A multi-dimensional mechanical characteristic detection method and device for a switch device

Abstract

The application discloses a kind of switchgear multidimensional mechanical characteristic detection method and device, according to the first peak point of vibration signal and the first inflection point of stroke signal, self-adapting detection window is constructed, and mechanical transmission delay parameter screening effective node is formed by the time sequence coupling of vibration characteristic point and stroke mutation point to form contact action time sequence feature;Through time-stroke deviation analysis, identify deviation abnormal section to construct three-dimensional deviation combination, determine fault type and severity level to form state diagnosis result;Based on state diagnosis result, identify continuous deterioration feature to determine wear acceleration area, according to the trigger spectrum refinement detection of bounce accumulation, identify high-frequency wear feature;Through frequency-time correlation detection, the reliability of diagnosis result is corrected, and the mechanical characteristic evaluation result containing deterioration trend, wear state and confidence is output, to provide support for switchgear state evaluation and maintenance decision.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, and in particular to a method and apparatus for detecting the multi-dimensional mechanical characteristics of switchgear. Background Technology

[0002] As a key control and protection device in the power system, the health of the mechanical operating mechanism of high-voltage switchgear directly affects the reliability of power supply and the safety of the system. During long-term operation, the mechanical components of the operating mechanism, such as springs, trip units, transmission chains, and contacts, will deteriorate due to repeated operation, arc erosion, environmental aging, and other factors, manifesting as various fault symptoms such as abnormal operating time, stroke deviation, and increased contact bounce.

[0003] Existing methods for testing the mechanical characteristics of switchgear mainly rely on single-dimensional parameter measurements, such as monitoring only the opening and closing time or stroke curve, which is insufficient to comprehensively reflect the complex degradation process of the mechanical system. These methods lack the ability to jointly analyze vibration and stroke signals, and cannot reveal the temporal coupling relationship between various links in the mechanical transmission chain, resulting in the masking of early fault characteristics and difficulty in identifying the accelerated degradation stage in advance. At the same time, traditional testing methods have failed to establish a correlation model between dynamic characteristic parameters and mechanical wear state, and cannot identify fault type and severity information from the combination pattern of time-stroke deviation. Furthermore, they lack high-frequency feature detection methods for contact surface degradation. Summary of the Invention

[0004] This invention discloses a method and apparatus for detecting multi-dimensional mechanical characteristics of switchgear. It aims to achieve precise capture of the action process through synchronous acquisition of vibration and travel signals and adaptive window construction; reveal the bidirectional delay characteristics of mechanical transmission through time-series coupling analysis; identify fault types and severity levels through time-travel deviation combinations; determine wear acceleration areas by identifying continuous deterioration characteristics and trigger high-frequency wear characteristic detection; and finally verify and correct the reliability of diagnostic results through frequency-time correlation, providing technical support for switchgear condition assessment and preventative maintenance.

[0005] The first aspect of this invention proposes a method for detecting multi-dimensional mechanical characteristics of switching equipment, comprising the following steps: Collect vibration signals and travel signals of the switching equipment during opening and closing, and determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal; The vibration signal peak amplitude of the mechanical characteristic detection window is used to identify a set of vibration feature points, and the slope change of the stroke signal is used to identify a set of stroke change points. The vibration feature point set and the stroke change point set are time-coupled to form the contact action timing feature. The action time offset and stroke integrity are calculated from the contact action timing characteristics. The action time offset and stroke integrity are analyzed for deviation characteristics to form a time-stroke deviation combination. The fault level is determined based on the time-stroke deviation combination to form a state diagnosis result. Based on the condition diagnosis results, continuous deterioration characteristics are identified to determine the wear acceleration area. The degree of bounce aggravation is identified from the wear acceleration area to generate a bounce accumulation. The vibration signal spectrum is triggered to refine the detection and identify high-frequency wear characteristics based on the bounce accumulation. The high-frequency wear characteristics and the contact action timing characteristics are correlated to generate a frequency-time correlation coefficient. Based on the frequency-time correlation coefficient, the reliability of the state diagnosis results is corrected and the mechanical characteristic evaluation results are output.

[0006] A second aspect of this invention provides a multi-dimensional mechanical characteristic testing device for switchgear, comprising: The signal acquisition module is used to acquire vibration signals and travel signals of the switching equipment during opening and closing, and to determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal. The feature extraction module is used to identify a set of vibration feature points by detecting the peak amplitude of the vibration signal in the mechanical characteristic detection window, identify a set of stroke change points by detecting the slope change of the stroke signal, and time-couple the set of vibration feature points and the set of stroke change points to form contact action timing features. The fault diagnosis module is used to calculate the action time offset and stroke integrity from the action timing characteristics of the contact, perform deviation characteristic analysis on the action time offset and stroke integrity to form a time-stroke deviation combination, and determine the fault level based on the time-stroke deviation combination to form a state diagnosis result. The wear analysis module is used to identify continuous deterioration characteristics and determine wear acceleration areas based on the condition diagnosis results, identify the degree of bounce aggravation from the wear acceleration areas to generate bounce accumulation, and trigger vibration signal spectrum refinement detection to identify high-frequency wear characteristics based on the bounce accumulation. The result output module is used to perform correlation detection between the high-frequency wear characteristics and the contact action timing characteristics to generate a frequency-time correlation coefficient, and to perform credibility correction on the state diagnosis results based on the frequency-time correlation coefficient to output mechanical characteristic evaluation results.

[0007] The beneficial effects of this invention are reflected in the following points: First, by establishing a synchronization time marker through the first peak point of the vibration signal and the first inflection point of the stroke signal, and adaptively determining the window duration according to the operating power level, dynamic adjustment of the detection window is achieved. By using time proximity to optimally pair vibration feature points with stroke abrupt change points, establishing bidirectional transmission delay parameters, and performing correlation correction to screen effective nodes, the temporal coupling relationship of the mechanical transmission chain is revealed, improving the accuracy of feature extraction during the action process. Second, by pairing the action time offset and stroke integrity in stages to form characteristic data points, identifying deviation exceeding limits and determining continuity to form abnormal sections, and combining the severity indices of the three types of abnormalities into a three-dimensional vector, the severity level of the fault is quantified through the vector magnitude, and the fault subtypes are subdivided according to the dominant deviation dimension, achieving comprehensive diagnosis of fault type and severity, improving the accuracy of fault location and the pertinence of maintenance decisions. Finally, by identifying continuous degradation characteristics from historical diagnostic results and determining wear acceleration areas based on the decreasing trend of time intervals, and by refining the detection of high-frequency wear characteristics based on the vibration signal spectrum triggered by the cumulative bounce, a frequency-time correlation detection mechanism between high-frequency wear characteristics and contact action timing characteristics was established. The confidence of the diagnostic results was corrected based on the correlation coefficient, thereby realizing the prediction and identification of degradation trends and the reliability verification of diagnostic results, and improving the credibility of condition assessment.

[0008] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0009] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0010] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0011] Figure 1 This is a flowchart illustrating a multi-dimensional mechanical characteristic testing method for switching equipment according to the present invention.

[0012] Figure 2 This is a schematic diagram of a multi-dimensional mechanical characteristic detection device for switching equipment according to the present invention.

[0013] Figure 3 This is a structural block diagram of a multi-dimensional mechanical characteristic detection device for switching equipment according to the present invention.

[0014] Wherein: 1-Switch device; 2-Operating mechanism; 3-Operating mechanism housing; 4-Main shaft; 5-Acceleration sensor; 6-Angular displacement sensor; 7-Output shaft; 8-Linkage mechanism; 9-Contact; 10-Transmission chain. Detailed Implementation

[0015] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0016] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0017] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] The technical solutions of the embodiments of this application will be described below.

[0019] like Figure 1 As shown, this embodiment of the invention provides a method for detecting multi-dimensional mechanical characteristics of switching equipment, including the following steps S110-S150: Step S110: Collect the opening and closing vibration signals and travel signals of the switchgear, and determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal.

[0020] Specifically, vibration signals and travel signals from the switching equipment during opening and closing are collected. For example... Figure 2As shown, the accelerometer 5 is fixedly mounted on the surface of the housing 3 of the operating mechanism 2 of the switchgear 1, at a rigid connection point 30-50mm from the center of the main shaft 4 of the operating mechanism. The analog voltage signal output by the sensor is amplified and filtered by the signal conditioning circuit and then sent to the data acquisition card. The acquisition card converts the analog signal into a digital vibration signal at a sampling frequency of 10kHz. The sensor sensitivity is 100mV / g, and the range covers an acceleration range of ±50g. After the opening and closing operation is triggered, the vibration signal acquisition duration is set to 500ms. Data recording starts from the moment the operation command is issued until the steady-state stage after the contact 9 is in position. The vibration signal includes vibration components generated by the start of the operating mechanism 2, the movement of the transmission chain 10, and the collision of the contact 9. The time-domain waveform of the vibration signal exhibits obvious multi-peak characteristics. An angular displacement sensor 6 is mounted on the output shaft 7 of the operating mechanism 2 or on the linkage mechanism 8. The sensor's axial direction is kept coaxial with the contact 9's movement direction, with an error of less than 1 degree. A substation circuit breaker's travel measurement showed a persistent deviation. During a power outage maintenance, it was discovered that the angular displacement sensor was not aligned during installation, and there was an angle between the sensor's axis and the actual movement direction of the contact. This prevented the sensor's measured rotation angle from being accurately converted into the contact's true linear displacement. The orthogonal pulse signal output by the rotary encoder is calculated into an angle value by a counter. This angle value is then geometrically converted into the linear displacement of the contact 9, forming a travel signal. The sampling frequency is synchronized with the vibration signal at 10kHz, and the resolution of the angular displacement sensor 6 is 0.1 degrees. The travel signal during opening and closing operations increases rapidly after the contact starts, slowing down as the contact approaches its end point. The travel signal and vibration signal are synchronously acquired by a data acquisition card. The acquisition card's built-in hardware clock adds a unified timestamp to both signals, with a timestamp accuracy of 0.1μs.

[0021] In some embodiments, determining the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal includes: obtaining timestamps from the first peak point of the vibration signal and the first inflection point of the travel signal to establish a synchronization time identifier; collecting vibration signal amplitude features backward from the synchronization time identifier to identify the operating power level; determining the window duration based on the corresponding preset window duration matched with the operating power level; and constructing the mechanical characteristic detection window based on the synchronization time identifier and the window duration.

[0022] Synchronization time markers are established by obtaining timestamps from the first peak point of the vibration signal and the first inflection point of the travel signal. The peak detection algorithm scans the vibration signal waveform starting 10ms after the operation command. When the first extreme point where the amplitude exceeds three standard deviations of the noise baseline is detected, this point is the first peak point of the vibration signal, and the corresponding timestamp is t_v1. The first peak point of the vibration signal corresponds to the instant the trip unit releases, triggering the energy storage mechanism; t_v1 marks the start of the mechanical vibration response. The first-order differential of the travel signal obtains the contact displacement velocity curve, with a differential step size of 0.1ms sampling period. The moment when the velocity curve first exceeds the 0.5mm / ms threshold (500mm / s) is defined as the first inflection point of the travel signal. The timestamp corresponding to the first inflection point of the travel signal is t_s1, marking the beginning of significant displacement change in the contact. The smaller of the timestamps t_v1 (the first peak point of the vibration signal) and t_s1 (the first inflection point of the travel signal) is taken as the synchronization time marker, with the formula t_sync = min(t_v1, t_s1), where t_sync is the synchronization time marker. All mechanical characteristic parameter time measurements are referenced to the synchronization time marker, which establishes the zero point for window positioning. The time difference between the first peak point of the vibration signal and the first inflection point of the travel signal is typically within the range of 5-15 ms. A time difference exceeding 20 ms is marked as a warning of abnormal transmission chain clearance. In repeated tests, the time interval between the trip unit's release vibration and the contact's starting displacement gradually increased. This was ultimately confirmed to be due to severe wear of the transmission linkage connecting pin, leading to increased transmission clearance and a longer time required for the trip unit to drive the contacts. The accuracy of the synchronization time marker inherits the 0.1μs resolution of the timestamp system. Peak detection adopts the sliding window extreme value judgment method. By comparing multiple adjacent sampling points, the locality of the peak value is confirmed to avoid misjudgment caused by noise interference. The vibration signal of a certain device is mixed with high-frequency noise spikes generated by electromagnetic interference. If the maximum amplitude point is directly searched, the noise spike will be misjudged as the vibration peak. By comparing adjacent sampling points, isolated noise pulses can be effectively filtered out.

[0023] The operating power level is identified by tracing back the synchronization time marker to collect vibration signal amplitude characteristics. An analysis window is established 50ms backward from the synchronization time marker. This window contains 500 sampling points of vibration signal data. The maximum absolute value within the window is determined as the peak amplitude, the root mean square value as the effective amplitude, and the cumulative sum of squared signals as the energy integral value. These three values ​​together constitute the vibration signal amplitude characteristics. The timestamp of the synchronization time marker serves as the end point of the analysis window, and the start point is the timestamp minus 50ms. The time range of the analysis window ensures coverage of the energy storage state characteristics before the trip unit releases. The three parameters of the vibration signal amplitude characteristics are normalized by dividing by the reference values ​​recorded in the equipment's factory calibration test. The normalized peak amplitude, effective amplitude, and energy integral value are then weighted and summed using weighting coefficients of 0.4, 0.3, and 0.3, respectively, to obtain the power score. A score less than 0.3 indicates a low operating power level, 0.3-0.7 indicates medium power, and greater than 0.7 indicates high power. A low power level is marked as insufficient energy storage. In this case, the circuit breaker was identified as having severely insufficient operating energy during routine testing. Upon power outage inspection, it was found that one end of the energy storage spring hook was loose, and the spring had lost pre-compression, resulting in insufficient driving force during tripping. A high operating power level indicates normal energy storage. A ratio of the standard deviation to the mean of the three vibration signal amplitude characteristic parameters less than 0.15 indicates good parameter consistency; poor parameter consistency is marked as an uncertain operating energy level.

[0024] The window duration is determined based on the preset window duration corresponding to the operating power level. The three preset window durations stored in the configuration table—300ms for low power, 200ms for medium power, and 150ms for high power—are the base values. Once the operating power level is determined, the corresponding preset window duration is retrieved as the baseline value. The relative position of the power score within the current level range is calculated using linear interpolation, with a fine-tuning range of ±20ms. This fine-tuning is added to the preset window duration to obtain the window duration. The window duration undergoes boundary constraint checks: values ​​less than 100ms are forcibly increased to 100ms, and values ​​greater than 400ms are forcibly truncated to 400ms. Boundary constraints ensure that the window captures the complete motion without including excessive irrelevant vibrations. When the operating power level is uncertain, the window duration is conservatively chosen as the preset window duration of 200ms for the medium power level. The window duration setting used in the initial commissioning of a certain batch of circuit breakers was too short, and the tail segment of the tripping action of some equipment was truncated. After analyzing the actual action duration characteristics of the batch of equipment and adjusting the window parameters accordingly, the capture of the complete action process was ensured.

[0025] A mechanical characteristic detection window is constructed based on the synchronization time marker and the window duration. The start time t_start is obtained by subtracting 10ms from the synchronization time marker, and the end time t_end is obtained by adding the window duration and 30ms to the start time. The window duration determines the main width range of the mechanical characteristic detection window, with 10ms and 20ms margins added at the beginning and end to accommodate the preparation phase before the action starts and the stabilization phase after the action is in place. The time interval between the start time t_start and the end time t_end is the mechanical characteristic detection window, and the actual width of the window is equal to the window duration plus the 30ms margin. Based on the timestamp indices of the start time t_start and the end time t_end, data segments corresponding to the time intervals are extracted from the original vibration signal and stroke signal to form the vibration signal data and stroke signal data within the window. The window boundaries employ a gradual transition process. Data in the central region of the window retains its original amplitude, while the amplitude of data in the boundary region gradually decays to zero according to a linear function. The width of the boundary transition zone is 5 sampling points. Spectral analysis of the vibration signal revealed high-frequency components near the window boundaries that should not be present in actual mechanical action. These spurious frequencies originated from signal abrupt changes at the window edges. The gradual transition process at the window boundaries eliminated this type of spectral interference. A synchronization time marker is placed inside the mechanical characteristic detection window to indicate the actual start time of the mechanical action, located 10 ms after the window begins.

[0026] Step S120: Identify the set of vibration feature points by detecting the peak amplitude of the vibration signal in the mechanical characteristic detection window, identify the set of stroke change points by detecting the slope change of the stroke signal, and time-couple the set of vibration feature points and the set of stroke change points to form the contact action timing feature.

[0027] Specifically, the vibration signal peak amplitude is detected within the mechanical characteristic detection window to identify the set of vibration feature points. The vibration signal within the window is preprocessed using a third-order Butterworth low-pass filter with a cutoff frequency set to 2kHz. The filtered vibration signal retains the main vibration components and suppresses high-frequency noise interference. A sliding window method is applied for peak detection. The sliding window width is 21 sampling points corresponding to a duration of 2.1ms. When the amplitude at the center point of the window is greater than the amplitudes of all other points within the window, that center point is marked as a local maximum. Local maximum points must simultaneously meet two threshold conditions to be included in the set of vibration feature points: the first threshold is that the peak amplitude exceeds 1.5 times the root mean square value of the vibration signal; the second threshold is that the difference between the peak and the adjacent valley value exceeds 0.3 times the peak amplitude. In a certain device's vibration signal, there is a continuous low-amplitude background vibration. If only a single amplitude threshold is used, a large number of background vibrations will be misjudged as characteristic peaks. By increasing the relative amplitude threshold, requiring the peak value to be significantly higher than the adjacent valley value, background vibration interference is effectively filtered out. Local maxima points satisfying the dual threshold conditions are arranged chronologically to form a set of vibration feature points. Each feature point in the set contains three attributes: timestamp, amplitude, and index position in the original vibration signal. The timestamp is used for subsequent time-series coupling analysis, the amplitude is used for priority ranking and weighted calculation of feature points, and the index position is used to quickly locate the feature point's position in the vibration signal waveform. The number of elements in the vibration feature point set reflects the complexity of the vibration during the mechanical action. A typical opening operation contains 5-12 feature points, while a closing operation, due to contact bounce, typically contains 8-20 feature points.

[0028] The stroke signal is used to detect slope abrupt changes and identify a set of stroke abrupt change points. The stroke signal within the mechanical characteristic detection window is smoothed using a Savitzky-Golay filter with a window length of 11 sampling points and a polynomial order of 3. The smoothed stroke signal retains the true motion trajectory and eliminates measurement jitter. The forward difference method is applied to differentiate the stroke signal to obtain the velocity curve, with a difference step size of 0.1 ms (sampling period). The velocity curve is expressed in mm / ms. The velocity curve is differentiated again to obtain the acceleration curve, which reflects the rate of change of the contact motion state. Abrupt changes in the acceleration curve correspond to the turning points of the contact motion. The point where the absolute value of the acceleration curve exceeds the threshold of 5 m / s² (5000 mm / s²) is defined as a candidate point for slope abrupt change. Candidate points must meet the persistence criterion to be included in the set of stroke abrupt change points. The continuity criterion requires that the average absolute value of the acceleration of the five sampling points before and after the candidate point be greater than 2 m / s² (2000 mm / s²). This criterion filters out false abrupt changes caused by transient interference. For example, a circuit breaker in a factory was subjected to vibration interference from nearby equipment during tripping, resulting in isolated interference spikes in the acceleration curve of the travel signal. By requiring the abrupt change to last for a certain period rather than being a single pulse, the true contact movement inflection point was successfully identified, and external interference was eliminated. Candidate points meeting the continuity criterion are arranged in chronological order to form a travel abrupt change point set. Each abrupt change point in the set is recorded with a timestamp attribute, marking the precise moment of the contact movement state transition. The number of elements in the travel abrupt change point set reflects the stage division of the contact movement process. A typical operation's travel abrupt change point set contains 4-6 abrupt change points, corresponding to key inflection points such as start-up, acceleration, deceleration, and arrival. An empty travel abrupt change point set indicates that the travel signal is too smooth with no obvious transitions or a sensor malfunction.

[0029] In some embodiments, the step of temporally coupling the set of vibration feature points with the set of stroke abrupt change points to form contact action timing features includes: reading the time stamp of each feature point from the set of vibration feature points and the set of stroke abrupt change points; performing sequential analysis on the time stamps to identify the vibration-stroke trigger sequence; generating mechanical transmission delay parameters based on the time interval of each point in the vibration-stroke trigger sequence; and constructing contact action timing features by performing correlation correction based on the mechanical transmission delay parameters.

[0030] The timestamps of each vibration feature point are read from the vibration feature point set and the stroke abrupt change point set. The timestamp attribute of each vibration feature point in the vibration feature point set is extracted to form a vibration timestamp sequence. The sequence length is equal to the number of elements in the vibration feature point set, and the sequence elements are arranged in the order of appearance of the feature points in the vibration signal. The timestamp attribute of each abrupt change point in the stroke abrupt change point set is extracted to form a stroke timestamp sequence. The sequence length is equal to the number of elements in the stroke abrupt change point set, and the sequence elements reflect the distribution of each turning point during the contact movement. In a certain tripping operation, 5 vibration feature points were identified, with a timestamp sequence of [12ms, 18ms, 26ms, 35ms, 42ms], and 3 stroke abrupt change points were also identified, with a timestamp sequence of [15ms, 28ms, 40ms]. The vibration timestamp sequence and the stroke timestamp sequence are merged to form a joint timestamp set. The joint timestamp set contains all the time information of both types of feature points, and the total number of elements in the set is equal to the sum of the lengths of the two sequences. The elements in the joint time stamp set are arranged in ascending order of timestamps. In the sorted sequence, vibration feature points and stroke change points are interspersed. After merging and sorting the aforementioned cases, an interspersed pattern is formed [12ms(V), 15ms(S), 18ms(V), 26ms(V), 28ms(S), 35ms(V), 40ms(S), 42ms(V)]. The interspersed pattern reflects the temporal relationship between mechanical action and vibration response.

[0031] The vibration-stroke trigger sequence is identified by analyzing the chronological order of time markers. The source determination of adjacent time markers in the joint time marker set constitutes the basic unit of the trigger sequence: V for vibration feature points and S for stroke abrupt change points. Two adjacent time markers form a trigger pair. Each trigger pair records three pieces of information: the type of the preceding node, the type of the following node, and the time interval between the two nodes. Trigger pairs include four combination modes: VV, VS, SV, and SS. A VS-type trigger pair indicates that the vibration feature point occurs before the stroke abrupt change point; this mode corresponds to contact movement caused by mechanical impact, and the time interval of the trigger pair reflects the response delay of the mechanical transmission. An SV-type trigger pair indicates that the stroke abrupt change point occurs before the vibration feature point; this mode corresponds to mechanical impact generated by contact movement, and the time interval of the trigger pair reflects the structural stiffness and damping characteristics. A VV-type trigger pair indicates that there is no stroke node insertion between consecutive vibration feature points, and an SS-type trigger pair indicates that there is no vibration feature point insertion between consecutive stroke nodes. The vibration-stroke trigger sequence is formed by concatenating all trigger pairs in the joint time stamp set in chronological order. The sequence length is equal to the total number of time stamps minus 1. The vibration-stroke trigger sequence is stored in array form, with array indices starting from 0 and increasing chronologically. Array elements are trigger pair structures, each containing four fields: previous node index, next node index, node type identifier, and time interval. The i-th element in the vibration-stroke trigger sequence corresponds to the triggering relationship between the i-th time stamp and the (i+1)-th time stamp in the joint time stamp set.

[0032] For example, generating mechanical transmission delay parameters based on the time interval of each point in the vibration-stroke triggering sequence includes: determining multiple candidate vibration feature points corresponding to a single stroke abrupt change point from the vibration-stroke triggering sequence; calculating the time proximity of each of the multiple candidate vibration feature points to the single stroke abrupt change point; performing optimal pairing based on the time proximity to determine the feature point pairing result; and constructing mechanical transmission delay parameters according to the time interval of each point pair in the feature point pairing result.

[0033] Multiple candidate vibration feature points corresponding to a single stroke abrupt change point are determined from the vibration-stroke trigger sequence. The vibration-stroke trigger sequence records the type identifier and time interval of each trigger pair. The position of the stroke abrupt change point in the trigger sequence is located by identifying the node with type identifier S. Each stroke abrupt change point node in the trigger sequence is traversed, and the timestamp of that node is extracted as the search center, with a time search window of 15ms before and 5ms after. Actual measurements show that the mechanical impact vibration generated by the trip unit release usually precedes the contact initiation stroke. Statistical analysis of the time interval of VS type trigger pairs in the trigger sequence shows that the time difference can reach more than ten milliseconds. If the search window is symmetrically set, such pairings with clear causal relationships but misaligned timing will be missed. The asymmetrical window design ensures the integrity of the pairings. Vibration feature points whose timestamps fall into the search window are selected from the set of vibration feature points to form a candidate set of vibration feature points for the stroke abrupt change point. The timing relationship provided by the trigger sequence ensures that the search window can cover vibration feature points causally related to the stroke abrupt change point. Vibration feature points falling into the window are sorted in ascending order of timestamp and stored in a candidate set. An empty candidate set indicates that the abrupt change point has no corresponding vibration feature within the search range of the trigger sequence, and this abrupt change point is directly marked as unpaired in subsequent pairing processes. If the number of vibration feature points in the candidate set exceeds 5, the 5 feature points with the largest amplitudes are retained through amplitude filtering. Each abrupt change point's candidate vibration feature point set is stored separately, with each set element containing the vibration feature point's timestamp and its index position within the set.

[0034] The time proximity between multiple candidate vibration feature points and a single stroke abrupt change point is calculated. The absolute value of the timestamp difference between the candidate vibration feature point and the stroke abrupt change point is denoted as the time difference Δt. The smaller the time difference Δt, the closer the two feature points are. The time proximity reflects the degree of causal correlation between vibration impact and contact movement. The time proximity is defined as a negative exponential function P=exp(-Δt / τ), where P is the time proximity and τ is the time constant set to 5ms. The causal correlation between vibration and stroke weakens rapidly as the time difference increases. The linear decay model cannot accurately describe this rapid decay characteristic. The negative exponential function can reasonably reflect the physical law that the proximity is close to 1 when the time difference is small and rapidly approaches 0 when the time difference is large. The time proximity range is 0-1. When the time difference is 0, the time proximity is 1. The time proximity decays exponentially as the time difference increases. Setting the time constant τ to 5ms causes the time proximity to decay to 0.37 when the time difference is 5ms and to 0.05 when the time difference is 15ms. For each vibration feature point in the candidate vibration feature point set, a temporal proximity calculation is performed between it and the abrupt change point in the stroke. The calculation results form a proximity vector, the length of which is equal to the number of candidate vibration feature points. The maximum value in the proximity vector corresponds to the vibration feature point that is closest in time, and this feature point is selected as the preferred pairing.

[0035] Feature point pairing results are determined based on time proximity. Candidate vibration feature points with a time proximity greater than 0.3 in the proximity vector are included in the pairing candidate set. The threshold of 0.3 corresponds to a time difference of approximately 6ms; vibration feature points exceeding this time difference have a weaker correlation with stroke abrupt change points. When the pairing candidate set is empty, the stroke abrupt change point is marked as unpaired. When the pairing candidate set contains a single vibration feature point, the pairing relationship is directly determined. When the pairing candidate set contains multiple vibration feature points, the one with the highest time proximity is selected as the pairing object. A vibration feature point may appear in the pairing candidate sets of multiple stroke abrupt change points simultaneously. In this case, a one-to-one pairing constraint is adopted, prioritizing the pairing relationship with the highest time proximity. Stroke abrupt change points that are not selected are removed from the pairing candidate set, and a new selection process is initiated. In a certain detection, multiple vibration peaks appear densely around the moment the contact arrives. Without constraints, a stroke node might be repeatedly paired by multiple vibration peaks. By using a one-to-one constraint and prioritizing the pairing relationship with the highest proximity, the uniqueness and rationality of the pairing results are ensured. The feature point pairing results are stored in the form of a list of point pairs, where each element is a point pair between the vibration feature point index and the stroke change point index.

[0036] Mechanical transmission delay parameters are constructed based on the time intervals of each point pair in the feature point pairing results. The time interval Δt_pair is obtained by subtracting the vibration feature point timestamp from the stroke abrupt change point timestamp of each point pair. A positive time interval indicates that vibration precedes stroke, while a negative time interval indicates that stroke precedes vibration. The feature point pairing results are organized as a list of point pairs. Each point pair in the list contains three pieces of information: the vibration feature point index, the stroke abrupt change point index, and the time interval. The completeness of the feature point pairing results directly affects the accuracy of the mechanical transmission delay parameters. Point pairs with positive time intervals are classified as VS-type point pairs. The absolute values ​​of the time intervals of these point pairs form a positive delay sample, and the median of the positive delay sample is used as the positive delay component of the mechanical transmission delay parameter. Point pairs with negative time intervals are classified as SV-type point pairs. The absolute values ​​of the time intervals of these point pairs form a negative delay sample, and the median of the negative delay sample is used as the negative delay component of the mechanical transmission delay parameter. The mechanical transmission delay parameters also include a delay dispersion index, defined as the combined standard deviation of the positive and negative delay samples. A dispersion of less than 2ms indicates good consistency in delay characteristics. The output structure of the mechanical transmission delay parameter includes three attributes: forward delay, reverse delay, and delay dispersion. These three attributes comprehensively describe the timing characteristics of the mechanical transmission.

[0037] The timing characteristics of contact action are constructed by correcting the correlation based on the mechanical transmission delay parameters. The mechanical transmission delay parameters include three indicators: forward delay, reverse delay, and delay dispersion. These three indicators are used to evaluate the correlation strength of trigger pairs and screen effective nodes. For VS-type trigger pairs, the absolute value of the deviation between the time interval and the representative value of the forward delay of the mechanical transmission delay parameters is less than 3ms, indicating a strongly correlated pair. Similarly, for SV-type trigger pairs, the absolute value of the deviation between the time interval and the representative value of the reverse delay of the mechanical transmission delay parameters is less than 3ms, also indicating a strongly correlated pair. When the delay dispersion of the mechanical transmission delay parameters exceeds 3ms, it is marked as an unstable transmission timing state. Corresponding pairs in the unstable state are downgraded to weakly correlated pairs, even if the time deviation meets the threshold. A clear causal relationship exists between the vibration characteristic points and the stroke abrupt change points of strongly correlated pairs. Vibration characteristic points in strongly correlated pairs are marked as effective vibration nodes, and stroke abrupt change points in strongly correlated pairs are marked as effective stroke nodes. Effective nodes constitute the key timing anchors of the action process. The contact action timing characteristics consist of time series of effective vibration nodes and effective travel nodes, and node type series. The time series records the time offset of each node relative to the synchronization moment marker, and the node type series distinguishes whether the node is a vibration node or a travel node. The contact action timing characteristics are output as a structure array, and the array elements contain two attributes: node time and node type.

[0038] Step S130: Calculate the action time offset and stroke integrity from the contact action timing characteristics, perform deviation characteristic analysis on the action time offset and stroke integrity to form a time-stroke deviation combination, and determine the fault level based on the time-stroke deviation combination to form a state diagnosis result.

[0039] Specifically, the actuation time offset and stroke completeness are calculated from the contact actuation timing characteristics. The stroke nodes in the contact actuation timing characteristics are arranged in ascending time order, dividing the actuation process into four stages: start-up, acceleration, deceleration, and arrival. Each stage corresponds to the action of different mechanical components. The start-up stage reflects the initial response of the trip unit and transmission chain, while the acceleration stage reflects the driving characteristics of the energy storage spring. The difference between the time interval between the first and second stroke nodes and the rated start-up time of the equipment forms the start-up time deviation. A positive start-up time deviation indicates a slow trip unit operation or an increased initial clearance in the transmission chain. The interval between the second and third stroke nodes forms the acceleration time deviation, the interval between the third and fourth stroke nodes forms the deceleration time deviation, and the time interval from the fourth stroke node to the final arrival of the contact forms the arrival time deviation. The combination of these four time deviations forms the actuation time offset. The actuation time offset includes four components: start-up deviation, acceleration deviation, deceleration deviation, and arrival deviation. These four components reflect the timing characteristics of different mechanical components. The stroke achievement rate is obtained by dividing the stroke signal value at the last stroke node in the contact action timing characteristics by the rated stroke value of the equipment. For a switchgear with a rated stroke of 11mm, if the actual stroke only reaches 10mm, the stroke achievement rate is 0.91, indicating insufficient stroke. The standard deviation of the stroke signal change rate between each stroke node is normalized, and 1 minus this normalized value is used as the stroke curve smoothness. The stroke integrity is obtained by weighting the stroke achievement rate and the stroke curve smoothness with weights of 0.7 and 0.3, respectively. The stroke integrity value ranges from 0 to 1; the closer the value is to 1, the higher the stroke integrity and the more complete the stroke execution.

[0040] In some embodiments, the step of performing deviation characteristic analysis on the motion time offset and the stroke integrity to form a time-stroke deviation combination includes: performing deviation correlation analysis on the motion time offset and the stroke integrity to generate a time-stroke characteristic distribution; identifying deviation abnormal segments from the time-stroke characteristic distribution to generate deviation feature parameters; using the deviation feature parameters to characterize the deviation abnormal segments to form a deviation diagnosis interval; and constructing a time-stroke deviation combination based on the deviation diagnosis interval.

[0041] A deviation correlation analysis is performed on the motion time offset and stroke integrity to generate a time-stroke characteristic distribution. The four components of the motion time offset are arranged in chronological order to form a time offset sequence. The start-up deviation, acceleration deviation, deceleration deviation, and arrival deviation included in the motion time offset are used as the first to fourth elements of the time offset sequence, with a fixed array length of 4. The stroke increment for each motion stage is calculated from adjacent stroke nodes of the stroke signal. The stroke increment for each stage is the stroke value at the end node minus the stroke value at the start node. The ratio of the stroke increment for each stage to the corresponding rated stroke increment is arranged to form a stroke integrity sequence. The time offset sequence and the stroke integrity sequence are paired by stage index. The time offset and stroke integrity of the i-th stage constitute the characteristic data point for that stage. Four motion stages generate four characteristic data points. The distribution of these data points on the time-stroke two-dimensional plane constitutes the time-stroke characteristic distribution, which is stored as a data point array. The array element is a triplet of (stage index, time offset, stroke integrity). The array length of the time-stroke characteristic distribution is equal to the number of motion stages, which is 4. For ease of analysis, the time deviation sequence and the travel integrity sequence can be plotted on the same coordinate system as a dual-axis curve. The horizontal axis represents the operating stage index 1-4, the left vertical axis represents the time deviation range of -20ms to +20ms, and the right vertical axis represents the travel integrity range of 0-1.2. The dual-axis curve visually demonstrates the correspondence between time deviation and travel integrity at each stage. A circuit breaker exhibiting prolonged start-up time and insufficient travel, a pattern of simultaneous deterioration in both time and travel, suggests a decrease in the stiffness of the energy storage spring. Conversely, another device experiencing prolonged start-up time but normal start-up travel, a pattern of varying time and travel differences, suggests a sluggish trip unit operation but sufficient spring energy storage.

[0042] For example, the step of identifying abnormal deviation segments and generating deviation feature parameters from the time-travel characteristic distribution includes: setting a deviation tolerance range for the time-travel characteristic distribution to determine a normal interval limit; filtering a set of deviation over-limit points from deviation distribution points that exceed the normal interval limit; performing continuity determination on the set of deviation over-limit points to identify abnormal deviation segments; and generating deviation feature parameters based on the deviation amplitude of the abnormal deviation segments.

[0043] A deviation tolerance range is set for the time-travel characteristic distribution to determine the normal range limit. The mean time deviation of the normal operation data stored in the equipment operation history database, ±2 times the standard deviation, is used as the time deviation tolerance boundary. When calculating the tolerance, the database is selected from the most recent year's test records for this equipment model with a fault severity level of "normal". The mean and standard deviation of the time deviation for each action stage are calculated separately. The tolerance for typical opening equipment is -3ms to +3ms, and the tolerance for closing equipment is -5ms to +5ms. The tolerance for closing operation is wider because the action process is greatly affected by contact bounce and fluctuates more. The deviation tolerance for stroke integrity is set at ±5% of the rated value, i.e., 0.95-1.05. This range covers the combined effects of manufacturing tolerances and measurement errors. For switchgear with a rated stroke of 11mm, the actual stroke is allowed to be within the range of 10.45-11.55mm. The time deviation tolerance and the travel integrity tolerance together define the normal range limit. The normal range limit includes the time dimension boundary and the travel dimension boundary, forming a judgment area on a two-dimensional plane. All data points in the time-travel characteristic distribution are traversed, and a triplet of (stage index, time deviation, travel integrity) is extracted from each data point array. The extracted time deviation and travel integrity are compared with the boundary of this area. Points falling within the area are judged as normal points, and points exceeding the area are judged as abnormal candidate points. The normal range limit tolerance for the start-up and arrival phases can be appropriately relaxed because these two phases are greatly affected by the instantaneous fluctuations of the trip unit release and the instantaneous fluctuations of the buffer contact. The tolerance for the acceleration and deceleration phases is tightened to improve detection sensitivity.

[0044] The deviation distribution points exceeding the normal range limits are used to filter the set of deviation over-limit points. Each candidate abnormal point is checked to see if its deviation amplitude exceeds the normal range limit boundary. Points with an absolute time deviation exceeding the time boundary or a stroke integrity exceeding the stroke boundary are included in the deviation over-limit point set. The action stage index, time deviation value, stroke integrity value, and over-limit direction of each over-limit point are recorded in the deviation over-limit point set. The over-limit direction is distinguished as positive time over-limit, negative time over-limit, positive stroke over-limit, and negative stroke over-limit. A positive time over-limit corresponds to an extended action time; for example, the start-up phase time increases from the rated 12ms to 19ms, exceeding the limit by 7ms, indicating a slow trip unit action. A negative time over-limit corresponds to a shortened action time; the acceleration phase time shortens from the rated 25ms to 18ms, exceeding the limit by -7ms, indicating excessive spring preload or reduced transmission chain resistance. A positive stroke over-limit corresponds to excessive stroke; the arrival phase stroke reaches 112% of the rated value, exceeding the limit by 7%, indicating improper adjustment of the limit bolt or wear of the limit block. A negative stroke over-limit corresponds to insufficient stroke; during acceleration, the stroke only reaches 88% of the rated value, exceeding the limit by 7%, indicating contact erosion and wear or transmission rod bending and deformation. An empty set of deviation over-limit points indicates that all operating stages are within the normal range, and the equipment is currently in good condition requiring no further analysis. The distance between the actual deviation value and the boundary of the normal range limit is defined as the over-limit amplitude. A larger over-limit amplitude indicates a more severe deviation. The deviation over-limit point set records the over-limit amplitude values ​​of each over-limit point.

[0045] For a set of deviation exceedance points, a continuity judgment is performed to identify abnormal deviation segments. Exceedance points in the deviation exceedance point set are arranged in ascending order by the action stage index. The stage index of each exceedance point is read from the deviation exceedance point set to form an index sequence. The index sequence is traversed to calculate the difference between adjacent indices. When the stage index difference between two adjacent exceedance points is 1, it is determined to be a continuous exceedance; when the index difference is greater than 1, it is determined to be an intermittent exceedance. Continuously exceedance points are merged into one abnormal deviation segment. The starting index of the merged segment is the first exceedance point index, and the ending index is the last exceedance point index. For example, if a device continuously exceeds the limit during acceleration and deceleration, forming a continuous abnormal segment, it indicates that insufficient spring stiffness is affecting the entire high-speed motion process. Another device only exceeds the limit during the startup stage while other stages are normal, forming a scattered abnormality, indicating a single-point fault in the trip unit. Continuity judgment can distinguish between systemic degradation and local faults. Continuously exceeding the limit in stages 2 and 3 is merged into a continuous deviation abnormality segment during acceleration-deceleration. This pattern indicates that insufficient spring stiffness is affecting the entire high-speed motion process. Intermittent over-limit points each independently constitute a single-point deviation anomaly segment. The starting and ending indices of the single-point segment are the same, with only stage 1 initiating the over-limit while other stages are normal. This pattern indicates a single-point fault in the trip unit, while the spring and buffer system are normal. Four consecutive over-limit points with indices 1-2-3-4 form a single full-range deviation anomaly segment. This situation indicates a severe deterioration in the overall performance of the equipment. For example, a circuit breaker that has been in operation for 15 years may have multiple problems such as spring aging, contact wear, lubrication deterioration, and buffer failure, resulting in full-range over-limit.

[0046] Deviation characteristic parameters are generated based on the deviation amplitude of the deviation anomaly segment. Excess points belonging to the current deviation anomaly segment are selected from the set of deviation exceedance points. The selection criterion is that the stage index of the exceedance point falls within the range of the segment's start and end indices. The deviation amplitude of these exceedance points is read to form a segment deviation sample. The maximum value of the segment deviation sample is defined as the segment deviation peak value. During the circuit breaker's acceleration phase, three exceedance points appear with deviation amplitudes of 6ms, 9ms, and 7ms, respectively. The peak deviation value for this segment is taken as the maximum value of 9ms. The location corresponding to the peak value reveals the most severe deviation point in this phase. The total number of deviation anomaly segments, start index, end index, deviation peak value, and anomaly type are integrated to form five core fields of the deviation characteristic parameters. The "Total Number of Sections" field records the number of identified deviation anomaly sections. A single section indicates that the fault is concentrated in a specific stage, while multiple sections indicate that the fault is dispersed. The "Section Start Index" field records the action stage number where the first over-limit point of the section is located. A section starting from the start stage indicates a problem with the trip unit or the initial link of the transmission chain, while a section starting from the acceleration stage indicates a problem with the energy storage spring. The "Section End Index" field records the action stage number where the last over-limit point of the section is located. The "Segment Deviation Peak Value" field stores the most severe deviation value within the section; the larger the peak value, the more severe the fault in the section. The "Segment Anomaly Type" field distinguishes whether the section is time-deviation-dominant, stroke-deviation-dominant, or a dual-deviation type. Time-dominant indicates a power transmission problem, stroke-dominant indicates an actuator problem, and dual-deviation indicates a combined problem of energy distribution and transmission efficiency. When the deviation anomaly section is an empty set, the "Total Number of Sections" field of the deviation characteristic parameter is set to 0, and the other fields are left empty. This state indicates that no deviation anomalies were detected.

[0047] Deviation diagnostic intervals are formed by characterizing abnormal deviation sections using deviation characteristic parameters. The abnormality type field of the section is read from the deviation characteristic parameters. Sections dominated by time deviation are marked as power transmission abnormalities. In this type of section, prolonged time indicates insufficient transmission chain stiffness or jamming; wear of connecting rod pins leads to increased transmission clearance. Shortened time indicates excessive energy storage or premature tripping; excessive preload of the energy storage spring causes excessively fast contact opening. Sections dominated by stroke deviation are marked as stroke execution abnormalities. In this type of section, insufficient stroke indicates transmission ratio mismatch or contact wear; contact erosion in the vacuum interrupter causes a shortened actual stroke; stroke overshoot indicates buffer mechanism failure or end-point limit abnormality; hydraulic buffer leakage causes contact impact with the rigid limit. Sections with dual deviations are marked as comprehensive performance abnormalities. This type of section indicates problems with energy input, transmission efficiency, and execution accuracy in this stage; decreased spring stiffness and increased transmission chain friction result in a slow and short acceleration phase. A deviation diagnosis interval is constructed based on three fields: the segment start index, the segment end index, and the anomaly type identifier. The deviation diagnosis interval consists of the spatial range and characteristic identifier of the deviation anomaly segment. The data structure of the deviation diagnosis interval includes the three fields: interval start index, interval end index, and anomaly type identifier. A segment with an interval start index of 1 is marked as an anomaly in the startup phase, indicating a problem with the energy release of the operating mechanism or the trip unit's operation. A segment with an interval end index of 4 is marked as an anomaly in the positioning phase, indicating a deterioration in the performance of the buffer device or limit device. An empty deviation diagnosis interval indicates that all operating phases are within the normal range and the equipment's mechanical characteristics are good.

[0048] The deviation diagnosis interval is constructed based on a time-travel deviation combination. The start and end indices of the deviation diagnosis interval define the spatial range of the abnormal segment. The interval with the start index of 1 is marked as the dominant abnormality in the start phase, the interval with the end index of 4 is marked as the dominant abnormality in the arrival phase, and the interval spanning indices 2-3 is marked as the abnormality in the high-speed movement phase. The deviation diagnosis intervals are grouped and statistically analyzed according to three categories: power transmission abnormality, travel execution abnormality, and comprehensive performance abnormality. The maximum value of the deviation peak value of each interval of the same type of abnormality is extracted as the representative peak value of the type of abnormality. The normalized interval number and the normalized value of the representative peak value are weighted and summed with weights of 0.4 and 0.6 to form the severity index of the type of abnormality. The reference value for the normalization of the interval number is the total number of stages, which is 4, and the reference value for the normalization of the deviation peak value is the value of the normal interval limit boundary. The severity index combination for the three types of anomalies is a three-dimensional vector forming a time-travel deviation combination. The three components of the vector are the severity of power transmission anomaly S_t, the severity of travel execution anomaly S_s, and the severity of overall performance anomaly S_c. While power transmission deviation may not be severe individually, and travel execution deviation may be within acceptable limits individually, the overall risk of the equipment increases significantly when both exist simultaneously. By calculating the magnitude of the three-dimensional vector, deviations from multiple dimensions can be combined into a single index, preventing multiple small problems from being ignored individually and accumulating into a major failure. The time-travel deviation combination includes deviation diagnosis interval data as auxiliary information, recording the specific location and type of anomaly.

[0049] In some embodiments, determining the fault level based on the time-travel deviation combination to form a state diagnosis result includes: quantifying the fault degree for the time-travel deviation combination to determine the fault severity level; obtaining diagnostic parameters corresponding to the fault severity level; performing type matching between the diagnostic parameters and the fault severity level to generate a fault classification identifier; and forming a state diagnosis result based on the fault classification identifier and the fault severity level.

[0050] The severity level of a fault is determined by quantifying the fault severity based on the time-travel deviation combination. The values ​​of three components, S_t, S_s, and S_c, are read from the time-travel deviation combination. The magnitude L = sqrt(S_t² + S_s² + S_c²) of the three-dimensional vector of the time-travel deviation combination is used as a comprehensive index of fault severity, where S_t represents the severity of power transmission abnormality, S_s represents the severity of travel execution abnormality, and S_c represents the severity of overall performance abnormality. A magnitude L less than 0.3 indicates a normal fault severity level, 0.3-0.5 indicates a minor fault level, 0.5-0.7 indicates a moderate fault level, 0.7-0.9 indicates a severe fault level, and greater than 0.9 indicates a critical fault level. Minor faults allow continued operation but require enhanced monitoring; moderate faults require a maintenance plan; faults with less than 8% travel require contact replacement during the next power outage; severe faults require immediate shutdown and maintenance; buffer failure leading to severe impact upon arrival requires emergency handling; critical faults require immediate shutdown; a 15ms increase in start-up time and less than 15% travel indicates trip unit failure and spring breakage risk, necessitating immediate shutdown. The values ​​of the three components of the time-travel deviation are checked one by one. If the severity of any dimension exceeds 0.8, even if the modulus length does not reach the threshold, the fault severity level is forcibly increased to the severe level. This rule prevents extreme anomalies in a single dimension from being masked by the vector averaging effect. The confidence level of the fault severity level is determined by combining data integrity and measurement signal-to-noise ratio within the deviation diagnosis interval. A confidence level below 0.7 is marked with an uncertain label after the level.

[0051] The system obtains diagnostic parameters corresponding to each fault severity level. It pre-configures a five-level diagnostic parameter template library, with one template for each severity level. Once the severity level is determined, the system indexes the template library based on the severity level: index 0 for normal, index 1 for minor faults, index 2 for moderate faults, index 3 for severe faults, and index 4 for critical faults. For minor faults and above, the system uses a dominant deviation dimension identification method from the template. This diagnostic parameter determines the dimension corresponding to the largest component by comparing the values ​​of the three components of the time-travel deviation combination. The dominant deviation dimension identifies the main deterioration direction of the equipment. For moderate faults and above, the system extracts anomaly location information extraction rules from the template. This diagnostic parameter reads data from the interval location field of the deviation diagnosis interval and maps it to the corresponding mechanical component. For example, the starting index of the deviation diagnosis interval for a circuit breaker is 2; the anomaly location information maps it to the energy storage spring drive system. This anomaly location information provides a basis for accurately locating the faulty component. The differentiated configuration of diagnostic parameters for different fault severity levels ensures the accuracy of the graded diagnosis.

[0052] The diagnostic parameters are matched with the fault severity level to generate fault classification labels. A dominant deviation dimension is extracted from the diagnostic parameters, and the dominant fault type is determined based on this dimension. When the dominant deviation dimension is the power transmission dimension, it corresponds to a power system fault; when it's the stroke execution dimension, it corresponds to a stroke system fault; and when it's the comprehensive performance dimension, it corresponds to a multi-system coupling fault. The fault severity level determines whether sub-type subdivision is performed. Normal and minor fault severity levels only output the dominant fault type without subdivision. Moderate and above fault severity levels are subdivided based on the anomaly location information in the diagnostic parameters. Power system faults are subdivided into insufficient energy storage, tripping delay, and transmission jamming. For example, if a circuit breaker's start-up time is delayed by 6ms, the anomaly location information in the diagnostic parameters subdivides it into a tripping delay fault. Stroke system faults are subdivided into contact wear, transmission ratio mismatch, and limit switch failure. Multi-system coupling faults are subdivided into overall aging and sudden damage. The fault classification identifier is constructed using a two-level coding format of "dimensional code - subclass code". The first-level code of the fault classification identifier represents the dominant fault dimension, and the second-level code represents the fault subtype. The fault classification identifier fully describes the fault type and specific degradation mode of the equipment.

[0053] The status diagnosis result is generated based on the fault classification identifier and fault severity level. The combined fault classification identifier and fault severity level constitute the diagnostic conclusion field of the status diagnosis result. The diagnostic conclusion field uses a structured text format and includes three pieces of information: fault type, severity, and confidence level. The deviation diagnosis interval data includes the location and type of each abnormal segment. The diagnostic parameters include the corresponding maintenance recommendations for each level. The complete data of the deviation diagnosis interval, the full content of the diagnostic parameters, and the current detection diagnostic timestamp are recorded in the detailed field of the status diagnosis result. The detailed field stores each data item in a key-value pair format, with the key name identifying the data type and the key value storing the actual content. The output for the normal level is "Equipment status is good, maintain routine monitoring"; for the minor fault level, it is output "Minor anomaly detected, it is recommended to shorten the monitoring cycle"; for the moderate fault level, it is output "Moderate fault risk exists, it is recommended to arrange maintenance"; for the severe fault level, it is output "Severe fault, it is recommended to shut down for maintenance as soon as possible"; and for the critical fault level, it is output "Critical fault, shut down immediately and initiate emergency response." The status diagnosis result records four core fields: diagnosis time, device number obtained from the data acquisition system, fault classification identifier, and fault severity level. The device number is associated with the device identity information, the diagnosis time marks the detection time, and the fault classification identifier and fault severity level describe the fault status. These four fields constitute a complete identifier for the status diagnosis result.

[0054] Step S140: Based on the condition diagnosis results, identify continuous deterioration characteristics to determine the wear acceleration area, identify the degree of bounce aggravation from the wear acceleration area to generate the bounce accumulation, and trigger the vibration signal spectrum refinement detection to identify high-frequency wear characteristics based on the bounce accumulation.

[0055] In some embodiments, identifying continuous degradation features and determining the wear acceleration region based on the condition diagnosis results includes: identifying a continuous degradation point time series from the condition diagnosis results; calculating adjacent intervals to generate a time interval series from the continuous degradation point time series; identifying periods of continuously shortening intervals from the time interval series to form an acceleration start point; and extending backward from the acceleration start point to the point where the intervals return to stability to determine the wear acceleration region.

[0056] Identify continuous degradation time series from condition diagnosis results. For a specific equipment number, retrieve all condition diagnosis result records from the system's historical database. The equipment number is used to isolate degradation data from different equipment. The database query returns all inspection records for the equipment since its commissioning. Each condition diagnosis result contains three core fields: diagnosis time, fault severity level, and fault classification identifier. Records are arranged in ascending order of diagnosis time to form a complete inspection history. Traverse the condition diagnosis result record sequence, filtering records where the fault severity level value is greater than the previous inspection result as degradation event points. The filtering process compares the fault severity levels of two adjacent inspections one by one. When the numerical code of the current level is greater than the numerical code of the previous level, it is determined as a degradation event. The level coding rules are: Normal Level 0, Minor Fault Level 1, Moderate Fault Level 2, Severe Fault Level 3, and Critical Fault Level 4. The fault classification identifier of the diagnosis is also recorded for each degradation event point to track the evolution of the degradation type. After extracting the diagnosis time, fault severity level, and fault classification identifier of the degradation event point, the data is sorted in ascending order by diagnosis time, forming a continuous degradation point time series. Each element in the time series contains three attribute fields: the diagnosis time field records the moment the degradation event occurred, the fault severity level field records the equipment status level at that moment, and the fault classification identifier field records the fault type code at that moment. For example, a switchgear jumping directly from a minor fault level to a severe fault level, skipping the medium fault level, suggests sudden damage such as spring breakage or linkage detachment. Trend analysis cannot be performed when the number of elements in the continuous degradation point time series is less than four, and wear acceleration identification is not performed in this case. Trend analysis is possible when the number of elements is four or more. The number of elements reflects the length of the equipment degradation process; a higher number indicates a more complete degradation stage experienced by the equipment.

[0057] A time interval sequence is generated by calculating adjacent intervals for the continuous degradation point time series. The continuous degradation point time series is traversed, starting from the first element to the second-to-last element. For each element, a time interval calculation is performed: the timestamp of the (i+1)th degradation point is subtracted from the timestamp of the ith degradation point to obtain the ith time interval. The time interval calculation formula is ΔT_i = T_(i+1) - T_i, where T is the diagnosis time and ΔT is the time interval. All time interval values ​​are obtained by traversing the sequence. The time interval values ​​are arranged in the order of degradation to form a time interval sequence. The length of the time interval sequence is equal to the length of the continuous degradation point time series minus 1; 5 degradation points generate 4 time intervals. The time interval sequence is organized as an array. The array index corresponds to the degradation stage number, and the array element value is the time interval for that stage. The first array element corresponds to the interval from the first degradation to the second degradation, the second array element corresponds to the interval from the second degradation to the third degradation, and so on. The unit of measurement in the time interval sequence is days. The value reflects the frequency of degradation events. A large value indicates a low degradation frequency, and a small value indicates a high degradation frequency. The shortening of the time interval directly indicates that the time required for the equipment to deteriorate from one fault level to the next fault level is decreasing, that is, the degradation speed is accelerating.

[0058] Acceleration initiation points are identified by identifying periods of continuously shortening intervals in a time interval sequence. The sliding window method is applied to determine local trends in the time interval sequence. The sliding window width is set to three consecutive intervals. Choosing a window width of three intervals allows observation of short-term trends and filters out interference from single fluctuations. When the three intervals within the window satisfy a decreasing relationship of I1>I2>I3, the window is determined to be an acceleration trend window. The degradation point corresponding to the first interval I1 of the acceleration trend window is defined as a candidate acceleration initiation point. A candidate acceleration initiation point must meet a decreasing amplitude condition to be confirmed as a true acceleration initiation point. The decreasing amplitude condition requires (I1-I3) / I1>0.3, meaning the shortening amplitude of the first and last intervals exceeds 30%. This threshold is set based on engineering experience to avoid normal fluctuations being misjudged as acceleration. For example, if the degradation interval of a certain device decreases from 200 days to 180 days and then to 170 days, although it shows a decrease, the decrease is only 15%, which may be a normal fluctuation. The 30% decreasing amplitude threshold requires a significant difference between the first and last intervals to be determined as true acceleration. Candidate points that satisfy both the decreasing relationship and the decreasing magnitude are identified as acceleration start points, marking the moment when the equipment degradation rate begins to accelerate significantly. Multiple candidate acceleration start points may exist in the time interval sequence; the candidate point with the earliest timestamp is selected as the final acceleration start point, corresponding to the moment when accelerated degradation first occurs. The fault severity level corresponding to the acceleration start point is typically minor or moderate, at which level the equipment can continue to operate, but the degradation rate has already accelerated significantly.

[0059] The wear acceleration zone is determined by extending the acceleration starting point forward to the point where the interval stabilizes. The time interval sequence is traversed backward from the interval corresponding to the acceleration starting point, checking whether subsequent intervals continue to decrease or remain at a low level after shortening. Interval values ​​before the acceleration starting point are extracted from the continuous degradation point time series, and their average is calculated as the benchmark for the normal interval average. A continuous acceleration state is defined when subsequent intervals are consistently less than 70% of the normal interval average. The 70% threshold is set based on engineering experience; the normal interval average is 200 days. After acceleration, the interval stabilizes in the 120-140 day range, below the 140-day threshold. This state is considered part of the acceleration zone. Acceleration ends when subsequent intervals recover to above 90% of the normal interval average. The 90% threshold is also set based on engineering experience. The end of acceleration may correspond to improved equipment condition after maintenance or a plateau phase after degradation reaches a steady state. The time range from the acceleration starting point to the acceleration ending point is defined as the wear acceleration zone. The wear acceleration zone records two attributes: the starting time and the duration. The starting time marks the point when the equipment enters rapid degradation, and the duration reflects the length of the accelerated degradation phase. If the accelerated wear phase ends before any time interval sequence is reached, the termination time of the accelerated wear region is set to the current time, indicating that the equipment is still in the accelerated degradation phase. In this state, equipment risks continue to accumulate and require close monitoring. An empty set in the accelerated wear region indicates that the equipment degradation process has not shown significant acceleration, and the degradation rate is relatively stable, belonging to a normal aging mode.

[0060] The degree of bounce severity is identified from the wear-accelerated area to generate a cumulative bounce value. Vibration and stroke signal data within the wear-accelerated area are retrieved from the system's historical database for bounce characteristic analysis. The historical database stores complete vibration and stroke signals collected during each test, organized by equipment number and test time index. Reverse displacement fluctuations in the stroke signal within a 50ms time window after the contact is fully closed are identified as bounce events. The threshold for judging a bounce event is a reverse stroke change exceeding 0.5mm and a duration exceeding 1ms. The threshold setting is determined based on engineering experience. After the contact is fully closed, mechanical vibration may cause a slight rebound, but micro-vibrations with a rebound amplitude of less than 0.5mm or a very short duration do not constitute true bounce separation. The threshold filters out normal mechanical vibration noise and only identifies observable separation and re-closing processes of the contact. The number of bounce events detected in a single closing operation is recorded as the bounce count. A bounce count exceeding 3 is considered severe bounce. The threshold of 3 is set based on engineering experience. The number of bounces detected in each test within the wear acceleration zone is extracted to form a bounce sequence. For a certain vacuum circuit breaker, the number of bounces in five consecutive tests during the wear acceleration phase were 2, 3, 5, 7, and 9, showing an increasing trend. The cumulative bounce value is obtained by summing the values ​​of the bounce sequence, reflecting the cumulative severity of the bounce problem within the wear acceleration zone. An increase in the cumulative bounce value reflects the continuous deterioration of the contact surface condition; a larger cumulative value indicates more severe contact surface roughness or more significant deterioration of spring buffer characteristics. The cumulative bounce value is accompanied by information on the time range of the wear acceleration zone, and the two sets of data are output together.

[0061] The vibration signal spectrum refinement detection is triggered based on the cumulative bounce amount to identify high-frequency wear characteristics. It checks if the cumulative bounce amount exceeds a threshold of 15 bounces. If it does, spectrum refinement detection is triggered; otherwise, it skips high-frequency wear feature extraction and directly outputs the current diagnostic result. The threshold of 15 bounces is set based on engineering experience as the critical point where significant deterioration of the contact surface occurs. Spectrum refinement detection performs high-resolution spectrum analysis on the vibration signal within a 100ms period before and after the contact's arrival. The vibration signal during this period is extracted from the most recent detection record within the wear acceleration region. Fourier transform is applied to the vibration signal during this period to obtain the spectrum. The spectral resolution is set to 10Hz, and the spectrum analysis range is 0-5kHz, covering the main frequency components generated by contact collision and surface friction. The energy proportion in the 3-5kHz frequency band of the spectrum is defined as the high-frequency energy ratio. A high-frequency energy ratio exceeding 15% indicates increased contact surface roughness. Microscopic protrusions formed after contact surface ablation generate additional high-frequency vibration components during collision, and the increase in the high-frequency energy ratio directly reflects the degree of surface roughness deterioration. Isolated peak frequencies appearing in the spectrum are identified as characteristic frequencies. The amplitude and frequency position of a characteristic frequency form a spectral feature pair. The reciprocal of the half-width at half-maximum (WHM) of the characteristic frequency peak is defined as the quality factor. A high quality factor indicates the purity of the frequency component. The spectral feature pair and its corresponding quality factor together constitute the characteristic frequency component. High-frequency wear characteristics consist of the characteristic frequency component and the high-frequency energy ratio. The characteristic frequency component includes three attributes: frequency value, amplitude value, and quality factor. The characteristic frequency range of high-frequency wear characteristics is usually concentrated in the 3.2-4.8 kHz range.

[0062] Step S150: Correlation detection is performed between high-frequency wear characteristics and contact action timing characteristics to generate frequency-time correlation coefficient. Based on the frequency-time correlation coefficient, the reliability of the condition diagnosis results is corrected and the mechanical characteristic evaluation results are output.

[0063] In some embodiments, the step of generating a frequency-time correlation coefficient by performing correlation detection between the high-frequency wear characteristics and the contact action timing characteristics includes: obtaining the characteristic frequency components of the high-frequency wear characteristics and the time-domain characteristic parameters of the contact action timing characteristics; establishing a frequency-domain-time domain correlation between the characteristic frequency components and the time-domain characteristic parameters; evaluating the correlation between the frequency domain and the time domain based on the frequency-domain-time domain correlation to generate a correlation value; and weighting and correcting the correlation value according to the amplitude of the high-frequency wear characteristics to generate a frequency-time correlation coefficient.

[0064] The characteristic frequency components of high-frequency wear features and the time-domain characteristic parameters of contact action timing features are obtained. The characteristic frequency component field is extracted from the high-frequency wear feature data structure. The frequency value, amplitude value, and quality factor of each characteristic frequency point in the high-frequency wear feature are extracted to form the characteristic frequency component. The high-frequency energy ratio field included in the high-frequency wear feature does not participate in correlation detection; only the characteristic frequency component participates in correlation analysis. The frequency value locates the frequency band position of the wear feature, the amplitude value reflects the severity of wear, and the quality factor reflects the sharpness of the frequency peak, characterizing the purity of the frequency. If a device detects that the quality factor of a frequency point is significantly higher than other frequency points, this high quality factor peak corresponds to localized ablation at a specific location on the contact surface. The reciprocal of the frequency of the characteristic frequency component is converted into the corresponding period, and the period value is used for comparison with the time interval as a multiple. The time field of each effective node is extracted from the structure array of the contact action timing features. The time interval mean and variance of adjacent effective nodes in the contact action timing features are calculated statistically. The node type field included in the contact action timing features is not included in the calculation of time domain characteristic parameters. The time interval mean is obtained by averaging the time differences of all adjacent nodes, and the variance is calculated by dividing the sum of the squares of the deviations of each time interval from the mean by the number of intervals. The time interval mean and variance are defined as time domain characteristic parameters. Among the time domain characteristic parameters, the time interval mean reflects the average action cycle, and the variance reflects the stability of the action timing. A small time interval variance for one device indicates that the timing of each action is highly consistent, while a large variance for another device indicates significant fluctuations in the action timing, suggesting an increase in transmission chain clearance or uneven spring stiffness.

[0065] A frequency-time domain correlation was established between the characteristic frequency components and the time-domain characteristic parameters. The period values ​​of the characteristic frequency components were checked against the average time interval of the time-domain characteristic parameters. When the period was an integer multiple of the time interval, or the time interval was an integer multiple of the period, an overtone correlation was established. The contact actuation time interval of 8 ms corresponds to a fundamental frequency of 125 Hz. The characteristic frequency of 3.75 kHz appearing in the spectrum is exactly 30 times the fundamental frequency. This overtone correlation indicates that the high-frequency vibration is not random noise, but a higher-order harmonic excited by low-frequency mechanical action, confirming the physical correlation between the frequency domain characteristics and the time-domain action. The overtone correlation reflects that the high-frequency vibration is a higher-order harmonic of the low-frequency mechanical action. An inverse correlation analysis was performed between the quality factor of the characteristic frequency components and the variance of the time-domain characteristic parameters. A high quality factor and small time-domain variance indicate a clean frequency domain and stable time domain. For example, a device with a high characteristic frequency quality factor and small time-domain variance indicates sharp and clean frequency peaks and stable and repetitive time-domain actions, reflecting good mechanical condition. Conversely, a device with a low quality factor and large variance, exhibiting frequency domain heterogeneity and large time-domain fluctuations, suggests mechanical deterioration. This combination corresponds to mechanical deterioration. A low quality factor or large time-domain variance indicates frequency domain heterogeneity or time-domain fluctuations, corresponding to mechanical deterioration or measurement interference. Frequency-domain and time-domain correlations are represented by an correlation matrix. The matrix rows correspond to the characteristic frequency components, the columns to the time-domain characteristic parameters, and the elements represent the correlation strength values. The frequency-domain and time-domain correlation matrix has dimensions M×N, where M is the number of characteristic frequency components and N is a fixed 2 (mean and variance) for the number of time-domain characteristic parameters. The element in the i-th row and j-th column of the frequency-domain and time-domain correlation matrix represents the correlation strength between the i-th characteristic frequency component and the j-th time-domain characteristic parameter. The correlation strength value is determined based on a combination of period-interval matching degree and quality factor-variance consistency. Positions with high matching degree and good consistency are assigned a correlation strength of 1, positions with low matching degree or poor consistency are assigned a correlation strength of 0, and intermediate cases are assigned continuous values ​​between 0 and 1.

[0066] The correlation between the frequency and time domains is evaluated based on the frequency-time domain correlation, generating a correlation value. The total correlation strength is obtained by summing all elements of the frequency-time domain correlation matrix. Dividing the total correlation strength by the total number of matrix elements yields the correlation density, which reflects the overall tightness of the frequency-time domain correlation. A correlation density greater than 0.5 indicates a tight correlation, while a density less than 0.3 indicates a sparse correlation. The threshold is set based on engineering experience. The diagonal elements in the frequency-time domain correlation matrix reflect the correlation between features with the same index. A large sum of diagonal elements indicates a strong sequential correspondence; for example, the first frequency component is strongly correlated with the first time-domain parameter, and the second with the second, indicating good consistency in feature extraction. Off-diagonal elements reflect cross-correlation. A large number of off-diagonal elements indicates a complex correlation pattern, where a frequency component is simultaneously correlated with multiple time-domain parameters, suggesting that the frequency domain features are affected by multiple mechanical actions. The correlation value R is determined by three factors: the correlation density ρ, the proportion of the main diagonal elements to the total correlation strength d, and the number of characteristic frequency components M. The calculation formula is R = ρ × (1 + α × d) × log(M + 1), where α is a weighting coefficient set to 0.5, and log is a commonly used logarithm. The correlation value is normalized to the 0-1 interval; a larger value indicates a higher degree of correlation between the frequency domain and the time domain.

[0067] The correlation coefficient is generated by weighting the correlation values ​​based on the amplitude of high-frequency wear characteristics. The amplitude of each characteristic frequency point in the high-frequency wear characteristics is normalized to form an amplitude weight vector. The normalization method is to divide each amplitude by the largest amplitude. The elements of the amplitude weight vector range from 0 to 1. The amplitude weight vector is multiplied row by row by row of the frequency-time domain correlation matrix. The result is a weighted correlation strength vector. The weighting operation highlights the correlation contribution of characteristic frequency points with large amplitudes. In a device's spectrum, multiple characteristic frequencies with significant amplitude differences are detected. Without weighting, all frequencies would be treated equally, but the actual dominant frequency corresponds to the main wear characteristics. Amplitude weighting increases the correlation contribution weight of the dominant frequency, preventing secondary frequencies from diluting the main information. The weighted correlation strength vectors are summed to obtain the total weighted correlation strength. Dividing the total weighted correlation strength by the amplitude weights yields the weighted correlation density, which reflects the actual correlation degree after considering the amplitude effect. The correlation coefficient C is determined by extracting the correlation value R and the weighted association density ρ_w. The correlation coefficient C is calculated as the geometric mean of the correlation value and the weighted association density, using the formula C = sqrt(R × ρ_w), where R is the correlation value and ρ_w is the weighted association density. Each of the correlation value and the weighted association density contributes 50% to the correlation coefficient. The correlation coefficient ranges from 0 to 1. A value greater than 0.7 indicates a strong correlation, meaning the frequency and time domain features are highly consistent. A value between 0.4 and 0.7 indicates a moderate correlation, meaning there is a correlation but the consistency is generally poor. A value less than 0.4 indicates a weak correlation, meaning the correlation is poor or the frequency domain features are affected by noise. The threshold is set based on engineering experience. The correlation coefficient includes a confidence assessment. The confidence level is determined by combining the quality factor of the characteristic frequency components and the measured signal-to-noise ratio of the time-domain characteristic parameters. A low-confidence indicator is added after the correlation coefficient when the confidence level is below 0.6.

[0068] The mechanical characteristic assessment results are output after confidence correction based on the frequency-time correlation coefficient. When the frequency-time correlation coefficient exceeds 0.7, the high-frequency wear characteristics are considered genuine and reliable, and the confidence level of the condition diagnosis result is increased by multiplying the original value by 1.1. If the increased confidence level is greater than 1, it is forcibly truncated to 1. When the frequency-time correlation coefficient is less than 0.4, the high-frequency wear characteristics may be due to noise contamination, and the confidence level of the condition diagnosis result is decreased by multiplying the original value by 0.85. If a high-frequency wear characteristic is identified in a test, but the frequency-time correlation coefficient is very low, it indicates a weak correlation between the frequency domain characteristics and the time domain action. These high-frequency components may originate from interference from nearby equipment or sensor electromagnetic noise rather than actual wear of the equipment itself. Therefore, the confidence level of the diagnosis result is reduced, and it is recommended to re-collect data to investigate the interference source. In this case, it is recommended to re-collect data to investigate sensor malfunctions. The fault severity level of the condition diagnosis result, combined with the corrected confidence level, forms the core field of the mechanical characteristic assessment result, which adopts the "level-confidence level" format. The mechanical characteristic assessment results also include four detailed fields: the start time and duration of the wear acceleration zone, the value of cumulative bounce, the frequency distribution of high-frequency wear characteristics, and the frequency-time correlation coefficient. The start time of the wear acceleration zone marks the point at which the equipment enters rapid degradation. The output structure of the mechanical characteristic assessment results includes two levels: core fields and detailed fields. The core fields provide a brief assessment of the fault level and confidence level, while the detailed fields provide complete information on wear acceleration, bounce accumulation, frequency domain characteristics, and correlation coefficients. The mechanical characteristic assessment results are output in a structured data format, containing the assessment time, equipment number, core fields, and detailed fields.

[0069] To implement the multi-dimensional mechanical characteristic testing method for switchgear corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See also Figure 3 , Figure 3 This diagram illustrates a structural block diagram of a multi-dimensional mechanical characteristic testing device 300 for a switching device according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The multi-dimensional mechanical characteristic testing device 300 for a switching device provided in this embodiment includes: Signal acquisition module 301 is used to acquire vibration signals and travel signals of switching equipment during opening and closing, and to determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal; The feature extraction module 302 is used to identify a set of vibration feature points by detecting the peak amplitude of the vibration signal in the mechanical characteristic detection window, identify a set of stroke change points by detecting the slope change of the stroke signal, and time-couple the set of vibration feature points and the set of stroke change points to form contact action timing features. The fault diagnosis module 303 is used to calculate the action time offset and stroke integrity from the action timing characteristics of the contact, perform deviation characteristic analysis on the action time offset and stroke integrity to form a time-stroke deviation combination, and determine the fault level to form a state diagnosis result based on the time-stroke deviation combination. Wear analysis module 304 is used to identify continuous deterioration characteristics and determine wear acceleration areas based on the condition diagnosis results, identify the degree of bounce aggravation from the wear acceleration areas to generate bounce accumulation, and trigger vibration signal spectrum refinement detection to identify high-frequency wear characteristics based on the bounce accumulation. The result output module 305 is used to perform correlation detection between the high-frequency wear characteristics and the contact action timing characteristics to generate a frequency-time correlation coefficient, and to perform credibility correction on the state diagnosis result based on the frequency-time correlation coefficient to output the mechanical characteristic evaluation result.

[0070] The aforementioned multi-dimensional mechanical characteristic testing device 300 for switching equipment can implement a multi-dimensional mechanical characteristic testing method for switching equipment according to the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining contents of this application embodiment can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0071] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0072] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for testing the multi-dimensional mechanical characteristics of a switchgear, characterized in that, include: Collect vibration signals and travel signals of the switching equipment during opening and closing, and determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal; The vibration signal peak amplitude of the mechanical characteristic detection window is used to identify a set of vibration feature points, and the slope change of the stroke signal is used to identify a set of stroke change points. The vibration feature point set and the stroke change point set are time-coupled to form the contact action timing feature. The action time offset and stroke integrity are calculated from the contact action timing characteristics. The action time offset and stroke integrity are analyzed for deviation characteristics to form a time-stroke deviation combination. The fault level is determined based on the time-stroke deviation combination to form a state diagnosis result. Based on the condition diagnosis results, continuous deterioration characteristics are identified to determine the wear acceleration area. The degree of bounce aggravation is identified from the wear acceleration area to generate a bounce accumulation. The vibration signal spectrum is triggered to refine the detection and identify high-frequency wear characteristics based on the bounce accumulation. The high-frequency wear characteristics and the contact action timing characteristics are correlated to generate a frequency-time correlation coefficient. Based on the frequency-time correlation coefficient, the reliability of the state diagnosis results is corrected and the mechanical characteristic evaluation results are output.

2. The method according to claim 1, characterized in that, The step of determining the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the stroke signal includes: Establish a synchronization time marker by obtaining the timestamps from the first peak point of the vibration signal and the first inflection point of the travel signal; The vibration signal amplitude characteristics are collected by tracing back from the synchronization time marker to identify the operation power level; The duration of the window is determined by matching the preset window duration corresponding to the operating power level. A mechanical characteristic detection window is constructed based on the synchronization time identifier and the window duration.

3. The method according to claim 1, characterized in that, The step of temporally coupling the set of vibration feature points with the set of stroke abrupt change points to form the contact action timing feature includes: Read the time stamp of each feature point from the set of vibration feature points and the set of stroke abrupt change points; The vibration-stroke trigger sequence is identified by performing a chronological analysis on the time stamps. Mechanical transmission delay parameters are generated based on the time intervals at each point in the vibration-stroke trigger sequence. The contact action timing characteristics are constructed by modifying the correlation based on the mechanical transmission delay parameters.

4. The method according to claim 1, characterized in that, The step of performing deviation characteristic analysis on the action time offset and the stroke integrity to form a time-stroke deviation combination includes: A deviation correlation analysis is performed between the action time offset and the stroke completeness to generate a time-stroke characteristic distribution; Deviation feature parameters are generated by identifying abnormal deviation segments from the time-travel characteristic distribution; The deviation characteristic parameters are used to identify the abnormal deviation segments to form a deviation diagnosis interval. Based on the aforementioned deviation diagnosis interval, a time-travel deviation combination is constructed.

5. The method according to claim 1, characterized in that, The process of determining the fault level based on the time-travel deviation combination to form a state diagnosis result includes: The severity level of the fault is determined by quantifying the fault degree based on the time-travel deviation combination. Based on the severity level of the fault, obtain the diagnostic parameters corresponding to the level; The diagnostic parameters are matched with the fault severity level to generate a fault classification identifier; A status diagnosis result is formed based on the fault classification identifier and the fault severity level.

6. The method according to claim 1, characterized in that, The step of identifying continuous degradation characteristics and determining accelerated wear regions based on the condition diagnosis results includes: Identify the time series of consecutive degradation points from the condition diagnosis results; The adjacent intervals of the continuous degradation point time series are calculated to generate a time interval series; The acceleration initiation point is formed by identifying periods of continuously shortening intervals from the time interval sequence; The wear acceleration zone is determined by extending backward from the acceleration starting point to the point where the interval returns to stability.

7. The method according to claim 1, characterized in that, The step of generating a frequency-time correlation coefficient by performing correlation detection between the high-frequency wear characteristics and the contact action timing characteristics includes: Obtain the characteristic frequency components of the high-frequency wear characteristics and the time-domain characteristic parameters of the contact action timing characteristics; Establish a frequency-domain and time-domain correlation between the characteristic frequency components and the time-domain characteristic parameters; Based on the frequency-domain-time domain correlation, the correlation between the frequency domain and the time domain is evaluated, and a correlation value is generated. The correlation coefficient is generated by weighting and correcting the correlation value based on the amplitude of the high-frequency wear characteristics.

8. The method according to claim 3, characterized in that, The generation of mechanical transmission delay parameters based on the time intervals at each point in the vibration-stroke triggering sequence includes: From the vibration-stroke triggering sequence, determine multiple candidate vibration feature points corresponding to a single stroke mutation point; Calculate the time proximity between each of the multiple candidate vibration feature points and the single abrupt change point in the stroke; The feature point pairing result is determined by selecting the best pairing based on the time proximity. Mechanical transmission delay parameters are constructed based on the time interval between each point pair in the feature point pairing results.

9. The method according to claim 4, characterized in that, The step of identifying aberration segments from the time-travel characteristic distribution and generating deviation feature parameters includes: A deviation tolerance range is set for the time-travel characteristic distribution to determine the normal range limit; Select a set of deviation-exceeding points from the deviation distribution points that exceed the normal range limit; For the set of deviation exceeding the limit points, a continuity determination is performed to identify abnormal deviation sections; Deviation characteristic parameters are generated based on the deviation amplitude of the deviation anomaly segment.

10. A multi-dimensional mechanical characteristic testing device for switchgear, characterized in that, include: The signal acquisition module is used to acquire vibration signals and travel signals of the switching equipment during opening and closing, and to determine the mechanical characteristic detection window based on the first peak point of the vibration signal and the first inflection point of the travel signal. The feature extraction module is used to identify a set of vibration feature points by detecting the peak amplitude of the vibration signal in the mechanical characteristic detection window, identify a set of stroke change points by detecting the slope change of the stroke signal, and time-couple the set of vibration feature points and the set of stroke change points to form contact action timing features. The fault diagnosis module is used to calculate the action time offset and stroke integrity from the action timing characteristics of the contact, perform deviation characteristic analysis on the action time offset and stroke integrity to form a time-stroke deviation combination, and determine the fault level based on the time-stroke deviation combination to form a state diagnosis result. The wear analysis module is used to identify continuous deterioration characteristics and determine wear acceleration areas based on the condition diagnosis results, identify the degree of bounce aggravation from the wear acceleration areas to generate bounce accumulation, and trigger vibration signal spectrum refinement detection to identify high-frequency wear characteristics based on the bounce accumulation. The result output module is used to perform correlation detection between the high-frequency wear characteristics and the contact action timing characteristics to generate a frequency-time correlation coefficient, and to perform credibility correction on the state diagnosis results based on the frequency-time correlation coefficient to output mechanical characteristic evaluation results.

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