feature indicators corresponding to multiple monitoring moments;

[0008] When the characteristic indicators in the characteristic indicator set meet the alarm condition, the remaining life of the target component is determined according to the test data set.

[0009] Optionally, the test data set includes test data subsets corresponding to multiple target test items;

[0010] The feature extraction of the test data set to obtain a feature index set includes:

[0011] Perform feature extraction on the test data subset corresponding to each target test item to obtain a feature index set corresponding to each target test item;

[0012] When the characteristic indicators in the characteristic indicator set meet the alarm condition, determining the remaining life of the target component according to the test data set includes:

[0013] For a characteristic indicator set corresponding to any target test item, if the characteristic indicators corresponding to at least two consecutive monitoring moments satisfy an alarm condition, determining the remaining sub-life of the target component corresponding to the any target test item based on the test data subset corresponding to the any target test item;

[0014] The remaining life of the target component is determined according to the remaining sub-life corresponding to at least one target test item.

[0015] Optionally, the feature extraction is performed on each test data subset corresponding to each target test item to obtain a feature indicator set corresponding to each target test item, including:

[0016] For a test data subset corresponding to any target test item, extract the test data subset through a sliding window to obtain multiple groups of candidate test data, and extract a group of candidate test data at each monitoring moment;

[0017] Feature extraction is performed on each set of candidate test data to obtain feature indicators corresponding to multiple monitoring moments.

[0018] Optionally, when the characteristic indicators corresponding to at least two consecutive monitoring moments satisfy the alarm condition, before determining the remaining sub-life of the target component corresponding to any target test item based on the test data subset corresponding to the any target test item, the method further includes:

[0019] Determining a first characteristic indicator and an alarm threshold corresponding to a current monitoring moment according to a characteristic indicator set corresponding to any target test item;

[0020] When the first characteristic indicator is less than the alarm threshold, it is determined that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition.

[0021] Optionally, determining the first characteristic indicator and the alarm threshold corresponding to the current monitoring moment according to the characteristic indicator set corresponding to any target test item includes:

[0022] Obtaining a first characteristic indicator corresponding to the current monitoring moment from the characteristic indicator set corresponding to any target test item;

[0023] Obtaining second characteristic indicators corresponding to multiple consecutive historical monitoring moments before the current monitoring moment;

[0024] The alarm threshold is determined according to the mean and standard deviation of the second characteristic indicator.

[0025] Optionally, determining the remaining sub-lifetime of the target component corresponding to any target test item based on the test data subset corresponding to the any target test item includes:

[0026] Determining a degradation model corresponding to the target component, and obtaining a life prediction model based on the degradation model;

[0027] Acquire multiple test data obtained after the at least two consecutive monitoring moments in the test data subset corresponding to any target test item to obtain a target data set;

[0028] The remaining sub-life of the target component corresponding to any target test item is determined according to the target data set and the life prediction model.

[0029] Optionally, determining the remaining sub-lifetime of the target component corresponding to any target test item according to the target data set and the life prediction model includes:

[0030] Performing parameter estimation on the target data set to obtain a degradation rate and a degradation volatility intensity;

[0031] Determining degradation results corresponding to different test moments according to the degradation rate and the degradation volatility intensity;

[0032] The degradation rate, the degradation volatility intensity and the degradation result are processed by the life prediction model to obtain the remaining sub-life of the target component corresponding to any target test item.

[0033] Optionally, obtaining a test data set corresponding to a target component includes:

[0034] determining at least one target test item related to the target component;

[0035] A test data set corresponding to each target test item is obtained respectively. The test data set corresponding to any target test item is obtained by periodically testing the target component using the test method corresponding to the target test item.

[0036] Optionally, determining the remaining life of the target component according to the remaining sub-life corresponding to at least one target test item includes:

[0037] The minimum value of the remaining sub-lifespans corresponding to the at least one target test item is determined as the remaining lifespan of the target component.

[0038] According to a second aspect of an embodiment of the present disclosure, there is provided a device for determining the remaining life of a component, comprising:

[0039] An acquisition module is configured to acquire a test data set corresponding to a target component;

[0040] a feature extraction module configured to extract features from the test data set to obtain a feature index set, wherein the feature index set includes feature indicators corresponding to multiple monitoring moments;

[0041] The first determining module is configured to determine the remaining life of the target component according to the test data set when the characteristic indicators in the characteristic indicator set meet the alarm condition.

[0042] According to a third aspect of an embodiment of the present disclosure, there is provided a device for determining the remaining life of a component, comprising:

[0043] processor;

[0044] a memory for storing processor-executable instructions;

[0045] The processor is configured to implement the steps of the method for determining the remaining life of a component provided by the first aspect of the present disclosure when executing.

[0046] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the method for determining the remaining life of a component provided by the first aspect of the present disclosure are implemented.

[0047] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0048] For a certain target component, a test data set for the target component can be obtained, and features can be extracted from the test data set to obtain characteristic indicators corresponding to multiple monitoring moments, avoiding interference of single test data on alarm prediction, and then processing the characteristic indicators. If the characteristic indicators in the characteristic indicator set meet the alarm conditions, the remaining life of the target component is determined based on the test data set, so that the remaining life of the target component can be warned in time before the target component fails, and then replaced before it fails, avoiding failure of the target component and avoiding premature replacement of the target component.

[0049] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0051] Figure 1 The present invention is a flowchart of a method for determining the remaining life of a component according to an exemplary embodiment.

[0052] Figure 2 The figure is a flow chart showing a method for determining the remaining life according to an exemplary embodiment.

[0053] Figure 3 The figure is a flowchart showing a method for determining a feature index set according to an exemplary embodiment.

[0054] Figure 4 The flowchart of a method for determining a remaining sub-lifetime is shown according to an exemplary embodiment.

[0055] Figure 5 The flowchart of another method for determining the remaining sub-lifetime is shown according to an exemplary embodiment.

[0056] Figure 6 The present invention is a flowchart of another method for determining the remaining life of a component according to an exemplary embodiment.

[0057] Figure 7 It is a schematic diagram showing a prediction effect according to an exemplary embodiment.

[0058] Figure 8 is a schematic diagram showing another prediction effect according to an exemplary embodiment.

[0059] Figure 9 The present invention is a block diagram of a device for determining the remaining life of a component according to an exemplary embodiment.

[0060] Figure 10 The present invention is a block diagram of a device for determining the remaining life of a component according to an exemplary embodiment. DETAILED DESCRIPTION

[0061] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0062] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the corresponding data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0063] For some components, performance degradation may occur during long-term use, leading to failure of the component. For example, motherboard testing is a key process in the manufacturing of mobile phones, which can test the radio frequency performance of mobile phones. In the board testing process, the test equipment contacts the motherboard under test through probes for testing, and evaluates the performance, function, stability and safety of the motherboard based on the test results to prevent defective products from flowing into subsequent processes. However, the testing of board testing equipment relies on the probe to be plugged and unplugged from the motherboard under test for a long time and many times, which inevitably causes the probe to wear. As the test equipment continues to operate, the wear of the probe accumulates over a long period of time, causing the probe to eventually fail, resulting in the failure of multiple consecutive motherboard tests in production.

[0064] In the related art, two methods can be used: post-maintenance and preventive maintenance. Post-maintenance inevitably causes production stagnation and other damage. Preventive maintenance relies on the life statistics of a large number of failed components to determine the service life of the component. If the current component has exceeded its service life, it is replaced. However, for the same component, different operating conditions have different service lives, making it difficult to accurately determine the service life of a single component. Inaccurate service life determination will cause the component to be replaced too early or too late, resulting in wasted costs or loss of production capacity.

[0065] In response to the above technical problems, the present disclosure provides a method, device, equipment and storage medium for determining the remaining life of a component. For a certain target component, a test data set for the target component can be obtained, and features can be extracted from the test data set to obtain characteristic indicators corresponding to multiple monitoring moments, avoiding interference of a single test data on the alarm prediction. The characteristic indicators are then processed. If the characteristic indicators in the characteristic indicator set meet the alarm conditions, the remaining life of the target component is determined based on the test data set, so that the remaining life of the target component can be warned in time before the target component fails, and then it can be replaced before it fails, avoiding failure of the target component and preventing the target component from being replaced prematurely.

[0066] Figure 1 FIG. 1 is a flow chart showing a method for determining the remaining life of a component according to an exemplary embodiment. Figure 1 As shown, the method can be applied to a server and may include the following steps.

[0067] In step S101 , a test data set corresponding to a target component is obtained.

[0068] In this embodiment, the target component can be tested periodically or in real time to obtain a corresponding test data set. This test data set can include multiple test data, each of which can be obtained by testing the target component at different test times. The target component can be tested continuously to continuously obtain corresponding test data, so that the operating status of the target component can be obtained in a timely manner. The target component can be any component that may experience performance degradation during use, for example, a probe for testing RF performance or a probe for testing current, as well as a nozzle, a bit, a motor, a bearing, etc.

[0069] In step S102 , feature extraction is performed on the test data set to obtain a feature index set, which includes feature indexes corresponding to multiple monitoring moments.

[0070] In this embodiment, multiple test data corresponding to the test data set can be obtained at different monitoring moments, and feature extraction can be performed on the multiple test data to obtain a feature index, that is, one monitoring moment corresponds to one feature index, so that multiple feature indexes can be obtained at multiple monitoring moments, and the multiple feature indexes can constitute a feature index set. That is, each feature index is obtained from multiple continuous test data. Based on the continuously obtained test data, feature extraction can be continuously performed on the test data set to obtain the corresponding feature index. Feature extraction of the test data set can obtain the changing trend of the test data and can avoid directly using the test data to determine whether to calculate the remaining life. Among them, errors are inevitable in the testing process, and a single test data may have a large error, resulting in inaccurate judgment results obtained using it. After extracting the features of the test data set to obtain a feature index set, and then determining whether to calculate the remaining life, the judgment result can be more accurate.

[0071] In step S103 , when the characteristic indicators in the characteristic indicator set meet the alarm condition, the remaining life of the target component is determined according to the test data set.

[0072] In this embodiment, when the characteristic indicators in the characteristic indicator set meet the alarm conditions, it is determined that the target component is at risk of failure, an alarm can be issued, and the remaining life of the target component can be determined based on the test data set. Issuing an alarm can draw the user's attention to the target component, and determining the remaining life of the target component can more intuitively determine how long the target component can be used, so as to reasonably arrange the use of the target component. In addition, maintenance operations can be arranged in advance based on the remaining life of the target component. For example, the aging time can be determined based on the remaining life, and according to the production line inspection plan, the nearest inspection before the failure time can be selected to perform maintenance on the target component. This can ensure the service life of the target component as much as possible, avoid the waste of resources caused by premature replacement, and also allow it to be replaced before it fails, avoiding the failure of the target component.

[0073] In one possible implementation, the test data set may include test data subsets corresponding to multiple target test items, and a method for performing feature extraction on the test data set to obtain a feature indicator set may be: performing feature extraction on the test data subset corresponding to each target test item respectively to obtain a feature indicator set corresponding to each target test item.

[0074] In this embodiment, multiple target test items can be tested on a target component, and a corresponding test data subset can be obtained for each target test item. Feature extraction can be performed on the test data subset corresponding to each target test item to obtain a feature index set corresponding to each target test item. That is, a feature index set corresponding to each target test item is obtained for each test data subset corresponding to that target test item.

[0075] In one possible embodiment, a method for obtaining a test data set corresponding to a target component may be: determining at least one target test item related to the target component; respectively obtaining a test data set corresponding to each target test item, and the test data set corresponding to any target test item is obtained by periodically testing the target component using the test method corresponding to the target test item.

[0076] In this embodiment, at least one target test item related to the performance of the target component can be screened out, and based on the test method corresponding to the at least one target test item, the target component is periodically tested to obtain a test data set corresponding to each target test item.

[0077] Figure 2 FIG. 1 is a flow chart showing a method for determining the remaining life according to an exemplary embodiment. Figure 2As shown, in a possible implementation manner, when a characteristic indicator in a characteristic indicator set meets an alarm condition, determining the remaining life of a target component according to a test data set may include the following steps:

[0078] In step S201, for the characteristic indicator set corresponding to any target test item, when the characteristic indicators corresponding to at least two consecutive monitoring moments meet the alarm conditions, the remaining sub-life of the target component corresponding to any target test item is determined based on the test data subset corresponding to the any target test item.

[0079] In step S202, the remaining life of the target component is determined according to the remaining sub-life corresponding to the at least one target test item.

[0080] In this embodiment, based on the alarm condition, for the characteristic indicator set corresponding to any target test item, each characteristic indicator in the obtained characteristic indicator set can be judged to determine whether the characteristic indicator meets the alarm condition. If the characteristic indicators corresponding to at least two consecutive monitoring moments all meet the alarm condition, it is determined that the target component is at risk of failure, an alarm can be issued, and the remaining sub-life of the target component corresponding to any target test item can be determined based on the test data subset corresponding to the any target test item. Among them, there may be more than one characteristic indicator set corresponding to the target test item that triggers the alarm and calculates the corresponding remaining life, and for different target test items, the corresponding test data sets are different, and the corresponding characteristic indicator sets are also different, so the obtained remaining life is also different. The remaining life of the target component can be determined based on the remaining sub-life corresponding to the at least one target test item.

[0081] In one possible embodiment, when the characteristic indicators in the characteristic indicator set meet the alarm conditions, the method for determining the remaining life of the target component based on the test data set may be: for the characteristic indicator set corresponding to any target test item, when there is a characteristic indicator less than the alarm threshold, the total number of alarms is obtained, wherein if there is a characteristic indicator less than the alarm threshold, an alarm is issued, that is, the total number of alarms is increased by 1, and when the total number of alarms is greater than the alarm number threshold, the remaining sub-life of the target component corresponding to any target test item is determined based on the test data subset corresponding to the any target test item; and the remaining life of the target component is determined based on the remaining sub-life corresponding to the at least one target test item.

[0082] Figure 3 is a flow chart showing a method for determining a feature index set according to an exemplary embodiment. Figure 3As shown, in a possible implementation, feature extraction is performed on each test data subset corresponding to each target test item to obtain a feature index set corresponding to each target test item, which may include the following steps:

[0083] In step S301 , for a test data subset corresponding to any target test item, the test data subset is extracted through a sliding window to obtain multiple groups of candidate test data, and a group of candidate test data is extracted at each monitoring moment.

[0084] In this embodiment, for a test data subset corresponding to any target test item, the test data subset can be extracted through a sliding window. The length of the sliding window can be determined according to actual conditions. For example, the sliding window may include 10 test data, that is, each time the sliding window slides, it includes 10 test data. The sliding window can be a non-overlapping sliding window or a partially overlapping sliding window. For example, for a non-overlapping sliding window, if there are currently 20 test data, the sliding window may include the first 10 test data at one time, and the sliding window may include the last 10 test data at another time. For a partially overlapping sliding window, if the number of overlaps is 9, the sliding window only slides the distance of one test data at a time. For example, the sliding window includes the 1st to 10th test data at a time, and after the sliding window moves once, it includes the 2nd to 11th test data.

[0085] Through the above sliding window, a set of candidate test data is obtained by sliding once at each monitoring moment, and multiple sets of candidate test data can be obtained until all the test data in the current test data subset are completely slid.

[0086] In step S302 , feature extraction is performed on each set of candidate test data to obtain feature indicators corresponding to multiple monitoring moments.

[0087] In this embodiment, feature extraction can be performed on each set of candidate test data. Each monitoring moment corresponds to a set of candidate test data, and a feature index can be obtained for each set of candidate test data. For each set of candidate test data, the root mean square (RMS) of the set of candidate test data can be extracted to obtain the feature index corresponding to the monitoring moment in which the set of candidate test data occurred. Alternatively, the mean or kurtosis of the set of candidate test data can be extracted as a feature index.

[0088] In one possible implementation, if the characteristic indicators corresponding to at least two consecutive monitoring moments satisfy the alarm condition, before determining the remaining sub-life of the target component for any target test item based on the test data subset corresponding to the target test item, it may be determined whether the characteristic indicator corresponding to each monitoring moment satisfies the alarm condition. The determination of whether the characteristic indicator satisfies the alarm condition may be based on an alarm threshold. For example, if any characteristic indicator is less than the alarm threshold, it is determined that the characteristic indicator satisfies the alarm condition.

[0089] Among them, the method for judging whether any characteristic indicator meets the alarm condition can be specifically as follows: according to the characteristic indicator set corresponding to any target test item, determine the first characteristic indicator and alarm threshold corresponding to the current monitoring moment; when the first characteristic indicator is less than the alarm threshold, determine that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition.

[0090] In this embodiment, each monitoring moment corresponds to a characteristic indicator, and the corresponding first characteristic indicator can be obtained from the characteristic indicator set according to the current monitoring moment. The alarm threshold can also be determined based on the characteristic indicator set corresponding to any target test item.

[0091] In a possible implementation, the method for determining the alarm threshold may be: obtaining second characteristic indicators corresponding to multiple consecutive historical monitoring moments before the current monitoring moment; and determining the alarm threshold based on the mean and standard deviation of the second characteristic indicators.

[0092] In this embodiment, the current monitoring time can be recorded as t k , take the historical feature length as N, where N can be 50, that is, obtain the second feature index corresponding to the 50 consecutive historical monitoring moments before the current monitoring moment. At this time, the second feature index can be X k-50:k-1 , calculate its mean(X k-50:k-1 ) and standard deviation std(X k-50:k-1 ), and then the value of the alarm threshold can be set to: mean(X k-50:k-1 )-3×std(X k-50:k-1 ). Wherein, k is a positive integer.

[0093] The characteristic index corresponding to the current detection moment can be recorded as x k , if x k If the value is less than the alarm threshold, it is determined that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition.

[0094] In a possible implementation, the interval alarm counter and the continuous alarm counter can be used to determine whether there are at least two consecutive monitoring moments where the characteristic indicators corresponding to the monitoring moments satisfy the alarm condition. For example, if the characteristic indicator corresponding to the current detection moment is x k If the value is lower than the alarm threshold, the interval alarm counter increases its count value l=k; otherwise, k Incorporate the test data set to obtain the new second feature index Xk-50:k and update the alarm threshold. k+1 At this moment, new characteristic indicators and alarm thresholds are obtained, and it is determined whether the new characteristic indicators are lower than the alarm threshold. If so, an alarm will be issued. If t k and t k+1 If an alarm occurs at all times, the value of the continuous alarm counter A is increased. l =k+1. As the test data is continuously acquired, new characteristic indicators will be continuously obtained. The count of the continuous alarm counter can also be continuously increased. When A l -A l-1 When <2, an alarm is output and the remaining life of the target component is predicted.

[0095] Among them, the number of alarms of the interval alarm counter can also be counted to obtain the total number of alarms. When the characteristic indicators corresponding to at least two consecutive monitoring moments meet the alarm conditions and the total number of alarms is greater than the alarm number threshold, the remaining sub-life of the target component corresponding to any target test item can be determined based on the test data subset corresponding to any target test item.

[0096] Figure 4 FIG. 1 is a flow chart showing a method for determining a remaining sub-lifetime according to an exemplary embodiment. Figure 4 As shown, in a possible implementation manner, determining the remaining sub-life of a target component corresponding to any target test item based on the test data subset corresponding to the any target test item may include the following steps:

[0097] In step S401 , a degradation model corresponding to a target component is determined, and a life prediction model is obtained based on the degradation model.

[0098] In this embodiment, a degradation model may be constructed based on the trend characteristics of the test data of the target component. For example, the degradation model may be:

[0099]

[0100] Among them, if the time when the remaining life is triggered is t FPT , then t is t FPTt is a test time after the target component; s(t) represents the test data of the target component at the test time t; s0 is the initial test data of the target test item, which can be eliminated by differentiation; v is the degradation rate; σ is the degradation volatility intensity, which is a parameter that describes the degradation volatility intensity of the target component; B(t) is the standard Brownian motion.

[0101] According to the degradation model, the remaining life of the target component at the test time t obeys the probability distribution of the following life prediction model:

[0102]

[0103] Among them, Γ is the test value threshold of the current target test item, π is the pi, is the estimated result of v, is the estimated result of σ, τ is any time, f RUL (τ) is the probability at any time. The time corresponding to the maximum probability is the remaining life.

[0104] In step S402 , a plurality of test data obtained after at least two consecutive monitoring moments in a test data subset corresponding to any target test item is obtained to obtain a target data set.

[0105] In step S403, the remaining life of the target component is determined according to the remaining sub-life corresponding to at least one target test item.

[0106] In this embodiment, at the above-mentioned at least two consecutive monitoring moments, the corresponding characteristic indicators all meet the alarm conditions. At this time, an alarm can be issued and the remaining life can be calculated. By obtaining multiple test data obtained from the test data subset corresponding to any target test item after at least two consecutive monitoring moments, and using it as the target data set to predict the remaining life, the remaining life of the target component can be determined more accurately.

[0107] Figure 5 FIG. 1 is a flow chart showing another method for determining the remaining sub-lifetime according to an exemplary embodiment. Figure 5 As shown, in a possible implementation manner, determining the remaining sub-life of the target component corresponding to any target test item based on the target data set and the life prediction model may include the following steps:

[0108] In step S501 , parameter estimation is performed on the target data set to obtain the degradation rate and degradation volatility intensity.

[0109] In this embodiment, a logarithmic maximum likelihood function may be constructed, and the maximum likelihood estimation of parameters of the target data set may be performed using the logarithmic maximum likelihood function to obtain estimation results of the degradation rate and the degradation volatility intensity.

[0110] Among them, the logarithmic maximum likelihood function can be:

[0111]

[0112] Where, ΔS t For S t , Δt is the difference between the previous and next test times.

[0113] Through the logarithmic maximum likelihood function, the estimated results of the degradation rate and degradation volatility intensity can be obtained as follows:

[0114]

[0115] Where N is the length of the differential sequence.

[0116] In step S502, degradation results corresponding to different test moments are determined according to the degradation rate and the degradation fluctuation intensity.

[0117] In this embodiment, based on the degradation rate and degradation volatility intensity, Kalman filtering can be used to estimate the degradation state of the target component, and the degradation results corresponding to different test times can be obtained, which can be recorded as

[0118] In step S503, the degradation rate, degradation volatility intensity and degradation result are processed by the life prediction model to obtain the remaining sub-life of the target component corresponding to any target test item.

[0119] In this embodiment, the degradation rate, degradation volatility intensity, and degradation result are input into the life prediction model to obtain the remaining sub-life of the target component corresponding to any target test item.

[0120] In a possible implementation, the method for determining the remaining life of a target component based on the remaining sub-life corresponding to at least one target test item may be: determining the minimum value of the remaining sub-life corresponding to at least one target test item as the remaining life of the target component.

[0121] In this embodiment, for different target test items, the corresponding test data sets are different, and the corresponding feature indicator sets are also different, so the remaining life obtained is also different. The smallest remaining sub-life is taken as the remaining life of the target component. The aging time can be determined based on the remaining life, and according to the production line inspection plan, the inspection closest to the failure time is selected to maintain the target component, which can avoid the failure of the target component during use.

[0122] The above method for determining the remaining life of a target component based on a test data set continuously updates the test data set as test data is continuously acquired, thereby continuously outputting the remaining life of the target component. This allows for dynamic updating of the target component's remaining life, allowing for constant monitoring of the target component's remaining life after an alarm is triggered. Furthermore, there is no need to pre-collect a large amount of failure data specific to the target component to determine its lifespan, which can reduce costs.

[0123] Figure 6 FIG. 1 is a flow chart showing another method for determining the remaining life of a component according to an exemplary embodiment. Figure 6 As shown, in step S601, a target test item is determined.

[0124] In step S602, a test data subset corresponding to each target test item is obtained.

[0125] In step S603 , feature index extraction is performed on the test data subset of each target test item.

[0126] In step S604, the status monitoring data is initialized, the interval alarm counter l is recorded as 0, and the continuous alarm counter A is recorded as l =0.

[0127] In step S605 , second characteristic indicators corresponding to a plurality of consecutive historical monitoring moments are determined.

[0128] In step S606, an alarm threshold is determined.

[0129] In step S607, the current first characteristic index is determined.

[0130] In step S608, the alarm threshold is adaptively updated and it is determined whether to issue an alarm.

[0131] In step S609, the interval alarm counter 1 is updated, and the continuous alarm counter A is updated. l .

[0132] In step S610, it is determined whether there is A l -A l-1 ≥2, if yes, execute step S611, if no, execute step S612.

[0133] In step S611 , no remaining life prediction is required, and the process returns to step S608 .

[0134] In step S612, a degradation model is constructed, and then a life prediction model is constructed.

[0135] In step S613 , maximum likelihood estimation of parameters is performed on the target data set.

[0136] In step S614, a degradation result is obtained through Kalman filter state estimation.

[0137] In step S615, the remaining life is calculated.

[0138] In step S616 , predictive maintenance of the target component is performed based on the remaining life.

[0139] Figure 7 is a schematic diagram showing a prediction effect according to an exemplary embodiment. Figure 8 This is a schematic diagram showing another prediction effect according to an exemplary embodiment. Taking the target component as the probe as an example, the results are as follows: Figure 7 and Figure 8 As shown, among which, Figure 7 It can be seen that the method for determining the remaining life of components proposed in this disclosure can issue an alarm for probe abnormalities more than 400 test cycles before the probe fails, and has the ability to keenly identify abnormal probe status. Figure 8 It can be seen that when the calculation of the remaining life is triggered, the remaining life determination method of the component proposed in the present disclosure can iteratively perform parameter estimation, state estimation and remaining life calculation based on the test data acquired in real time, and the output remaining life prediction result continues to approach the actual remaining life of the probe, thereby providing the decision-making information necessary for the probe predictive maintenance operation.

[0140] Figure 9 1 is a block diagram of a device for determining the remaining life of a component according to an exemplary embodiment. Figure 9 The device 900 for determining the remaining life of a component includes an acquisition module 901 , a feature extraction module 902 and a first determination module 903 .

[0141] The acquisition module 901 is configured to acquire a test data set corresponding to a target component;

[0142] The feature extraction module 902 is configured to extract features from the test data set to obtain a feature index set, wherein the feature index set includes feature indexes corresponding to multiple monitoring moments;

[0143] The first determining module 903 is configured to determine the remaining life of the target component according to the test data set when the characteristic indicators in the characteristic indicator set meet the alarm condition.

[0144] Optionally, the test data set includes test data subsets corresponding to multiple target test items;

[0145] The feature extraction module 902 includes:

[0146] The feature extraction submodule is configured to perform feature extraction on the test data subset corresponding to each target test item to obtain a feature index set corresponding to each target test item;

[0147] The first determining module 903 includes:

[0148] A first determination submodule is configured to determine, for a characteristic indicator set corresponding to any target test item, a remaining sub-life of the target component corresponding to the any target test item based on a test data subset corresponding to the any target test item when the characteristic indicators corresponding to at least two consecutive monitoring moments satisfy an alarm condition;

[0149] The second determining submodule is configured to determine the remaining life of the target component according to the remaining sub-life corresponding to at least one target test item.

[0150] Optionally, the feature extraction submodule includes:

[0151] A first obtaining unit is configured to extract a test data subset corresponding to any target test item through a sliding window to obtain multiple groups of candidate test data, and extract a group of candidate test data at each monitoring moment;

[0152] The feature extraction unit is configured to perform feature extraction on each set of candidate test data to obtain feature indicators corresponding to multiple monitoring moments.

[0153] Optionally, the device 900 for determining the remaining life of a component further includes:

[0154] A second determination module is configured to determine a first characteristic indicator and an alarm threshold corresponding to a current monitoring moment according to a characteristic indicator set corresponding to any target test item;

[0155] The third determining module is configured to determine that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition when the first characteristic indicator is less than the alarm threshold.

[0156] Optionally, the second determining module includes:

[0157] A first acquisition submodule is configured to acquire a first characteristic indicator corresponding to a current monitoring moment from a characteristic indicator set corresponding to any target test item;

[0158] A second acquisition submodule is configured to acquire second characteristic indicators corresponding to a plurality of consecutive historical monitoring moments before the current monitoring moment;

[0159] The third determining submodule is configured to determine the alarm threshold according to the mean and standard deviation of the second characteristic indicator.

[0160] Optionally, the first determining submodule includes:

[0161] a second obtaining unit configured to determine a degradation model corresponding to the target component and obtain a life prediction model based on the degradation model;

[0162] A third obtaining unit is configured to obtain a plurality of test data obtained after the at least two consecutive monitoring moments in the test data subset corresponding to any target test item to obtain a target data set;

[0163] The first determining unit is configured to determine the remaining sub-lifetime of the target component corresponding to any target test item according to the target data set and the life prediction model.

[0164] Optionally, the first determining unit includes:

[0165] a first obtaining subunit, configured to perform parameter estimation on the target data set to obtain a degradation rate and a degradation volatility intensity;

[0166] a determination subunit, configured to determine degradation results corresponding to different test moments according to the degradation rate and the degradation volatility intensity;

[0167] The second obtaining subunit is configured to process the degradation rate, the degradation volatility intensity and the degradation result through the life prediction model to obtain the remaining sub-life of the target component corresponding to any target test item.

[0168] Optionally, the acquisition module 901 includes:

[0169] a fourth determining submodule, configured to determine at least one target test item related to the target component;

[0170] The third acquisition submodule is configured to respectively acquire a test data set corresponding to each target test item. The test data set corresponding to any target test item is obtained by periodically testing the target component using the test method corresponding to the target test item.

[0171] Optionally, the second determining submodule includes:

[0172] The second determining unit is configured to determine a minimum value among the remaining sub-lifetimes corresponding to the at least one target test item as the remaining lifetime of the target component.

[0173] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0174] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the method for determining the remaining life of a component provided by the present disclosure are implemented.

[0175] In another exemplary embodiment, a computer program product is provided. The computer program product includes a computer program executable by a programmable device, and has a code portion for executing the above-mentioned method for determining the remaining lifetime of a component when executed by the programmable device.

[0176] Figure 10 1 is a block diagram of a device for determining the remaining life of a component according to an exemplary embodiment. For example, the device 1000 for determining the remaining life of a component can be provided as a server. Figure 10 The apparatus 1000 for determining the remaining life of a component includes a processing component 1022, which further includes one or more processors, and memory resources represented by a memory 1032 for storing instructions, such as an application, executable by the processing component 1022. The application stored in the memory 1032 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1022 is configured to execute the instructions to perform the aforementioned method for determining the remaining life of a component.

[0177] The device 1000 for determining the remaining life of a component may further include a power supply component 1026 configured to perform power management of the device 1000 for determining the remaining life of a component, a wired or wireless network interface 1050 configured to connect the device 1000 for determining the remaining life of a component to a network, and an input / output interface 1058. The device 1000 for determining the remaining life of a component may operate based on an operating system stored in the memory 1032, such as Windows Server 2003. TM , Mac OS X TM , Unix TM , Linux TM , FreeBSD TM or similar.

[0178] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the present disclosure. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0179] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for determining the remaining life of a component, characterized in that: include: Obtain the test data set corresponding to the target component; Performing feature extraction on the test data set to obtain a feature index set, wherein the feature index set includes feature indexes corresponding to multiple monitoring moments, wherein the feature index corresponding to a monitoring moment includes a root mean square of a group of candidate test data corresponding to the monitoring moment; Determining, based on the characteristic indicator set, a first characteristic indicator and an alarm threshold corresponding to a current monitoring moment, wherein the alarm threshold is obtained based on second characteristic indicators corresponding to a plurality of consecutive historical monitoring moments before the current monitoring moment; When the first characteristic indicator is less than the alarm threshold, determining that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition; When the characteristic indicators in the characteristic indicator set meet the alarm condition, the remaining life of the target component is determined according to the test data set.

2. The method for determining the remaining life of a component according to claim 1, wherein: The test data set includes test data subsets corresponding to multiple target test items; The feature extraction of the test data set to obtain a feature index set includes: Perform feature extraction on the test data subset corresponding to each target test item to obtain a feature index set corresponding to each target test item; When the characteristic indicators in the characteristic indicator set meet the alarm condition, determining the remaining life of the target component according to the test data set includes: For a characteristic indicator set corresponding to any target test item, if the characteristic indicators corresponding to at least two consecutive monitoring moments satisfy an alarm condition, determining the remaining sub-life of the target component corresponding to the any target test item based on the test data subset corresponding to the any target test item; The remaining life of the target component is determined according to the remaining sub-life corresponding to at least one target test item.

3. The method for determining the remaining life of a component according to claim 2, wherein: The feature extraction is performed on the test data subset corresponding to each target test item to obtain the feature indicator set corresponding to each target test item, including: For a test data subset corresponding to any target test item, extract the test data subset through a sliding window to obtain multiple groups of candidate test data, and extract a group of candidate test data at each monitoring moment; Feature extraction is performed on each set of candidate test data to obtain feature indicators corresponding to multiple monitoring moments.

4. The method for determining the remaining life of a component according to claim 2, wherein: Determining the first characteristic indicator and the alarm threshold corresponding to the current monitoring moment according to the characteristic indicator set includes: Obtaining a first characteristic indicator corresponding to the current monitoring moment from the characteristic indicator set corresponding to any target test item; Obtaining second characteristic indicators corresponding to multiple consecutive historical monitoring moments before the current monitoring moment; The alarm threshold is determined according to the mean and standard deviation of the second characteristic indicator.

5. The method for determining the remaining life of a component according to claim 2, wherein: The determining, based on the test data subset corresponding to the any target test item, the remaining sub-life of the target component corresponding to the any target test item includes: Determining a degradation model corresponding to the target component, and obtaining a life prediction model based on the degradation model; Acquire multiple test data obtained after the at least two consecutive monitoring moments in the test data subset corresponding to any target test item to obtain a target data set; The remaining sub-life of the target component corresponding to any target test item is determined according to the target data set and the life prediction model.

6. The method for determining the remaining life of a component according to claim 5, wherein: The determining, based on the target data set and the life prediction model, the remaining sub-life of the target component corresponding to any target test item includes: Performing parameter estimation on the target data set to obtain a degradation rate and a degradation volatility intensity; Determining degradation results corresponding to different test moments according to the degradation rate and the degradation volatility intensity; The degradation rate, the degradation volatility intensity and the degradation result are processed by the life prediction model to obtain the remaining sub-life of the target component corresponding to any target test item.

7. The method for determining the remaining life of a component according to any one of claims 2 to 6, characterized in that: The obtaining of a test data set corresponding to the target component includes: determining at least one target test item related to the target component; A test data set corresponding to each target test item is obtained respectively. The test data set corresponding to any target test item is obtained by periodically testing the target component using the test method corresponding to the target test item.

8. The method for determining the remaining life of a component according to any one of claims 2 to 6, characterized in that: The determining the remaining life of the target component according to the remaining sub-life corresponding to at least one target test item includes: The minimum value of the remaining sub-lifespans corresponding to the at least one target test item is determined as the remaining lifespan of the target component.

9. A device for determining the remaining life of a component, characterized in that: include: An acquisition module is configured to acquire a test data set corresponding to a target component; a feature extraction module configured to perform feature extraction on the test data set to obtain a feature indicator set, wherein the feature indicator set includes feature indicators corresponding to multiple monitoring moments, wherein the feature indicator corresponding to a monitoring moment includes the root mean square value of a group of candidate test data corresponding to the monitoring moment; a second determination module configured to determine, based on the feature indicator set, a first feature indicator and an alarm threshold corresponding to the current monitoring moment, wherein the alarm threshold is obtained based on the second feature indicators corresponding to multiple consecutive historical monitoring moments before the current monitoring moment; a third determining module, configured to determine that the first characteristic indicator corresponding to the current monitoring moment meets the alarm condition when the first characteristic indicator is less than the alarm threshold; The first determining module is configured to determine the remaining life of the target component according to the test data set when the characteristic indicators in the characteristic indicator set meet the alarm condition.

10. A device for determining the remaining life of a component, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the steps of the method for determining the remaining life of a component as described in any one of claims 1 to 8 when executing.

11. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the steps of the method for determining the remaining life of a component according to any one of claims 1 to 8 are implemented.

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