Vacuum circuit breaker mechanical fault diagnosis method and system based on current waveform

Abstract

The application discloses a kind of vacuum circuit breaker mechanical fault diagnosis method and system based on current waveform, belong to electric power equipment fault diagnosis field.The method includes: obtaining opening and closing coil current waveform data;Determine the waveform section of which current amplitude is lower than preset threshold;First smoothing waveform section is generated based on first smoothing parameter, and second smoothing waveform section is generated based on second smoothing parameter greater than first smoothing parameter;The number of local extreme points in two smoothing waveform sections is respectively counted, and the change characteristics of local extreme points number along with the increase of smoothing parameter are obtained to obtain strong attenuation, weak attenuation or non-attenuation characteristics;The change characteristics are matched with the preset fault change characteristics established in advance, and the mechanical fault type of vacuum circuit breaker is determined.Through the extraction of local extreme point attenuation characteristics under double smoothing scales, different mechanical faults such as spring fatigue and transmission jamming can be accurately distinguished, and the reliability and adaptive ability of diagnosis are improved.

Description

Technical Field

[0001] This application relates to the field of power equipment fault diagnosis technology, and in particular to a method and system for diagnosing mechanical faults in vacuum circuit breakers based on current waveforms. Background Technology

[0002] Vacuum circuit breakers are crucial switching devices in power systems, and the reliability of their mechanical structure directly impacts the safe operation of the power system. The current waveforms of the opening and closing coils contain rich information about the mechanical state; analyzing these current waveforms allows for non-invasive diagnosis of mechanical faults in the circuit breaker.

[0003] In existing technologies, fault types are often determined by extracting time-domain features or performing spectral analysis on current waveforms. However, traditional methods often employ single-scale signal analysis, making it difficult to simultaneously consider both macroscopic trends and microscopic fluctuations in the waveform. This results in insufficient ability to distinguish between different mechanical faults, such as spring fatigue, transmission jamming, and core sticking, thus limiting diagnostic accuracy. Summary of the Invention

[0004] This application provides a method and system for diagnosing mechanical faults in vacuum circuit breakers based on current waveforms, in order to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application proposes a method for diagnosing mechanical faults in vacuum circuit breakers based on current waveforms. This method is applied to a system for diagnosing mechanical faults in vacuum circuit breakers based on current waveforms. The system includes a control terminal, and the method is executed by the control terminal. The method includes: acquiring coil current waveform data during the opening and closing process of the vacuum circuit breaker; determining waveform segments with current amplitudes below a preset threshold based on the coil current waveform data; determining a first smoothed waveform segment based on the waveform segment and a first smoothing parameter; determining a second smoothed waveform segment based on the waveform segment and a second smoothing parameter, wherein the second smoothing parameter is greater than the first smoothing parameter; determining the number of first local extrema points based on the comparison of the current amplitude of each first data point with its adjacent first data points, and determining the number of second local extrema points based on the comparison of the current amplitude of each second data point with its adjacent second data points; determining the variation characteristics of the number of local extrema points as the smoothing parameter increases based on the number of first and second local extrema points; and determining the mechanical fault type of the vacuum circuit breaker based on the matching result between the variation characteristics and preset fault variation characteristics.

[0007] Therefore, by obtaining the number of local extrema at different smoothing scales through dual smoothing parameters and using the change characteristics of the number of extrema as the smoothing parameters increase for fault diagnosis, it is possible to enhance the ability to extract the fluctuation characteristics of different scales in the current waveform, effectively distinguish different types of mechanical faults, and improve the reliability and accuracy of diagnosis.

[0008] In conjunction with the first aspect, optionally, based on the coil current waveform data, the waveform segment with the current amplitude lower than a preset threshold is determined, including: determining the median of the current amplitude based on the current amplitude of all data points in the coil current waveform data; determining the interquartile range of the current amplitude based on the current amplitude of all data points; and determining the preset threshold as the result of subtracting twice the interquartile range of the current amplitude from the median of the current amplitude.

[0009] Therefore, it can be seen that setting the threshold adaptively based on the median and interquartile range of the waveform can adapt to the waveform characteristics under different circuit breakers and operating conditions, accurately capture the low amplitude section, and avoid the problem of poor adaptability of fixed threshold.

[0010] In conjunction with the first aspect, optionally, based on the coil current waveform data, determining the waveform segment where the current amplitude is lower than a preset threshold includes: determining an initial segment in the coil current waveform data consisting of data points where the current amplitude is continuously lower than the preset threshold; determining the duration of the initial segment based on the number of data points contained in the initial segment and the sampling rate of the coil current waveform data; and determining the initial segment with a duration greater than a preset duration threshold as the waveform segment.

[0011] Therefore, by filtering over a continuous duration, transient interference signals can be eliminated, ensuring that the selected waveform segment corresponds to the actual low-amplitude phase during the circuit breaker's opening and closing process.

[0012] In conjunction with the first aspect, optionally, the first smoothing parameter and the second smoothing parameter are determined based on the degree of fluctuation of the current amplitude of data points within the waveform segment, and include: obtaining the absolute value of the difference of the current amplitude of all adjacent data points within the waveform segment, and determining the average value of the absolute values ​​as the average amplitude fluctuation amount; setting the first smoothing parameter to an integer not less than the average amplitude fluctuation amount; and setting the second smoothing parameter to 2n times the first smoothing parameter, where n is an integer greater than or equal to 1.

[0013] Therefore, it can be seen that adaptively determining the smoothing parameters based on the local fluctuation of the current amplitude can balance noise suppression and detail preservation, making waveform segments under different smoothing levels more accurately reflect the structural characteristics of the original signal, thereby improving the effectiveness of the change characteristics.

[0014] In conjunction with the first aspect, optionally, the first smoothed waveform segment is determined based on the waveform segment and the first smoothing parameter, including: taking a set of continuous data points within the waveform segment centered on each data point and spanning the first smoothing parameter as a smoothing window, determining the median of the current amplitude of all data points within the smoothing window, and taking the median as the corresponding current amplitude of the center data point in the first smoothed waveform segment; wherein, the first smoothed waveform segment is formed sequentially by the corresponding current amplitude of each center data point.

[0015] Therefore, it can be seen that using median smoothing to generate the first smooth waveform segment can effectively suppress impulse noise while preserving the edge and local extremum information in the waveform, which is beneficial for the stable extraction of subsequent local extremum points.

[0016] In conjunction with the first aspect, optionally, based on the comparison results of the current amplitudes of each first data point with the preceding and following adjacent first data points, the number of first local extrema points is determined, including: when the current amplitude of any first data point is greater than the sum of the current amplitudes of its preceding adjacent first data points and a preset tolerance, and is also greater than the sum of the current amplitudes of its following adjacent first data points and a preset tolerance, then the first data point is determined as a local maximum point; when the current amplitude of any first data point is less than the difference between the amplitudes of its preceding adjacent first data points and a preset tolerance, and is also less than the difference between the current amplitudes of its following adjacent first data points and a preset tolerance, then the first data point is determined as a local minimum point; the total number of local maximum points and local minimum points within the first smooth waveform segment is determined and used as the number of first local extrema points.

[0017] Therefore, by introducing a local extreme point determination method with a preset tolerance, we can eliminate pseudo extreme points caused by small random fluctuations, improve the robustness of local extreme point statistics, and thus make the change characteristics more realistically reflect the essential structure of the signal.

[0018] In conjunction with the first aspect, optionally, the preset tolerance is determined by: obtaining the difference between the maximum amplitude and the minimum amplitude of the data points within the waveform segment; and reducing the difference according to a preset ratio to obtain the preset tolerance.

[0019] Therefore, by introducing a local extreme point determination method with a preset tolerance, we can eliminate pseudo extreme points caused by small random fluctuations, improve the robustness of local extreme point statistics, and thus make the change characteristics more realistically reflect the essential structure of the signal.

[0020] In conjunction with the first aspect, optionally, based on the number of first local extrema and the number of second local extrema, the characteristic of the change in the number of local extrema as the smoothing parameter increases is determined, including: comparing the number of first local extrema and the number of second local extrema; obtaining the parameter difference between the second smoothing parameter and the first smoothing parameter; when the number of second local extrema is less than the number of first local extrema and the parameter difference is greater than twice the first smoothing parameter, the characteristic of change is determined to be a strong attenuation characteristic; when the number of second local extrema is less than the number of first local extrema and the parameter difference is not greater than twice the first smoothing parameter, the characteristic of change is determined to be a weak attenuation characteristic; when the number of second local extrema is not less than the number of first local extrema, the characteristic of change is determined to be a non-attenuation characteristic.

[0021] Therefore, defining strong and weak attenuation characteristics based on the first smoothing parameter standardizes the attenuation assessment under different smoothing spans, avoids the scale effect of absolute parameter differences, makes the variation characteristics more universal and distinguishable, and thus improves the accuracy of fault type identification.

[0022] In conjunction with the first aspect, optionally, the preset fault change characteristics are established in advance in the following manner: Coil current waveform samples of vacuum circuit breakers with various known mechanical fault types are collected during the opening and closing process; change characteristics are extracted for each sample; the extracted change characteristics are used as preset characteristics associated with the mechanical fault type; wherein, the preset fault change characteristics include preset characteristics corresponding to strong attenuation characteristics, weak attenuation characteristics, and non-attenuation characteristics, respectively; based on the matching results of the change characteristics and the preset fault change characteristics, the mechanical fault type of the vacuum circuit breaker is determined, including: comparing the change characteristics with the preset characteristics to determine the matching preset characteristics; and determining the mechanical fault type of the vacuum circuit breaker based on the mechanical fault type associated with the matching preset characteristics.

[0023] Therefore, by collecting known fault samples to establish a mapping relationship between changing characteristics and fault types in advance, and performing matching diagnosis based on this mapping, we can make full use of prior knowledge, improve diagnostic accuracy, and facilitate system expansion to cover new fault modes.

[0024] In a second aspect, this invention proposes a mechanical fault diagnosis system for vacuum circuit breakers based on current waveforms. The system includes:

[0025] The acquisition module is used to acquire coil current waveform data during the opening and closing process of the vacuum circuit breaker.

[0026] The first determining module is used to determine the waveform segment where the current amplitude is lower than a preset threshold based on the coil current waveform data.

[0027] The second determining module is used to determine the first smoothed waveform segment based on the waveform segment and the first smoothing parameter;

[0028] The third determining module is used to determine the second smoothed waveform segment based on the waveform segment and the second smoothing parameter, wherein the second smoothing parameter is greater than the first smoothing parameter;

[0029] The fourth determining module is used to determine the number of first local extreme points based on the comparison results of the current amplitude of each first data point with the first data points before and after it, and to determine the number of second local extreme points based on the comparison results of the current amplitude of each second data point with the second data points before and after it.

[0030] The fifth determining module is used to determine the variation characteristics of the number of local extreme points as the smoothing parameter increases, based on the number of the first local extreme points and the number of the second local extreme points.

[0031] The sixth determination module is used to determine the mechanical fault type of the vacuum circuit breaker based on the matching results between the changing characteristics and the preset fault changing characteristics.

[0032] A third aspect of the present invention provides a processing apparatus, the processing apparatus comprising:

[0033] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0034] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms, as proposed in this application.

[0036] Figure 2 This is a schematic diagram of the structure of a vacuum circuit breaker mechanical fault diagnosis system based on current waveform proposed in this application.

[0037] Figure 3 This is a schematic diagram of the structure of a processing device proposed in this application. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] This application proposes a mechanical fault diagnosis method for vacuum circuit breakers based on current waveforms, applied to a mechanical fault diagnosis system for vacuum circuit breakers based on current waveforms. The system includes a control terminal, and the method is executed by the control terminal, including:

[0040] S101: Acquire coil current waveform data during the opening and closing process of the vacuum circuit breaker.

[0041] Understandably, when a circuit breaker performs an opening or closing operation, a current sensor collects the current signal of the opening and closing coils in real time, and then converts it into coil current waveform data after analog-to-digital conversion. The sampling rate of the data is usually determined according to the rate of change of the current signal, for example, set to 10kHz or higher to ensure that sufficient waveform details are captured. The acquired waveform data is a series of discrete data points, each containing a timestamp and the corresponding current amplitude.

[0042] S102: Based on the coil current waveform data, determine the waveform segment where the current amplitude is lower than the preset threshold.

[0043] Understandably, not all stages of the current waveform in the opening and closing coils are sensitive to mechanical faults. This embodiment selects a segment with a low current amplitude that lasts for a certain period of time as the analysis object. This segment often corresponds to the commutation or jamming sensitive stage of the core movement and contains mechanical state information.

[0044] Specifically, preset thresholds can be automatically set based on statistical methods.

[0045] First, calculate the median M of the current amplitude across all data points in the coil current waveform data.

[0046] The current amplitude values ​​corresponding to all data points in the collected opening and closing coil current waveform data, i.e., the current magnitude of each sampling point, are sorted according to their numerical values. The value in the middle position after sorting is taken as the median current amplitude. If the total number of data points is odd, the median is the value in the exact middle; if it is even, the average of the two middle values ​​is usually taken.

[0047] Compared to using the arithmetic mean, the median is less sensitive to transient large-amplitude pulses or localized abnormally high amplitudes in the waveform, and can more robustly reflect the typical amplitude level of the overall waveform. For example, in a single closing current waveform, the current amplitude fluctuates around 0.5A for most of the time, with only a brief 2A pulse appearing at the moment the iron core is triggered; if the average value is used, this pulse will cause the average value to be too high, while the median can remain around 0.5A, providing a stable reference for subsequent threshold setting.

[0048] Next, the interquartile range (IQR) of the current amplitude at all data points is calculated. Understandably, the purpose of this step is to quantify the amplitude dispersion of the entire current waveform, that is, the range of fluctuation of the amplitude of each data point relative to the median. First, the first quartile (Q1), which is the amplitude of the data point at the 25th percentile after sorting, and the third quartile (Q3), which is the amplitude of the data point at the 75th percentile, are determined based on the sorted amplitude data. Then, the interquartile range (IQR) is calculated as IQR = Q3 - Q1. The interquartile range essentially reflects the amplitude span covering the middle 50% of the data points and is a robust statistical measure of deviation.

[0049] Compared to traditional standard deviation or variance, interquartile range (IQR) is also unaffected by extreme values ​​or outliers, requiring only data sorting information. It is simple to calculate and has strong anti-interference capabilities. Continuing with the waveform example above, although there are 2A pulses, the vast majority of data points are concentrated between 0.2A and 0.8A. Therefore, Q1 is approximately 0.3A, Q3 is approximately 0.7A, and IQR = 0.4A, objectively describing the amplitude fluctuation range of the main data.

[0050] Finally, the preset threshold is determined as the median current amplitude minus twice the interquartile range of the current amplitude, resulting in the preset threshold T = M - 2 * IQR. It is understandable that when the amplitude of a data point is lower than the median minus 1.5 times IQR, it can be considered that the point significantly deviates from the main distribution range of the data and belongs to a low-amplitude anomaly. This embodiment selects 2 times IQR as the multiplier to appropriately widen the distance between the threshold and the median, so that the threshold can cover normal low-amplitude fluctuations in the waveform, while effectively capturing persistent low-amplitude segments caused by mechanical faults, such as core jamming causing the current to remain low for a long time. For example, if the median M = 0.52A and IQR = 0.08A, then T = 0.52 - 0.16 = 0.36A. Subsequently, data points continuously below 0.36A can be identified as candidate waveform segments.

[0051] This adaptive threshold determination method eliminates the need for manual threshold setting for each waveform based on experience. It automatically adjusts the current waveform for different circuit breaker models and operating conditions (opening or closing), improving the versatility and accuracy of section extraction. Furthermore, since the median and interquartile range are insensitive to isolated pulses, the set thresholds also have strong anti-interference capabilities, preventing the misjudgment of transient noise fluctuations as valid sections.

[0052] As an alternative implementation, the preset threshold can also be calculated based on the mean and standard deviation of the waveform amplitude, or a preset fixed empirical value can be directly used. However, the above-mentioned scheme based on the median and interquartile range has advantages in terms of anti-pulse interference and adaptive capability, and is especially suitable for coil current waveforms acquired in complex electromagnetic environments on site.

[0053] Then, an initial segment is determined, consisting of data points whose current amplitude is continuously below a preset threshold T. For example, by iterating through all data points, an initial segment is formed when a point with an amplitude less than 0.36A is detected, and subsequent consecutive points are also below this threshold. Next, the duration Δt = N / fs is calculated based on the number of data points N in the initial segment and the sampling rate fs. If Δt is greater than the preset duration threshold, the initial segment is determined as the final waveform segment; otherwise, it is discarded. In this way, pseudo-segments formed by instantaneous glitches or short-term interference can be eliminated, ensuring that the selected waveform segment corresponds to the actual mechanical motion stage. For example, if the duration of an initial segment is 2ms, which is less than the threshold of 5ms, it will not be used.

[0054] S103: Determine the first smoothed waveform segment based on the waveform segment and the first smoothing parameter.

[0055] S104: Determine the second smoothed waveform segment based on the waveform segment and the second smoothing parameter, wherein the second smoothing parameter is greater than the first smoothing parameter.

[0056] Understandably, the waveform segment is smoothed using a first smoothing parameter and a second smoothing parameter, respectively, to generate a first smoothed waveform segment and a second smoothed waveform segment. The values ​​of the two smoothing parameters directly affect the decay characteristics of the number of subsequent extreme points. This embodiment employs an adaptive determination method based on the degree of fluctuation in the current amplitude of the waveform segment itself.

[0057] Specifically, the first smoothing parameter and the second smoothing parameter are determined based on the degree of fluctuation of the current amplitude at data points within the waveform segment, and may include the following steps:

[0058] S201: Obtain the absolute value of the current amplitude difference between all adjacent data points within the waveform segment, and determine the average value of the absolute values ​​as the average amplitude fluctuation.

[0059] Understandably, within a defined waveform segment, starting from the first data point, the current amplitude difference between each pair of adjacent data points is calculated sequentially, and its absolute value is taken. Then, all absolute values ​​are summed and divided by the total number of adjacent point pairs to obtain the average amplitude fluctuation. This indicator reflects the average fluctuation of the signal between adjacent sampling points within the waveform segment. The larger the fluctuation, the richer the high-frequency details or noise in the waveform; the smaller the fluctuation, the smoother the waveform.

[0060] S202: Set the first smoothing parameter to an integer not less than the average amplitude fluctuation.

[0061] Specifically, this step determines the smaller first smoothing parameter. Since the smoothing parameter represents the span of the smoothing window in practical use, i.e., the number of consecutive data points participating in median filtering, it must be an integer. Therefore, the average amplitude fluctuation obtained in step one is directly assigned to the median by rounding up or selecting the smallest integer value not less than it. .

[0062] For example, if the average amplitude fluctuation is 0.10A, then the smallest integer not less than 0.10 is 1. If the unit of fluctuation is A, and the value is small, it can be directly taken as 1. Alternatively, in actual engineering, the average amplitude fluctuation can be appropriately amplified according to the current amplitude dimension before rounding.

[0063] Under a more reasonable quantization setting, assuming that the average amplitude fluctuation of the waveform segment after digitization is 2.7 quantization units, then The value is 3. The purpose of this is to ensure that the span of the smoothing window at least covers the average fluctuation scale, so that the first smoothed waveform segment can smooth the random fluctuations of the average level while preserving the main waveform structure, and avoid insufficient smoothing that would lead to too many pseudo-extreme points in the subsequent extreme point statistics.

[0064] S203: Set the second smoothing parameter to 2n times the first smoothing parameter, where n is an integer greater than or equal to 1.

[0065] This step is used to determine the larger second smoothing parameter. and with Establish a fixed multiple relationship. Here, n is a preset positive integer, typically 1, 2, or 3, selected based on the degree of multi-scale variability required for diagnosis. For example, when... When n=3 and n=2, =12. This setting has two functions:

[0066] First, guarantee Much larger This makes the second smooth waveform segment significantly different from the first smooth waveform segment in terms of smoothness, thus enabling the observation of the attenuation of the number of local extrema at different smoothing scales.

[0067] Secondly, with Define a multiplication factor relationship based on the baseline to make the smooth span between different waveform segments relatively comparable, especially when When adapting to change, This also changes proportionally, thus ensuring a unified standard for the subsequently extracted strong attenuation, weak attenuation, and other variation characteristics, unaffected by differences in the amplitude range or absolute value of the original waveform's fluctuations. For example, for waveforms with violent fluctuations, Larger Setting the value to 40 by a factor of 4 does not change the attenuation characteristic judgment condition, and the classification boundary will not fail due to the difference in absolute values.

[0068] In one example, the waveform segment contains 150 data points, and Δ is calculated. =2.1, then If n is 3; if n is 2, then =12. In an alternative implementation, the smoothing parameter can also be preset to a fixed pair of values, such as... =5, =15, etc., but the adaptive approach can improve the versatility of the method.

[0069] For each data point in the waveform segment, take (k-1) / 2 points to the left and right of that point as the center, where k is the smoothing parameter. If k is even, it can be agreed to extend upwards or to use a delimitation rule to form a continuous set of data points with a span of k as a smoothing window.

[0070] Extract the median of the current amplitude of all data points within the window and use it as the corresponding current amplitude of the center data point in the new smooth waveform segment; sequentially traverse all data points to obtain the smooth waveform segment composed of the amplitudes corresponding to each center point.

[0071] Alternatively, the smoothing process can also employ mean filtering or Gaussian weighted filtering. The advantage of median filtering is that it is insensitive to impulse noise and can better preserve edge information, which is more conducive to the robust detection of subsequent extreme points.

[0072] S105: Based on the comparison results of the current amplitude of each first data point with the first data points before and after it, determine the number of first local extreme points, and based on the comparison results of the current amplitude of each second data point with the second data points before and after it, determine the number of second local extreme points.

[0073] Understandably, the number of local extreme points is counted in both the first and second smoothed waveform segments. This embodiment employs an extreme point determination method based on a preset tolerance to eliminate pseudo-extreme points caused by small random fluctuations.

[0074] The preset tolerance δ is determined by the amplitude range of the waveform segment itself:

[0075] Obtain the maximum amplitude of all data points within the waveform segment. With minimum amplitude ;

[0076] Calculate the amplitude difference Δ = - ;

[0077] Set δ to Δ Multiply by a preset ratio p, where p can be any value between 0.02 and 0.1. For example, Δ =2.0A, p=0.05, then δ=0.1A.

[0078] Next, for each first data point in the first smooth waveform segment, the following determination is performed:

[0079] If the current amplitude of a certain first data point is greater than the amplitude of the preceding first data point + δ, and greater than the amplitude of the following first data point + δ, then it is marked as a local maximum point.

[0080] If the current amplitude of a certain first data point is less than the amplitude of the preceding first data point minus δ, and less than the amplitude of the following first data point minus δ, then it is marked as a local minimum point.

[0081] The total number of all marked points is counted to obtain the number of first local extrema points. .

[0082] Similarly, the number of second local extrema points is statistically obtained in the second smooth waveform segment. .

[0083] By incorporating tolerance, local fluctuations with small amplitude changes can be effectively ignored, making the statistical results more reliably reflect the main structural features of the waveform. In the example above, the first smoothed waveform segment may detect 5 local extreme points, while the second smoothed waveform segment, due to its higher smoothness, further smooths out fluctuations with relatively small amplitude changes, and may only have 2 extreme points.

[0084] Alternatively, local extremum detection can also enhance noise suppression by comparing multiple points consecutively, such as requiring at least two points before and after the same point to be maximum / minimum, or by using dynamic thresholding methods based on extremum significance indices.

[0085] S106: Based on the number of the first local extreme points and the number of the second local extreme points, determine the variation characteristics of the number of local extreme points as the smoothing parameter increases.

[0086] Understandably, the variation characteristics reflect the degree of attenuation of the number of local extrema as the smoothing parameter increases, and are used to characterize the stability of the signal structure within the waveform segment.

[0087] Specifically, it may include the following steps:

[0088] S1061: Compare the number of the first local extrema with the number of the second local extrema.

[0089] The number of local extrema points obtained from the first smooth waveform segment The number of local extrema points obtained statistically from the second smooth waveform segment Perform a numerical comparison. This comparison clarifies how the smoothing parameter changes from a smaller initial smoothing parameter to a larger one. Increase to a larger second smoothing parameter The question is whether the total number of local extrema has decreased, remained unchanged, or even increased. This trend in quantity is the basis for subsequently distinguishing the attenuation type. For example, if 8 local extrema are detected in the first smooth waveform segment and 3 extrema are detected in the second smooth waveform segment, then... < The number of extreme points is decreasing, indicating that smoothing on a larger scale has smoothed out some local fluctuations.

[0090] S1062: Obtain the parameter difference between the second smoothing parameter and the first smoothing parameter.

[0091] The second smoothing parameter With the first smoothing parameter Subtracting them, we get the parameter difference Δk= - The magnitude of this difference directly reflects the degree of difference in window coverage between the two smoothing operations. For example, when =3, When =12, Δk=9; when =5, When Δk = 10, Δk = 5. The reason for obtaining this difference is that the decay of the number of extreme points must be comprehensively evaluated in conjunction with the relative size of the smooth span. A simple reduction in value is insufficient to provide stable characteristics for fault diagnosis, and the same amount of reduction may have completely different meanings under different smooth spans.

[0092] S1063: When the number of second local extreme points is less than the number of first local extreme points, and the parameter difference is greater than twice the first smoothing parameter, the change feature is determined to be a strong attenuation feature.

[0093] Specifically, this step defines the criteria for determining a strong attenuation feature. The feature is output when both conditions are met simultaneously.

[0094] Condition one: < That is, the number of extreme points does indeed decrease due to the enhanced smoothing;

[0095] Condition 2: Δk>2× That is, the increase in the smoothing parameter is more than twice that of the first smoothing parameter itself.

[0096] The second smoothing window covers a much larger area than the first, and the two operate at significantly different analytical scales. The decrease in the number of observed extreme points indicates that the original waveform contained a large number of extreme points that only existed within a small local scale. These extreme points are highly sensitive to the expansion of the smoothing span; once the smoothing window is large enough, many of these extreme points disappear. This characteristic often corresponds to a waveform rich in high-frequency, low-amplitude fluctuations but with an unstable macroscopic structure. In vacuum circuit breaker diagnosis, this typically corresponds to fatigue faults in the trip spring: spring fatigue causes many irregular short-term fluctuations in the vibration signal. When the smoothing scale increases, these fluctuations are quickly eliminated, and the number of extreme points decreases sharply. For example, =3, =12, Δk=9>6, and =10, If the value is 2, it is determined to be a strong attenuation characteristic.

[0097] S1064: When the number of second local extreme points is less than the number of first local extreme points, and the parameter difference is not greater than twice the first smoothing parameter, the change feature is determined to be a weak decay feature.

[0098] Specifically, this step defines the weak attenuation characteristic. It also requires... < However, at this point, the parameter difference Δk is small, satisfying Δk≤2× This means that although the smoothing parameter has increased, the increase is relatively limited, and the second smoothing operation only covers a slightly larger neighborhood than the first smoothing. Under this relatively gentle scale change, the number of extreme points has decreased, indicating that even with a small increase in the smoothing span, some local extreme points in the original waveform are filtered out. This suggests that the waveform's inherent structure is relatively sensitive to the initial stage of the smoothing operation, but not to the large span sensitivity of strong attenuation. This type of characteristic can correspond to faults such as mild fatigue of the closing spring or slight clearance in the transmission mechanism, which increase local fluctuations in the waveform, but the amplitude is not large. For example, =4, =8, Δk=4≤8, and =7, =4, indicating a weak attenuation characteristic.

[0099] S1065: When the number of second local extrema is not less than the number of first local extrema, the change characteristic is determined to be a non-decaying characteristic.

[0100] Specifically, this step defines the non-decaying characteristic. When ≥ At this time, regardless of the magnitude of the parameter difference, it falls under this characteristic. This situation indicates that increasing the smoothing parameter does not reduce the number of local extrema; in fact, it may even slightly increase new extrema due to the change in the relative high and low relationships of the waveform caused by median smoothing. This suggests that the main extrema in the original waveform have high amplitude and wide support range, making them difficult to eliminate by smoothing operations, and the signal structure exhibits strong stability across multiple scales. In circuit breaker diagnosis, this type of characteristic may be related to faults such as core jamming or moving part sticking: jamming causes fixed, large-amplitude inflection points in the current waveform, which remain prominent even when using a large smoothing window. For example, =5, =20, =4, =4 or =5, all are judged as non-attenuation characteristics.

[0101] S107: Based on the matching results of the change characteristics and the preset fault change characteristics, determine the mechanical fault type of the vacuum circuit breaker.

[0102] Specifically, this application utilizes a pre-established database of fault variation features for fault identification. Under laboratory or field conditions, it simulates vacuum circuit breakers with various known mechanical fault types, such as fatigue of the opening spring, fatigue of the closing spring, jamming of the transmission link, sluggish movement of the core, and failure of the buffer. Coil current waveform samples from multiple opening and closing operations under each fault condition are collected. Variation features are extracted from each sample, yielding the corresponding strong attenuation, weak attenuation, or non-attenuation features. Then, the extracted variation features are associated with the mechanical fault type to form a pre-established feature mapping table.

[0103] For example, strong attenuation characteristics correspond to fatigue of the tripping spring; weak attenuation characteristics correspond to fatigue of the closing spring; and non-attenuation characteristics correspond to core jamming, etc. A preset characteristic can be associated with one or more fault types, and can also be combined with other auxiliary characteristics for further subdivision.

[0104] In actual diagnosis, the changing characteristics of the waveform under test are compared with preset characteristics. If the match is a strong attenuation characteristic, the corresponding fault type is output, such as fatigue of the trip spring; if the match is a non-attenuation characteristic, the possibility of core jamming is output. If there are multiple candidate fault types, time-domain parameters, such as peak current time and steady-state current value, can be further combined for comprehensive judgment.

[0105] Pre-defined fault change characteristics are established in advance using the following methods:

[0106] The coil current waveform samples of vacuum circuit breakers with various known mechanical fault types during the opening and closing process are obtained. The variation characteristics of each sample are extracted and the extracted variation characteristics are used as preset characteristics associated with the mechanical fault type. The preset fault variation characteristics include preset characteristics corresponding to strong attenuation characteristics, weak attenuation characteristics and non-attenuation characteristics, respectively.

[0107] Based on the matching results between the changing characteristics and the preset fault changing characteristics, the mechanical fault types of the vacuum circuit breaker are determined, including:

[0108] S1071: Compare the changing features with preset features to determine the matching preset features.

[0109] In the previous steps, the waveform of the vacuum circuit breaker coil current to be diagnosed has had its variation characteristics extracted through a complete dual-smoothing scale analysis process. These characteristics are specifically identified as strong attenuation, weak attenuation, or no attenuation. Simultaneously, a pre-built fault variation characteristic library is constructed within the system. This library contains multiple records, each consisting of a variation characteristic category and its associated known mechanical fault type. This step involves comparing the variation characteristics of the waveform under test with the feature categories of all records in the feature library one by one. For example, if the variation characteristic to be tested is a strong attenuation characteristic, the feature library is traversed to find a pre-defined feature record also marked as a strong attenuation characteristic, thus completing the matching process. The comparison method can employ strict equivalence judgment, or a confidence level assessment can be introduced depending on the actual situation. However, under normal circumstances, the three feature categories differ significantly, and precise matching can ensure accuracy.

[0110] S1072: Determine the mechanical fault type of the vacuum circuit breaker based on the mechanical fault type associated with the matched preset features.

[0111] The matched preset feature records contain a mapping relationship determined in advance through sample learning: what type of mechanical fault the changing feature category corresponds to. For example, if the preset feature record matched in S1071 indicates that its associated fault type is breaker spring fatigue, the diagnostic system directly outputs breaker spring fatigue as the current mechanical fault type of the vacuum circuit breaker. If the same feature category is associated with multiple possible faults, such as strong attenuation characteristics which may correspond to both breaker spring fatigue and excessive transmission clearance, other auxiliary criteria can be combined, such as peak current time and steady-state amplitude, for secondary refinement and differentiation, or the fault type with the highest probability can be output according to historical statistical patterns. Alternatively, all associated fault types can be presented simultaneously for maintenance personnel to refer to.

[0112] The diagnostic process achieves fully automated forward reasoning from waveform physical characteristics to specific fault types. It utilizes the accumulated experience knowledge from a large number of prior samples and retains the ability of extreme point attenuation characteristics to distinguish the waveform microstructure, making the diagnostic results highly reliable and practical for engineering applications.

[0113] This embodiment effectively identifies several typical faults using the simple and robust indicator of extreme point decay characteristics, avoiding complex model training and feature engineering, and is easy to implement in embedded control terminals. Of course, the matching process can also employ fuzzy matching, nearest neighbor classification, or probability output to improve the fault tolerance of the diagnosis.

[0114] In summary, this application effectively characterizes the signal structure changes caused by mechanical faults in circuit breaker current waveforms by extracting the attenuation characteristics of the number of local extrema under a dual-smooth scale, thus enabling accurate diagnosis of various mechanical faults. The method has clear logical steps, strong parameter adaptability, and high engineering practical value.

[0115] Please see Figure 2 Based on the same inventive concept, this application also proposes a mechanical fault diagnosis system for vacuum circuit breakers based on current waveforms, the system comprising:

[0116] The acquisition module is used to acquire coil current waveform data during the opening and closing process of the vacuum circuit breaker.

[0117] The first determining module is used to determine the waveform segment where the current amplitude is lower than a preset threshold based on the coil current waveform data.

[0118] The second determining module is used to determine the first smoothed waveform segment based on the waveform segment and the first smoothing parameter;

[0119] The third determining module is used to determine the second smoothed waveform segment based on the waveform segment and the second smoothing parameter, wherein the second smoothing parameter is greater than the first smoothing parameter;

[0120] The fourth determining module is used to determine the number of first local extreme points based on the comparison results of the current amplitude of each first data point with the first data points before and after it, and to determine the number of second local extreme points based on the comparison results of the current amplitude of each second data point with the second data points before and after it.

[0121] The fifth determining module is used to determine the variation characteristics of the number of local extreme points as the smoothing parameter increases, based on the number of the first local extreme points and the number of the second local extreme points.

[0122] The sixth determination module is used to determine the mechanical fault type of the vacuum circuit breaker based on the matching results between the changing characteristics and the preset fault changing characteristics.

[0123] Based on the same inventive concept, embodiments of this application also propose a processing apparatus, which includes:

[0124] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the current waveform-based vacuum circuit breaker mechanical fault diagnosis method of the present application embodiments.

[0125] Furthermore, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the current waveform-based vacuum circuit breaker mechanical fault diagnosis method of this application.

[0126] The following is a detailed introduction to each component of the processing equipment:

[0127] The processor is the control center of the processing device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0128] Alternatively, the processor can perform various functions of the processing device by running or executing software programs stored in memory and by calling data stored in memory.

[0129] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0130] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the processing device; the embodiments of the present invention do not specifically limit this.

[0131] A transceiver is used to communicate with network devices or with terminal devices.

[0132] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0133] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0134] Figure 3 This is a schematic diagram of the structure of a processing device provided in an embodiment of the present invention. Exemplarily, this processing device may be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. Figure 3 As shown, the processing device 300 may include a processor 301. Optionally, the processing device 300 may also include a memory 302 and / or a transceiver 303. The processor 301 is coupled to the memory 302 and the transceiver 303, for example, via a communication bus.

[0135] The following is combined with Figure 3 A detailed description of each component of the processing equipment 300 is provided below:

[0136] The processor 301 is the control center of the processing device 300. It can be a single processor or a collective term for multiple processing elements. For example, the processor 301 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0137] Alternatively, the processor 301 can perform various functions of the processing device 300 by running or executing software programs stored in the memory 302 and by calling data stored in the memory 302.

[0138] In a specific implementation, as one example, processor 301 may include one or more CPUs, for example... Figure 3 CPU0 and CPU1 are shown in the diagram.

[0139] In a specific implementation, as one embodiment, the processing device 300 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used for processing data (e.g., computer program instructions).

[0140] The memory 302 is used to store the software program that executes the solution of the present invention, and the processor 301 controls the execution. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0141] Optionally, the memory 302 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 302 may be integrated with the processor 301 or exist independently, and may be connected via the interface circuit of the processing device 300. Figure 3 (Not shown in the image) is coupled to processor 301, and this embodiment of the invention does not specifically limit this.

[0142] Transceiver 303 is used for communication with other processing devices. For example, if processing device 300 is a terminal, transceiver 303 can be used to communicate with a network device or with another terminal device. As another example, if processing device 300 is a network device, transceiver 303 can be used to communicate with a terminal or with another network device.

[0143] Alternatively, transceiver 303 may include a receiver and a transmitter. Figure 3 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0144] Optionally, the transceiver 303 can be integrated with the processor 301, or it can exist independently and be connected via the interface circuit of the processing device 300. Figure 3 (Not shown in the image) is coupled to processor 301, and this embodiment of the invention does not specifically limit this.

[0145] Understandable, Figure 3 The structure of the processing device 300 shown does not constitute a limitation on the processing device. The actual processing device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0146] Furthermore, the technical effects of the processing device 300 can be referred to the technical effects of the methods in the above-described method embodiments, and will not be repeated here.

[0147] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0148] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DRRAM). The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuitry), firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to embodiments of the present invention is produced. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0149] It should be understood that the terms "and / or" in this document are merely descriptions of the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this document generally indicates an "or" relationship between the preceding and following objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0150] In this invention, "at least one" refers to one or more items, and "more than one" refers to two or more items. "At least one" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, "at least one" of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. It should be understood that in various embodiments of this invention, the sequence number of the above processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this invention.

[0151] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A method for diagnosing mechanical faults in vacuum circuit breakers based on current waveforms, characterized in that, A method is applied to a current waveform-based mechanical fault diagnosis system for vacuum circuit breakers, the system including a control terminal, the method being executed by the control terminal, including: Acquire coil current waveform data during the opening and closing process of a vacuum circuit breaker; Based on the coil current waveform data, determine the waveform segments where the current amplitude is lower than a preset threshold. Based on the waveform segment and the first smoothing parameter, the first smoothed waveform segment is determined; Based on the waveform segment and the second smoothing parameter, a second smoothed waveform segment is determined, wherein the second smoothing parameter is greater than the first smoothing parameter; Based on the comparison results of the current amplitude of each first data point with the adjacent first data points before and after it, the number of first local extreme points is determined, and based on the comparison results of the current amplitude of each second data point with the adjacent second data points before and after it, the number of second local extreme points is determined. Based on the first number of local extreme points and the second number of local extreme points, determine the variation characteristics of the number of local extreme points as the smoothing parameter increases; Based on the matching results between the change characteristics and the preset fault change characteristics, the mechanical fault type of the vacuum circuit breaker is determined.

2. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 1, characterized in that, Based on the coil current waveform data, waveform segments with current amplitudes below a preset threshold are determined, including: Based on the current amplitude of all data points in the coil current waveform data, determine the median of the current amplitude; Based on the current amplitude of all the data points, the interquartile range of the current amplitude is determined; The preset threshold is determined as the median of the current amplitude minus twice the interquartile range of the current amplitude.

3. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 2, characterized in that, Based on the coil current waveform data, waveform segments with current amplitudes below a preset threshold are determined, including: In the coil current waveform data, an initial segment is formed by the data points in which the current amplitude is continuously lower than the preset threshold. The duration of the initial segment is determined based on the number of data points contained in the initial segment and the sampling rate of the coil current waveform data. The initial segment whose duration is greater than the preset duration threshold is determined as the waveform segment.

4. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 1, characterized in that, The first smoothing parameter and the second smoothing parameter are determined based on the degree of current amplitude fluctuation of the data points within the waveform segment, and include: Obtain the absolute value of the current amplitude difference between all adjacent data points within the waveform segment, and determine the average value of the absolute values ​​as the average amplitude fluctuation. Set the first smoothing parameter to an integer not less than the average amplitude fluctuation; Set the second smoothing parameter to 2n times the first smoothing parameter, where n is an integer greater than or equal to 1.

5. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 4, characterized in that, Determining the first smoothed waveform segment based on the waveform segment and the first smoothing parameter includes: Take a set of continuous data points within the waveform segment, centered on each data point and spanning the first smoothing parameter, as a smoothing window. Determine the median of the current amplitude of all data points within the smoothing window, and use the median as the corresponding current amplitude of the center data point in the first smoothed waveform segment. The first smooth waveform segment is formed sequentially by the corresponding current amplitude of each of the central data points.

6. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 1, characterized in that, The determination of the number of first local extrema points based on the comparison results of the current amplitude of each first data point with the adjacent first data points includes: If the current amplitude of any first data point is greater than the sum of the current amplitude of the preceding first data point and the preset tolerance, and is also greater than the sum of the current amplitude of the following first data point and the preset tolerance, then the first data point is determined as a local maximum point. If the current amplitude of any first data point is less than the difference between the amplitude of its preceding adjacent first data point and the preset tolerance, and less than the difference between the current amplitude of its subsequent adjacent first data point and the preset tolerance, then the first data point is determined as a local minimum point. The total number of local maxima and local minima within the first smooth waveform segment is determined and used as the number of the first local extrema.

7. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 6, characterized in that, The preset tolerance is determined in the following way: Obtain the difference between the maximum and minimum amplitudes of the data points within the waveform segment; The difference is reduced according to a preset ratio and then used as the preset tolerance.

8. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 1, characterized in that, The step of determining the variation characteristics of the number of local extrema points as the smoothing parameter increases, based on the first number of local extrema points and the second number of local extrema points, includes: Compare the number of the first local extrema with the number of the second local extrema; Obtain the parameter difference between the second smoothing parameter and the first smoothing parameter; When the number of the second local extrema is less than the number of the first local extrema, and the parameter difference is greater than twice the first smoothing parameter, the change feature is determined to be a strong attenuation feature. When the number of the second local extreme points is less than the number of the first local extreme points, and the parameter difference is not greater than twice the first smoothing parameter, the change feature is determined to be a weak decay feature. When the number of the second local extrema is not less than the number of the first local extrema, the change feature is determined to be a non-decaying feature.

9. The method for diagnosing mechanical faults in a vacuum circuit breaker based on current waveforms according to claim 8, characterized in that, The preset fault change characteristics are established in advance through the following methods: Coil current waveform samples of vacuum circuit breakers with various known mechanical fault types during the opening and closing process are obtained, and the variation characteristics of each sample are extracted. The extracted variation features are used as preset features associated with the mechanical fault type, wherein the preset fault variation features include preset features corresponding to the strong attenuation feature, the weak attenuation feature and the non-attenuation feature respectively; Based on the matching results between the aforementioned change characteristics and preset fault change characteristics, the mechanical fault type of the vacuum circuit breaker is determined, including: The change features are compared with the preset features to determine the matching preset features; The mechanical fault type of the vacuum circuit breaker is determined based on the mechanical fault type associated with the matched preset features.

10. A mechanical fault diagnosis system for vacuum circuit breakers based on current waveforms, characterized in that, The system includes: The acquisition module is used to acquire coil current waveform data during the opening and closing process of the vacuum circuit breaker; The first determining module is used to determine the waveform segment where the current amplitude is lower than a preset threshold based on the coil current waveform data. The second determining module is used to determine the first smoothed waveform segment based on the waveform segment and the first smoothing parameter; The third determining module is used to determine the second smoothed waveform segment based on the waveform segment and the second smoothing parameter, wherein the second smoothing parameter is greater than the first smoothing parameter; The fourth determining module is used to determine the number of first local extreme points based on the comparison result of the current amplitude of each first data point with the adjacent first data points before and after it, and to determine the number of second local extreme points based on the comparison result of the current amplitude of each second data point with the adjacent second data points before and after it. The fifth determining module is used to determine the variation characteristics of the number of local extreme points as the smoothing parameter increases, based on the first number of local extreme points and the second number of local extreme points. The sixth determining module is used to determine the mechanical fault type of the vacuum circuit breaker based on the matching result between the change characteristics and the preset fault change characteristics.

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