An acoustic wave analysis method and system for equipment fault location

Abstract

The present invention discloses a sound wave analysis method and system for equipment fault location, which relates to the technical field of power grid equipment fault analysis methods. Each normal spectrum curve group is respectively compared with the basic spectrum curve group for compliance, and based on the comparison results, the most compliant normal spectrum curve group with the highest degree of compliance is determined. The spectrum section corresponding to the difference factor is marked in the basic spectrum curve group. Based on the time sequence relationship of the spectrum sections marked with difference factors in different basic spectrum curve groups, the abnormal position block where the abnormality occurs on the equipment is determined. The spectrum section marked as abnormal on the basic spectrum curve group is analyzed to determine the abnormal situation of the abnormal position block. Through the above technical solutions, the present invention realizes the effect of using sound waves to locate the abnormal position block of the equipment and analyzes the abnormal characteristics of the abnormal position block, which is beneficial to making corresponding maintenance measures for the equipment in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid equipment fault analysis, and particularly to a sound wave analysis method and system for equipment fault location. Background Art

[0002] In the power grid system, the normal operation of equipment is crucial for ensuring the stability and security of power supply. However, due to reasons such as long-term operation, overload, and environmental factors, equipment may malfunction. Traditional fault detection methods mostly rely on manual inspections and historical data of equipment. These methods are often time-consuming and laborious, and it is difficult to detect, locate, and analyze faults in real time. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system capable of fault location and analysis for power grid equipment.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A sound wave analysis method for equipment fault location includes:

[0006] Based on the set positions of the sound wave acquisition device relative to the equipment, a sound wave spatial acquisition array is constructed. The sound wave spatial acquisition array includes sound wave acquisition nodes at different spatial position nodes, and the sound wave data collected by the sound wave acquisition device is respectively recorded in the manner of associating with the sound wave acquisition nodes, obtaining a number of sound wave data sets;

[0007] Using time-frequency analysis technology to analyze the sound wave data sets, a number of basic frequency spectrum curves are obtained, and the combination of the basic frequency spectrum curves belonging to the same sound wave data set is identified as a basic frequency spectrum curve group;

[0008] For the normal operation state of the equipment, a number of normal frequency spectrum curve groups are preset. Each normal frequency spectrum curve group is respectively compared with the basic frequency spectrum curve group for conformity, and based on the comparison results, the most conforming normal frequency spectrum curve group with the highest degree of conformity is determined;

[0009] The most conforming normal frequency spectrum curve group is compared with the basic frequency spectrum curve group to determine a number of difference factors. If the difference factors are within the preset normal range, it is determined that the equipment has no abnormality. If the difference factors are not within the preset normal range, it is determined that the equipment has an abnormality, and the frequency spectrum section corresponding to the difference factor is marked in the basic frequency spectrum curve group;

[0010] Based on the time sequence relationship of the frequency spectrum sections marked with difference factors in different basic frequency spectrum curve groups, the abnormal position block where the abnormality occurs on the equipment is determined, and using the preset abnormal frequency spectrum analysis model, the abnormal frequency spectrum section marked with abnormality on the basic frequency spectrum curve group is analyzed to determine the abnormal situation of the abnormal position block.

[0011] In some embodiments disclosed by the present invention, the method for comparing the compliance of each normal spectrum curve group with the basic spectrum curve group respectively includes:

[0012] Perform corresponding grouping comparison on each normal spectrum curve in the normal spectrum curve group and each basic spectrum curve in the basic spectrum curve group, perform deformation on the basic spectrum curve within a preset range, perform relative dynamic translation on the normal spectrum curve and the basic spectrum curve in the time axis for each spectrum curve comparison group, and the translation methods of the normal spectrum curve and the basic spectrum curve within each spectrum curve comparison group are the same. Calculate the coincidence parameter between the normal spectrum curve and the basic spectrum curve in real time after each dynamic translation, and determine the translation method corresponding to the highest coincidence parameter as the coincidence translation method;

[0013] Based on the coincidence translation method, translate the normal spectrum curve and the basic spectrum curve on the time axis, and determine the sub-compliance degree of the corresponding spectrum curve comparison group based on the compliance characteristics between the translated normal spectrum curve and the basic spectrum curve. And determine the compliance degree between the normal spectrum curve group and the basic spectrum curve group based on the sub-compliance degrees of all spectrum curve comparison groups.

[0014] In the embodiments disclosed by the present invention, the method for determining the compliance degree between the normal spectrum curve group and the basic spectrum curve group includes:

[0015] Based on the equipment components preset corresponding to the normal spectrum curve in the spectrum curve comparison group, determine the compliance weight coefficient for the spectrum curve comparison group, and calculate the compliance degree between the normal spectrum curve group and the basic spectrum curve group in combination with the sub-compliance degree of the spectrum curve comparison group;

[0016] Among them, the method for determining the sub-compliance degree of the spectrum curve comparison group includes calculating the peak difference amount between the normal spectrum curve and the basic spectrum curve, and determining the number of spectrum line intersections between the normal spectrum curve and the basic spectrum curve within a preset time period. Based on the peak difference amount and the number of spectrum line intersections, determine the sub-compliance degree of the spectrum curve comparison group;

[0017] Among them, the expression for calculating the compliance degree is:

[0018] ;

[0019] Among them, is the compliance degree corresponding to the spectrum curve comparison group, is the compliance weight coefficient corresponding to the i-th equipment component, that is, the compliance weight coefficient corresponding to the i-th spectrum curve comparison group, is the sub-compliance degree corresponding to the i-th spectrum curve comparison group;

[0020] Among them, the expression for calculating the sub - matching degree is:

[0021] ;

[0022] Among them, is the first matching - degree adjustment coefficient, is the second matching - degree adjustment coefficient, is the peak - difference - quantity influence judgment function, is the peak - difference quantity, is a preset number of peak - difference - quantity intervals, According to the peak - difference quantity belonging to the peak - difference - quantity interval , output a specific first matching - degree parameter, is the spectral - line - crossing - number influence judgment function, u is the spectral - line - crossing number, is a preset number of spectral - line - crossing - number intervals, According to the spectral - line - crossing - number interval to which the spectral - line - crossing number u belongs output a specific second matching - degree parameter, b is the matching - degree adjustment constant.

[0023] In some embodiments disclosed by the present invention, the method for calculating the coincidence parameter of the normal spectral curve and the basic spectral curve includes:

[0024] Translate the time axis to the position of the central axis of the normal spectral curve and the basic spectral curve;

[0025] According to a preset time interval, set a number of time - mapping nodes on the time axis, and respectively for each time - mapping node, intercept the corresponding normal spectral - curve value and basic - spectral - curve value, and record the normal spectral - curve value and basic - spectral - curve value belonging to the same time - mapping node as a spectral - curve value group;

[0026] Analyze each spectral - curve value group. If both the normal spectral - curve value and the basic - spectral - curve value are positive or negative, then identify the smaller curve value among the normal spectral - curve value and the basic - spectral - curve value as the sub - coincidence parameter, and take the absolute value of the sub - coincidence parameter;

[0027] Sum all the sub - coincidence parameters to obtain the coincidence parameter of the normal spectral curve and the basic spectral curve.

[0028] In some embodiments disclosed by the present invention, the method for determining a number of difference factors includes:

[0029] One - to - one correspond the normal spectral curves in the most - matching normal - spectral - curve group and the basic spectral curves in the basic - spectral - curve group, and record each pair of corresponding normal spectral curve and basic spectral curve as a spectral - curve comparison group;

[0030] Deform the basic spectral curve in the basic spectral curve comparison group of the spectral curve comparison group. The deformation method adjusts the period and peak value of the basic spectral curve within a preset range to obtain a deformed spectral curve;

[0031] Align the normal spectral curve and the deformed spectral curve, construct a vertical scanning line, gradually move the vertical scanning line, and intercept the intersection points of the vertical scanning line with the normal spectral curve and the deformed spectral curve, record them as the normal spectral curve values and the deformed spectral curve values, and record the combination of the normal spectral curve values and the deformed spectral curve values as the analysis curve value group;

[0032] Sort the analysis curve value group according to the scanning front-back relationship of the vertical scanning line, calculate the curve value difference between the normal spectral curve value and the deformed spectral curve value in the analysis curve value group, and determine the difference factor parameter of the basic spectral curve relative to the normal spectral curve based on the continuous performance characteristics of the curve value difference. If the difference factor parameter is greater than or equal to the preset value, mark the difference factor for the section corresponding to the basic spectral curve.

[0033] In some embodiments disclosed by the present invention, the method for determining the abnormal position block where an abnormality occurs on the device based on the time sequence relationship of the spectral sections marked with the difference factor in different basic spectral curve groups includes:

[0034] A spatial coordinate system is established for the device, and based on the structural characteristics of the device, a corresponding virtual three-dimensional device structure is set in the spatial coordinate system, and the acoustic wave acquisition array is configured in the spatial coordinate system;

[0035] Analyze each basic spectral curve group corresponding to each device acquisition node respectively, determine the time node when each difference factor appears, sort the difference factors according to the time sequence relationship before and after, obtain a difference factor sequence, and mark the occurrence time node for the difference factors therein;

[0036] Compare the difference factor sequences corresponding to different device acquisition nodes, and calculate the occurrence time difference of the same difference factor between different device acquisition nodes;

[0037] Set several component blocks inside the virtual three-dimensional device structure, gradually measure the acoustic wave conduction distances of each component block relative to different device acquisition nodes, and calculate the difference in the acoustic wave conduction distances. If there is a component block whose difference in acoustic wave conduction distances matches the occurrence time difference of the same difference factor, it is determined that the component block is the abnormal position block, and the difference factor is associated with the abnormal position block.

[0038] In some embodiments disclosed by the present invention, the method for constructing an abnormal spectrogram analysis model includes:

[0039] Establish an equipment anomaly test, including an anomaly feature table set for each component, artificially set anomaly faults for the components corresponding to the equipment, collect anomaly acoustic wave data using an acoustic wave acquisition device, and record them separately according to the corresponding acoustic wave acquisition nodes to obtain several anomaly acoustic wave data sets;

[0040] Analyze the anomaly acoustic wave data sets to obtain several anomaly frequency spectrum curves, intercept the frequency spectrum sections on the anomaly frequency spectrum curves, and associate the anomaly frequency spectrum sections with the anomaly features to obtain an anomaly feature - frequency spectrum section relationship group;

[0041] Analyze the anomaly feature - frequency spectrum section relationship group to determine the feature labels of each anomaly feature - frequency spectrum section relationship group.

[0042] In some embodiments disclosed in the present invention, the feature labels of the anomaly feature - frequency spectrum section relationship group include: the component to which the anomaly feature belongs, the average frequency spectrum curve value of the frequency spectrum section, the upper and lower frequency spectrum curve peak values of the frequency spectrum section, and the average frequency spectrum curve period of the frequency spectrum section.

[0043] In some embodiments disclosed in the present invention, the method for classifying the frequency spectrum sections marked as anomalies on the basic frequency spectrum curve group includes:

[0044] Based on the determined anomaly position block, perform a first screening on the anomaly feature - frequency spectrum section relationship group, and based on the average frequency spectrum curve value, the upper and lower frequency spectrum curve peak values, and the average frequency spectrum curve period of the frequency spectrum sections marked as anomalies on the basic frequency spectrum curve group, perform a second screening on the anomaly feature - frequency spectrum section relationship group, and recognize the screened anomaly feature - frequency spectrum section relationship group as a reference anomaly feature - frequency spectrum section relationship group;

[0045] Recognize the anomaly features in the reference anomaly feature - frequency spectrum section relationship group as the anomaly conditions of the anomaly position block.

[0046] In some embodiments disclosed in the present invention, an acoustic wave analysis system for equipment fault location is also disclosed, including:

[0047] A first module for constructing an acoustic wave space acquisition array based on the set positions of the acoustic wave acquisition device relative to the equipment. The acoustic wave space acquisition array includes acoustic wave acquisition nodes at different spatial position nodes, and records the acoustic wave data collected by the acoustic wave acquisition device separately in a manner associated with the acoustic wave acquisition nodes to obtain several acoustic wave data sets;

[0048] A second module for analyzing the acoustic wave data sets using time - frequency analysis technology to obtain several basic frequency spectrum curves, and recognizing the combination of the basic frequency spectrum curves belonging to the same acoustic wave data set as a basic frequency spectrum curve group;

[0049] A third module is configured to preset a plurality of normal spectrum curve groups for the normal operating state of the device, compare each normal spectrum curve group with the basic spectrum curve group respectively, and determine the most matching normal spectrum curve group with the highest degree of matching based on the comparison result;

[0050] A fourth module is configured to compare the most matching normal spectrum curve group with the basic spectrum curve group to determine a number of difference factors. If the difference factors are within the preset normal range, it is determined that the device has no abnormality. If the difference factors are not within the preset normal range, it is determined that the device has an abnormality, and mark the spectrum section corresponding to the difference factors in the basic spectrum curve group;

[0051] A fifth module is configured to determine the abnormal position block where the abnormality occurs on the device based on the time sequence relationship of the spectrum sections marked with difference factors in different basic spectrum curve groups, and use the preset abnormal spectrum analysis model to analyze the spectrum sections marked with abnormalities in the basic spectrum curve group to determine the abnormal conditions of the abnormal position block.

[0052] The present invention discloses a sound wave analysis method and system for device fault location, which relates to the technical field of power grid device fault analysis methods. Each normal spectrum curve group is compared with the basic spectrum curve group respectively, and based on the comparison result, the most matching normal spectrum curve group with the highest degree of matching is determined. Mark the spectrum section corresponding to the difference factors in the basic spectrum curve group. Based on the time sequence relationship of the spectrum sections marked with difference factors in different basic spectrum curve groups, determine the abnormal position block where the abnormality occurs on the device, and analyze the spectrum sections marked with abnormalities in the basic spectrum curve group to determine the abnormal conditions of the abnormal position block. Through the above technical solutions, the present invention realizes the effect of using sound waves to locate the abnormal position block of the device and analyzes the abnormal characteristics of the abnormal position block, which is beneficial to making corresponding maintenance measures for the device in a timely manner. Description of the Drawings

[0053] Figure 1 A sound wave analysis method and system for device fault location proposed by the present invention. Detailed Embodiments

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0055] To achieve the above object, the present invention adopts the following technical solutions:

[0056] Refer to Figure 1 , a sound wave analysis method for device fault location, including:

[0057] Step S100: Based on the set positions of the acoustic wave acquisition device relative to the equipment, construct an acoustic wave spatial acquisition array. The acoustic wave spatial acquisition array includes acoustic wave acquisition nodes at different spatial position nodes, and record the acoustic wave data collected by the acoustic wave acquisition device in a manner associated with the relevant acoustic wave acquisition nodes to obtain a number of acoustic wave data sets.

[0058] In this step, in order to comprehensively capture the acoustic wave signals generated during the operation of the power grid equipment, acoustic wave acquisition nodes are installed at different spatial positions around the equipment to construct an acoustic wave spatial acquisition array; these nodes can collect acoustic wave data from multiple angles and distances, thereby obtaining more comprehensive acoustic wave information; the acoustic wave acquisition device will record this acoustic wave data and record it separately according to its association with each acoustic wave acquisition node, forming multiple acoustic wave data sets; such a recording method helps to accurately associate the acoustic wave data with specific position information during subsequent analysis.

[0059] Step S200: Use time-frequency analysis technology to analyze the acoustic wave data sets to obtain a number of basic spectral curves, and identify the combination of the basic spectral curves belonging to the same acoustic wave data set as a basic spectral curve group.

[0060] The acoustic wave data sets contain the acoustic wave signals generated during the operation of the equipment, and these signals carry rich information about the equipment status; time-frequency analysis technology can reveal the frequency content of the acoustic wave signals and their changes over time; by performing time-frequency analysis on each acoustic wave data set, a series of basic spectral curves can be obtained, and these curves show the frequency distribution characteristics of the acoustic wave signals; combining all the basic spectral curves belonging to the same acoustic wave data set forms a basic spectral curve group, providing a data basis for subsequent fault diagnosis.

[0061] Acoustic wave signals are composed of multiple waves with different frequencies, and each frequency may correspond to different operating states or fault modes of the equipment; the purpose of time-frequency analysis is to decompose complex acoustic wave signals into different frequency components and observe the changes of these components over time. Through time-frequency analysis, the spectral information of acoustic wave signals can be obtained, which is crucial for fault diagnosis and location.

[0062] Step S300: Preset a number of normal spectral curve groups for the normal operating state of the equipment, compare each normal spectral curve group with the basic spectral curve group respectively, and based on the comparison results, determine the most matching normal spectral curve group with the highest degree of matching.

[0063] To determine whether the operating state of the device is normal, a set of standards is required for reference. In this step, a series of normal spectrum curve groups are preset for the normal operating state of the device, and these curve groups represent the acoustic wave characteristics of the device during normal operation; then, each normal spectrum curve group is compared with the basic spectrum curve group for conformity. Through comparative analysis, the normal spectrum curve group that best matches the actually collected data is found. Such a comparison helps to understand whether the current operating state of the device deviates from the normal range.

[0064] In some embodiments disclosed by the present invention, the method of comparing each normal spectrum curve group with the basic spectrum curve group for conformity includes:

[0065] Step S301: Corresponding grouping comparison is performed between each normal spectrum curve in the normal spectrum curve group and each basic spectrum curve in the basic spectrum curve group, and the basic spectrum curve is deformed within a preset range. Relative dynamic translation is performed on the normal spectrum curve and the basic spectrum curve in the time axis for each spectrum curve comparison group, and the translation methods of the normal spectrum curve and the basic spectrum curve within each spectrum curve comparison group are the same. The coincidence parameter between the normal spectrum curve and the basic spectrum curve is calculated in real time after each dynamic translation, and the translation method corresponding to the highest coincidence parameter is determined as the coincidence translation method.

[0066] In some embodiments disclosed by the present invention, the method of calculating the coincidence parameter between the normal spectrum curve and the basic spectrum curve includes:

[0067] Step S3011: Translate the time axis to the position of the mid-axis of the normal spectrum curve and the basic spectrum curve.

[0068] Step S3012: Set a number of time mapping nodes on the time axis at a preset time interval, and for each time mapping node, intercept the corresponding normal spectrum curve value and basic spectrum curve value, and record the normal spectrum curve value and the basic spectrum curve value belonging to the same time mapping node as a spectrum curve value group.

[0069] Step S3013: Analyze each spectrum curve value group. If both the normal spectrum curve value and the basic spectrum curve value are positive or negative, the smaller curve value between the normal spectrum curve value and the basic spectrum curve value is determined as the sub-coincidence parameter, and the sub-coincidence parameter takes the absolute value.

[0070] Step S3014: Sum all the sub-coincidence parameters to obtain the coincidence parameter between the normal spectrum curve and the basic spectrum curve.

[0071] Step S302: Based on the coincidence translation method, translate the normal spectrum curve and the basic spectrum curve on the time axis, and determine the sub - coincidence degree of the corresponding spectrum curve comparison group based on the coincidence characteristics between the translated normal spectrum curve and the basic spectrum curve. Then, determine the coincidence degree between the normal spectrum curve group and the basic spectrum curve group based on the sub - coincidence degrees of all spectrum curve comparison groups.

[0072] In the embodiments disclosed in the present invention, the method for determining the coincidence degree between the normal spectrum curve group and the basic spectrum curve group includes:

[0073] Step S3021: Based on the device components preset for the normal spectrum curve in the spectrum curve comparison group, determine the coincidence weight coefficient for the spectrum curve comparison group, and calculate the coincidence degree between the normal spectrum curve group and the basic spectrum curve group in combination with the sub - coincidence degree of the spectrum curve comparison group.

[0074] Among them, the method for determining the sub - coincidence degree of the spectrum curve comparison group includes calculating the peak difference amount between the normal spectrum curve and the basic spectrum curve, and determining the number of spectrum line intersections between the normal spectrum curve and the basic spectrum curve within a preset time period. Based on the peak difference amount and the number of spectrum line intersections, determine the sub - coincidence degree of the spectrum curve comparison group.

[0075] Among them, the expression for calculating the coincidence degree is:

[0076] .

[0077] Among them, is the coincidence degree corresponding to the spectrum curve comparison group, is the coincidence weight coefficient corresponding to the i - th device component, that is, the coincidence weight coefficient corresponding to the i - th spectrum curve comparison group, is the sub - coincidence degree corresponding to the i - th spectrum curve comparison group;

[0078] Among them, the expression for calculating the sub - coincidence degree is:

[0079] .

[0080] Among them, is the first coincidence degree adjustment coefficient, is the second coincidence degree adjustment coefficient, is the peak difference amount influence judgment function, is the peak difference amount, is a preset number of peak difference amount intervals, According to the peak difference amount belonging to the peak difference amount interval , output a specific first coincidence degree parameter, is the function for judging the influence of the number of spectral line intersections, where u is the number of spectral line intersections. are several preset intervals of the number of spectral line intersections. According to the interval of the number of spectral line intersections to which the number of spectral line intersections u belongs output a specific second degree of conformity parameter, and b is the conformity adjustment constant.

[0081] Step S400: Compare the most conforming normal spectral curve group with the basic spectral curve group to determine several difference factors. If the difference factors are within the preset normal range, it is determined that the device is normal. If the difference factors are not within the preset normal range, it is determined that the device is abnormal, and mark the spectral section corresponding to the difference factor in the basic spectral curve group.

[0082] By comparing the most conforming normal spectral curve group and the basic spectral curve group, difference factors can be determined. These factors reflect the differences between the actual data and the normal data. If the difference factors are within the preset normal range, it can be considered that the operating state of the device is normal. However, if the difference factors exceed the normal range, this indicates that the device may be abnormal. In this case, the spectral sections corresponding to the difference factors will be marked in the basic spectral curve group, and these sections are where the potential fault signals are located.

[0083] In some embodiments disclosed by the present invention, the method for determining several difference factors includes:

[0084] Step S401: One-to-one correspond the normal spectral curves in the most conforming normal spectral curve group and the basic spectral curves in the basic spectral curve group, and record each pair of corresponding normal spectral curves and basic spectral curves as a spectral curve comparison group.

[0085] Step S402: Deform the basic spectral curves in the spectral curve comparison group. The deformation method adjusts the period and peak value of the basic spectral curve within the preset range to obtain a deformed spectral curve.

[0086] Step S403: Align the normal spectral curve and the deformed spectral curve, construct a vertical scanning line, gradually move the vertical scanning line, and intercept the intersection points of the vertical scanning line and the normal spectral curve and the deformed spectral curve, record them as the normal spectral curve values and the deformed spectral curve values, and record the combination of the normal spectral curve values and the deformed spectral curve values as an analysis curve value group.

[0087] Step S404: Sort the analysis curve value groups according to the scanning sequence of the vertical scanning lines, calculate the curve value difference between the normal frequency spectrum curve value and the deformed frequency spectrum curve value in the analysis curve value groups, and determine the difference factor parameter of the basic frequency spectrum curve relative to the normal frequency spectrum curve based on the continuous performance characteristics of the curve value difference. If the difference factor parameter is greater than or equal to the preset value, mark the difference factor for the section corresponding to the basic frequency spectrum curve.

[0088] Step S500: Determine the abnormal position block where the abnormality occurs on the device based on the time sequence of the spectrum sections marked with the difference factor in different basic frequency spectrum curve groups, and use the preset abnormal frequency spectrum analysis model to analyze the spectrum sections marked as abnormal in the basic frequency spectrum curve groups to determine the abnormal conditions of the abnormal position block.

[0089] The last step is to determine the specific position block where the abnormality occurs on the device; based on the time sequence of the spectrum sections marked with the difference factor in different basic frequency spectrum curve groups, the approximate location where the abnormality occurs can be inferred. Then, use the preset abnormal frequency spectrum analysis model to deeply analyze the spectrum sections marked as abnormal to determine the specific abnormal conditions of the abnormal position block; such analysis helps to detect faults and also understand the nature and severity of the faults, providing guidance for maintenance and repair.

[0090] In some embodiments disclosed in the present invention, the method for determining the abnormal position block where the abnormality occurs on the device based on the time sequence of the spectrum sections marked with the difference factor in different basic frequency spectrum curve groups includes:

[0091] Step S501: Establish a spatial coordinate system for the device, set a corresponding virtual three-dimensional device structure in the spatial coordinate system based on the structural characteristics of the device, and configure the acoustic wave acquisition array in the spatial coordinate system.

[0092] Step S502: Analyze each basic frequency spectrum curve group corresponding to the device acquisition nodes respectively, determine the time nodes when each difference factor appears, sort the difference factors according to the time sequence, obtain a difference factor sequence, and mark the occurrence time nodes for the difference factors therein.

[0093] Step S503: Compare the difference factor sequences corresponding to different device acquisition nodes, and calculate the occurrence time difference of the same difference factor between different device acquisition nodes.

[0094] Step S504: Set several component blocks inside the virtual three-dimensional device structure, gradually measure the acoustic wave conduction distances between each component block and different device acquisition nodes, calculate the difference amounts between the acoustic wave conduction distances. If the difference amount between the acoustic wave conduction distances corresponding to a certain component block matches the occurrence time difference amount of the same difference factor, then determine that the component block is an abnormal position block, and associate the difference factor with the abnormal position block.

[0095] In some embodiments disclosed in the present invention, the method for constructing an abnormal frequency spectrum diagram analysis model includes:

[0096] Step S505: Establish a device abnormality test, including an abnormal feature table set for each component, artificially set abnormal faults for the components corresponding to the device, use an acoustic wave acquisition device to acquire abnormal acoustic wave data, and record them separately according to the corresponding acoustic wave acquisition nodes to obtain several abnormal acoustic wave data sets.

[0097] In this step, by artificially setting abnormal faults of the device components, the possible fault conditions during the actual operation of the device are simulated; these abnormal faults can be preset or determined according to the historical fault data of the device; after setting the abnormal faults, use an acoustic wave acquisition device to acquire the acoustic wave data in these abnormal states; these data reflect the acoustic wave characteristics of the device components in the abnormal state and are very important for subsequent fault diagnosis and positioning.

[0098] Step S506: Analyze the abnormal acoustic wave data sets to obtain several abnormal frequency spectrum curves, intercept the frequency spectrum sections on the abnormal frequency spectrum curves, and associate the abnormal frequency spectrum sections with the abnormal features to obtain an abnormal feature - frequency spectrum section relationship group.

[0099] After obtaining the acoustic wave data in the abnormal state, it is necessary to perform time-frequency analysis on these data to extract the abnormal frequency spectrum curves; these frequency spectrum curves show the frequency distribution of the acoustic wave signals in the abnormal state; by analyzing these frequency spectrum curves, the frequency spectrum sections related to the abnormal features can be intercepted; these abnormal frequency spectrum sections are associated with the corresponding abnormal features to form an abnormal feature - frequency spectrum section relationship group; these relationship groups contain the acoustic wave feature information in the abnormal state and are crucial for constructing the abnormal frequency spectrum diagram analysis model.

[0100] Step S507: Analyze the abnormal feature - frequency spectrum section relationship group to determine the feature labels of each abnormal feature - frequency spectrum section relationship group.

[0101] The last step is to analyze the abnormal feature - frequency spectrum section relationship groups to determine the feature labels for each relationship group; the feature labels are descriptions of the abnormal frequency spectrum sections, which include information such as the component to which the abnormal feature belongs, the average frequency spectrum curve value of the frequency spectrum section, the upper and lower frequency spectrum curve peak values of the frequency spectrum section, and the average frequency spectrum curve period of the frequency spectrum section; these feature labels help the model identify and interpret abnormal patterns in the acoustic wave data, thereby accurately diagnosing and locating equipment faults.

[0102] In some embodiments disclosed by the present invention, the feature labels of the abnormal feature - frequency spectrum section relationship groups include: the component to which the abnormal feature belongs, the average frequency spectrum curve value of the frequency spectrum section, the upper and lower frequency spectrum curve peak values of the frequency spectrum section, and the average frequency spectrum curve period of the frequency spectrum section.

[0103] In some embodiments disclosed by the present invention, the method for dividing the frequency spectrum sections marked as abnormal on the basic frequency spectrum curve group includes:

[0104] Step S508, based on the determined abnormal position block, perform a first screening on the abnormal feature - frequency spectrum section relationship groups, and based on the average frequency spectrum curve value, the upper and lower frequency spectrum curve peak values, and the average frequency spectrum curve period of the frequency spectrum sections marked as abnormal on the basic frequency spectrum curve group, perform a second screening on the abnormal feature - frequency spectrum section relationship groups, and identify the screened abnormal feature - frequency spectrum section relationship groups as the reference abnormal feature - frequency spectrum section relationship groups.

[0105] Step S509, identify the abnormal features in the reference abnormal feature - frequency spectrum section relationship groups as the abnormal conditions of the abnormal position block.

[0106] In some embodiments disclosed by the present invention, an acoustic wave analysis system applied to equipment fault location is also disclosed, including:

[0107] The first module is used to construct an acoustic wave spatial acquisition array based on the set position of the acoustic wave acquisition device relative to the equipment. The acoustic wave spatial acquisition array includes acoustic wave acquisition nodes at different spatial position nodes, and records the acoustic wave data collected by the acoustic wave acquisition device in the way associated with the acoustic wave acquisition nodes, obtaining a number of acoustic wave data sets;

[0108] The second module is used to analyze the acoustic wave data sets by using time - frequency analysis technology to obtain a number of basic frequency spectrum curves, and identify the combination of the basic frequency spectrum curves belonging to the same acoustic wave data set as the basic frequency spectrum curve group;

[0109] The third module is used to pre - set a number of normal frequency spectrum curve groups for the normal operation state of the equipment, compare each normal frequency spectrum curve group with the basic frequency spectrum curve group respectively for compliance, and based on the comparison results, determine the most compliant normal frequency spectrum curve group with the highest degree of compliance;

[0110] A fourth module is configured to compare the most matching normal spectrum curve group with the basic spectrum curve group to determine a number of difference factors. If the difference factors are within a preset normal range, it is determined that the device is normal. If the difference factors are not within the preset normal range, it is determined that the device has an abnormality, and the spectrum section corresponding to the difference factor is marked in the basic spectrum curve group.

[0111] A fifth module is configured to determine the abnormal position block where the abnormality occurs on the device based on the time sequence relationship of the spectrum sections marked with difference factors in different basic spectrum curve groups, and use a preset abnormal spectrum analysis model to analyze the spectrum sections marked as abnormal in the basic spectrum curve group to determine the abnormal conditions of the abnormal position block.

[0112] The present invention discloses a sound wave analysis method and system for equipment fault location, which relates to the technical field of power grid equipment fault analysis methods. Each normal spectrum curve group is respectively compared with the basic spectrum curve group for conformity, and based on the comparison results, the most matching normal spectrum curve group with the highest degree of conformity is determined. The spectrum section corresponding to the difference factor is marked in the basic spectrum curve group. Based on the time sequence relationship of the spectrum sections marked with difference factors in different basic spectrum curve groups, the abnormal position block where the abnormality occurs on the device is determined, and the spectrum sections marked as abnormal in the basic spectrum curve group are analyzed to determine the abnormal conditions of the abnormal position block. Through the above technical solutions, the present invention realizes the effect of using sound waves to locate the abnormal position block of the device and analyzes the abnormal characteristics of the abnormal position block, which is beneficial to making corresponding maintenance measures for the device in a timely manner.

[0113] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An acoustic wave analysis method for locating equipment faults, characterized in that: include: Based on the set position of the acoustic wave collection device relative to the equipment, a sound wave spatial collection array is constructed, the sound wave spatial collection array includes sound wave collection nodes at different spatial position nodes, and the sound wave data collected by the sound wave collection device are recorded separately in a manner of associating the sound wave collection nodes to obtain a plurality of sound wave data sets; The acoustic wave data set is analyzed by using a time-frequency analysis technique to obtain a number of basic spectrum curves, and a combination of basic spectrum curves belonging to the same acoustic wave data set is identified as a basic spectrum curve group; Several normal spectrum curve groups are preset for the normal operation state of the equipment, and each normal spectrum curve group is compared with the basic spectrum curve group for consistency, and based on the comparison results, the most consistent normal spectrum curve group with the highest degree of consistency is determined; Compare the most consistent normal spectrum curve group with the basic spectrum curve group to determine a number of difference factors. If the difference factor is within the preset normal range, it is determined that the device is normal. If the difference factor is not within the preset normal range, it is determined that the device is abnormal, and the spectrum segment corresponding to the difference factor is marked in the basic spectrum curve group; Based on the time sequence of the appearance of the spectrum segments marked with difference factors in different basic spectrum curve groups, determine the abnormal position block where the abnormality occurs on the device, and use the preset abnormal spectrum analysis model to analyze the spectrum segments marked with abnormalities on the basic spectrum curve group to determine the abnormal situation of the abnormal position block; The method of comparing the consistency of each normal spectrum curve group with the basic spectrum curve group includes: Each normal spectrum curve in the normal spectrum curve group and each basic spectrum curve in the basic spectrum curve group are grouped and compared accordingly, and the basic spectrum curve is deformed within a preset range, and the normal spectrum curve and the basic spectrum curve in each spectrum curve comparison group are relatively dynamically translated on the time axis, and the normal spectrum curve and the basic spectrum curve in each spectrum curve comparison group are translated in the same manner, and after each dynamic translation, the overlap parameters of the normal spectrum curve and the basic spectrum curve are calculated in real time, and the translation mode corresponding to the highest overlap parameter is identified as the overlap translation mode; Based on the overlap translation method, the normal spectrum curve and the basic spectrum curve are translated on the time axis, and based on the consistency characteristics between the translated normal spectrum curve and the basic spectrum curve, the sub-conformity degree of the corresponding spectrum curve comparison group is determined, and based on the sub-conformity degree of all spectrum curve comparison groups, the conformity degree of the normal spectrum curve group and the basic spectrum curve group is determined; The method for calculating the coincidence parameter of the normal spectrum curve and the basic spectrum curve includes: Shift the time axis to the center axis of the normal spectrum curve and the basic spectrum curve; According to a preset time interval, a number of time mapping nodes are set on the time axis, and for each time mapping node, the corresponding normal spectrum curve value and the basic spectrum curve value are intercepted, and the normal spectrum curve value and the basic spectrum curve value belonging to the same time mapping node are recorded as a spectrum curve value group; Analyze each spectrum curve value group, if the normal spectrum curve value and the basic spectrum curve value are both positive or negative, then identify the smaller curve value between the normal spectrum curve value and the basic spectrum curve value as a sub-coincidence parameter, and the sub-coincidence parameter takes an absolute value; All sub-coincidence parameters are summed to obtain the coincidence parameters of the normal spectrum curve and the basic spectrum curve.

2. The acoustic wave analysis method for locating equipment faults according to claim 1, characterized in that: Methods for determining the degree of conformity between the normal spectrum curve group and the basic spectrum curve group include: Based on the equipment components preset corresponding to the normal spectrum curves in the spectrum curve comparison group, determining the matching weight coefficient of the spectrum curve comparison group, and combining the sub-matching degree of the spectrum curve comparison group, calculating the matching degree of the normal spectrum curve group and the basic spectrum curve group; The method for determining the sub-matching degree of the spectrum curve comparison group includes calculating the peak difference between the normal spectrum curve and the basic spectrum curve, and determining the number of spectrum line crossings between the normal spectrum curve and the basic spectrum curve within a preset time segment, and determining the sub-matching degree of the spectrum curve comparison group based on the peak difference and the number of spectrum line crossings; The expression for calculating the degree of conformity is: ； in, is the degree of correspondence between the spectrum curve comparison groups, is the corresponding weight coefficient of the i-th equipment component, that is, the corresponding weight coefficient of the i-th spectrum curve comparison group, is the sub-matching degree corresponding to the i-th spectrum curve comparison group; The expression for calculating the sub-matching degree is: ； in, is the first conformity adjustment coefficient, is the second conformity adjustment coefficient, is the peak difference amount affecting the judgment function, is the peak difference, are several preset peak difference intervals, According to the peak difference The peak difference interval , output a specific first degree of conformity parameter, is the influence judgment function of the number of spectrum line crossings, u is the number of spectrum line crossings, is a preset number of spectral line crossing intervals, According to the spectrum line crossing number u to which the spectrum line crossing number interval Output a specific second conformity parameter, b is a conformity adjustment constant.

3. The acoustic wave analysis method for locating equipment faults according to claim 1, characterized in that: Methods for determining several differentiating factors include: The normal spectrum curves in the most consistent normal spectrum curve group and the basic spectrum curves in the basic spectrum curve group are matched one by one, and each pair of corresponding normal spectrum curves and basic spectrum curves is recorded as a spectrum curve comparison group; Deforming a basic spectrum curve in the basic spectrum curve in the spectrum curve comparison group, wherein the deformation method is to adjust the period and peak value of the basic spectrum curve within a preset range to obtain a deformed spectrum curve; Align the normal spectrum curve and the deformed spectrum curve, and construct a vertical scan line, gradually move the vertical scan line, and intercept the intersection of the vertical scan line and the normal spectrum curve and the deformed spectrum curve, record them as the normal spectrum curve value and the deformed spectrum curve value, and record the combination of the normal spectrum curve value and the deformed spectrum curve value as the analysis curve value group; According to the relationship before and after the vertical scanning line is scanned, the curve value group for analysis is sorted, and the curve value difference between the normal spectrum curve value and the deformed spectrum curve value in the analysis curve value group is calculated. Based on the continuous performance characteristics of the curve value difference, the difference factor parameter of the basic spectrum curve relative to the normal spectrum curve is determined. If the difference factor parameter is greater than or equal to the preset value, the difference factor is marked on the segment corresponding to the basic spectrum curve.

4. The acoustic wave analysis method for locating equipment faults according to claim 1, characterized in that: The method for determining the abnormal position block where the abnormality occurs on the device based on the time sequence relationship of the frequency spectrum segments marked with the difference factors in different basic frequency spectrum curve groups includes: A spatial coordinate system is established for the device, and based on the structural characteristics of the device, a corresponding virtual three-dimensional device structure is set in the spatial coordinate system, and the sound wave collection array is configured in the spatial coordinate system; Analyze the basic spectrum curve group corresponding to each device acquisition node respectively, determine the time node of each difference factor, and sort the difference factors according to the time relationship to obtain the difference factor sequence, and mark the time node of the difference factor; Compare the difference factor sequences corresponding to different equipment collection nodes, and calculate the occurrence time difference of the same difference factor between different equipment collection nodes; A number of component blocks are set inside the virtual three-dimensional device structure, and the sound wave conduction distance between each component block and different device acquisition nodes is gradually measured, and the difference between the sound wave conduction distances is calculated. If the difference between the sound wave conduction distances corresponding to a certain component block is consistent with the difference in occurrence time of the same difference factor, the component block is identified as an abnormal position block, and the difference factor is associated with the abnormal position block.

5. The acoustic wave analysis method for locating equipment faults according to claim 1, characterized in that: Methods for constructing an abnormal spectrum analysis model include: Establishing equipment abnormality test, including artificially setting abnormal faults for the corresponding components of the equipment based on the abnormal feature table set for each component, and using the acoustic wave collection device to collect abnormal acoustic wave data, and recording them separately according to the corresponding acoustic wave collection nodes to obtain several abnormal acoustic wave data sets; Analyze the abnormal sound wave data set to obtain several abnormal spectrum curves, intercept the spectrum segments on the abnormal spectrum curves, and associate the abnormal spectrum segments with abnormal features to obtain an abnormal feature-spectrum segment relationship group; The abnormal feature-spectrum segment relationship groups are analyzed to determine the feature label of each abnormal feature-spectrum segment relationship group.

6. The acoustic wave analysis method for locating equipment faults according to claim 5, characterized in that: The feature labels of the abnormal feature-spectrum segment relationship group include: the component to which the abnormal feature belongs, the average spectrum curve value of the spectrum segment, the upper and lower spectrum curve peaks of the spectrum segment, and the average spectrum curve period of the spectrum segment.

7. The acoustic wave analysis method for locating equipment faults according to claim 5, characterized in that: The method for classifying the spectrum segments marked with abnormalities on the basic spectrum curve group includes: Based on the determined abnormal position block, the abnormal feature-spectrum segment relationship group is screened for the first time, and based on the average spectrum curve value, upper and lower spectrum curve peaks and average spectrum curve period of the spectrum segment marked with abnormality on the basic spectrum curve group, the abnormal feature-spectrum segment relationship group is screened for the second time, and the screened abnormal feature-spectrum segment relationship group is identified as the reference abnormal feature-spectrum segment relationship group; The abnormal features in the reference abnormal feature-spectrum segment relationship group are identified as abnormal situations of the abnormal position block.

8. An acoustic wave analysis system for locating equipment faults, characterized in that: The method for performing the acoustic wave analysis for equipment fault location according to any one of claims 1 to 7 comprises: The first module is used to construct an acoustic wave spatial acquisition array based on the set position of the acoustic wave acquisition device relative to the equipment, the acoustic wave spatial acquisition array includes acoustic wave acquisition nodes at different spatial position nodes, and the acoustic wave data collected by the acoustic wave acquisition device are recorded separately in a manner of associating the acoustic wave acquisition nodes to obtain a plurality of acoustic wave data sets; The second module is used to analyze the sound wave data set by using the time-frequency analysis technology to obtain a number of basic spectrum curves, and identify the combination of basic spectrum curves belonging to the same sound wave data set as a basic spectrum curve group; The third module is used to preset a number of normal spectrum curve groups for the normal operation state of the equipment, compare each normal spectrum curve group with the basic spectrum curve group for consistency, and determine the most consistent normal spectrum curve group with the highest degree of consistency based on the comparison results; The fourth module is used to compare the most consistent normal spectrum curve group with the basic spectrum curve group to determine a number of difference factors. If the difference factor is within the preset normal range, it is determined that the device is normal. If the difference factor is not within the preset normal range, it is determined that the device is abnormal, and the spectrum segment corresponding to the difference factor is marked in the basic spectrum curve group; The fifth module is used to determine the abnormal position block where the abnormality occurs on the equipment based on the time sequence of the appearance of the spectrum segments marked with difference factors in different basic spectrum curve groups, and use the preset abnormal spectrum analysis model to analyze the spectrum segments marked with abnormalities on the basic spectrum curve group to determine the abnormal situation of the abnormal position block.

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