Motor partial discharge map analysis and early warning system supporting WEB remote access

Abstract

The invention relates to a motor partial discharge map analysis and early warning system supporting WEB remote access, and belongs to the technical field of power systems and automation thereof. According to the system, a fusion acquisition unit acquires motor operation multi-source signals and extracts core features to form standardized data; an edge analysis unit pre-analyzes a partial discharge map, marks abnormal feature points, locally caches data and then orderly uploads the data to a cloud according to a network state, and automatic breakpoint resuming is carried out when the network is interrupted; the cloud analysis unit reconstructs a partial discharge full atlas, matches and judges a fault type, constructs a multi-dimensional comprehensive evaluation model to quantitatively judge an early warning level, and generates a targeted disposal suggestion; and the WEB interaction unit builds a distributed WEB access architecture, bidirectionally synchronizes edge end and WEB end parameters in real time, and backtracks historical data to generate a fault tracing track when triggering early warning. Accurate monitoring of partial discharge of the motor, edge cloud collaborative analysis, WEB remote intelligent early warning and fault tracing are realized, and stable operation of a power system is effectively guaranteed.

Description

Technical Field

[0001] This invention belongs to the field of power system and automation technology, specifically relating to a motor partial discharge spectrum analysis and early warning system that supports remote WEB access. Background Technology

[0002] High-voltage motors are core power equipment in industrial sectors such as rail transportation, petroleum and petrochemicals, steel and mining, and high-end manufacturing. Their stable operation is directly related to the continuity and safety of industrial production. Deterioration of the stator winding insulation is a major cause of equipment failure, and partial discharge is the core trigger for insulation degradation. Therefore, real-time online monitoring, spectral analysis, and early warning of partial discharge in motors are crucial means to ensure reliable operation of motor equipment and reduce the risk of industrial production downtime.

[0003] With the development of industrial intelligence and remote operation and maintenance technology, industrial sites have put forward the demand for WEB remote access to motor partial discharge monitoring systems. It is hoped that the entire process of operation, such as graph viewing, fault analysis, and parameter configuration, can be realized through the remote terminal, which is suitable for group-based and cross-regional equipment operation and maintenance management models.

[0004] Currently, while existing motor partial discharge monitoring systems possess basic signal acquisition, graph display, and early warning functions, they still suffer from numerous technical deficiencies in multi-source signal processing, edge data processing, cloud-based intelligent analysis, and WEB remote interactive adaptation, making it difficult to meet the actual application needs of industrial sites. At the multi-source signal processing level, existing systems lack precise time-frequency domain joint calibration for multi-source signals such as partial discharge, temperature vibration, and mechanical vibration during motor operation. They cannot effectively use temperature vibration parameters as interference removal factors to filter out interference components in partial discharge signals, resulting in low accuracy in multi-source signal fusion and deviations in the extracted core features of partial discharge, creating potential errors for subsequent graph analysis and fault diagnosis. At the edge processing level, existing systems' edge acquisition units mostly only handle data acquisition and raw data forwarding, lacking localized partial discharge graph pre-analysis, abnormal feature point marking, and data caching capabilities. Data loss is prone to occur during network interruptions, and direct transmission of large amounts of raw data to the cloud puts pressure on network bandwidth, significantly reducing overall analysis efficiency.

[0005] At the cloud-based analysis and early warning level, existing systems rely heavily on simple feature matching between partial discharge maps and standard maps for fault determination. They lack a multi-dimensional comprehensive evaluation model and determine warning levels solely based on a single partial discharge numerical threshold, ignoring key influencing factors such as map evolution characteristics and equipment operating status. This results in low accuracy in fault type determination, a lack of scientific rigor in warning level classification, and rather general and unrealistic handling suggestions for different warning levels. At the web remote access level, existing systems' web access architectures are mostly designed for single-terminal adaptation, lacking pre-defined multi-terminal adaptive access logic. This leads to issues such as mismatched display specifications and incompatible operation logic when various terminals initiate access. Furthermore, the systems lack professional interactive map analysis and simultaneous screen comparison functions, resulting in delays in parameter synchronization between the edge and web ends. Even after triggering an early warning, they cannot quickly trace back historical data to generate a complete fault tracing trajectory, significantly reducing the convenience and practicality of remote operation and maintenance.

[0006] In summary, existing motor partial discharge monitoring systems suffer from problems such as low signal processing accuracy, weak edge processing capabilities, lack of scientific rigor in cloud-based analysis and early warning, and poor adaptability and interactivity for remote web access. These issues make it difficult to meet the high demands for remote monitoring, accurate analysis, and scientific early warning of motor partial discharge under the development of industrial intelligence. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides a motor partial discharge spectrum analysis and early warning system that supports remote web access. The objective of this invention can be achieved through the following technical solutions: include: The fusion acquisition unit acquires multi-source signals of motor operation and performs joint time-frequency domain calibration on various signals; temperature and vibration parameters are used as interference removal factors to filter out partial discharge signal interference, and multi-source signals are time-domain aligned and feature fused; core features of partial discharge are extracted and preprocessed to form standardized feature data; The edge parsing unit pre-parses the partial discharge spectrum and marks abnormal feature points based on the standardized feature data; it caches the pre-parsed spectrum data and partial discharge feature parameters locally, and uploads them to the cloud in an orderly manner according to the real-time network status, and automatically resumes the transmission if the network is interrupted. The cloud-based analysis unit receives pre-analyzed data to reconstruct a full partial discharge spectrum, performs feature matching with the built-in standard spectrum, and determines the fault type; it constructs a multi-dimensional comprehensive evaluation model, quantitatively analyzes and comprehensively determines the early warning level, and generates targeted fault handling suggestions for different levels. The WEB interaction unit builds a distributed WEB access architecture with preset multi-terminal adaptive access; through graph interactive analysis and on-screen comparison, it synchronizes parameters between the edge end and the WEB end in real time; when an alert is triggered, it automatically backtracks historical data to generate a fault tracing trajectory.

[0008] Specifically, the process of performing time-frequency domain joint calibration on various signals is as follows: The time-domain and frequency-domain characteristics of the multi-source signals of motor operation are obtained, and a unified time-frequency calibration benchmark for the multi-source signals is determined. Time-domain phase calibration and frequency-domain band matching are performed separately for different types of signals; During the calibration process, the synchronization of signals is continuously verified, and the time-frequency deviations of various signals are corrected in real time, so that the multi-source signals maintain characteristic synchronization in the same time-frequency dimension.

[0009] Specifically, the process of using temperature and vibration parameters as interference removal factors to filter out partial discharge signal interference is as follows: The real-time characteristic values ​​of temperature and vibration parameters are obtained and a characteristic matrix is ​​constructed. The characteristic matrix is ​​then incorporated into the signal filtering logic as an interference removal factor. The partial discharge signal containing interference is introduced into the signal filtering logic system, and the characteristic intervals of temperature and vibration interference components in the signal are identified by factor matching. Interference components in the characteristic region are separated and eliminated, while the effective partial discharge signal characteristics without interference are fully preserved.

[0010] Specifically, the process of forming standardized feature data after preprocessing is as follows: The core features of partial discharge are subjected to global noise filtering to remove invalid noise feature points; the filtered effective features are normalized to eliminate dimensional differences between different features. A primary and secondary arrangement logic for the core features of partial discharge is preset, and the features are then integrated in an orderly manner according to the primary and secondary arrangement logic. The integrated feature data is transformed into a structured form with unified specifications, ultimately forming standardized feature data.

[0011] Specifically, the process of pre-analyzing the partial discharge spectrum and marking abnormal feature points is as follows: A pre-analysis logic for the partial discharge map is built based on standardized feature data. The feature data is then imported into the logic to generate the corresponding partial discharge map. The judgment range of the pre-defined map feature points is used to check all feature points of the map by traversing them point by point. Identify abnormal feature points that deviate from the judgment range, and mark each abnormal point with feature labels.

[0012] Specifically, the process of locally caching the pre-analyzed spectral data and partial discharge characteristic parameters is as follows: In the local storage area, a corresponding independent cache space is allocated to classify and organize the pre-analyzed spectral data and partial discharge characteristic parameters. Establish corresponding relationships based on data generation time and parameter attributes, set a retrieval identifier for each set of related data, and store the retrieval identifier, corresponding data, and parameters in the cache space in an orderly manner.

[0013] Specifically, the process of performing feature matching and determining the fault type is as follows: The core features and feature distribution patterns of the full partial discharge spectrum after pre-reconstruction are defined. Retrieve the standard features and distribution features corresponding to various faults from the built-in standard map library, and perform a layer-by-layer and dimension-by-dimensional comparison and analysis between the actual map features and the standard map features; Calculate the feature similarity matching degree, and determine the specific fault type based on the fault interval corresponding to the feature similarity matching degree.

[0014] Specifically, the multi-dimensional comprehensive evaluation model includes a model input layer, a weight allocation layer, a quantization calculation layer, and a level mapping layer, and the specific construction process is as follows: The core evaluation dimensions are partial discharge values, spectrum evolution characteristics, and equipment operating status. The input consists of multi-dimensional monitoring parameters, and the total output is a comprehensive evaluation score and a matching warning level. The model input layer receives partial discharge characteristic parameters and equipment operating status parameters. The weight allocation layer assigns appropriate weights to each dimension according to operation and maintenance requirements. The quantization calculation layer normalizes and quantizes the parameters and completes the comprehensive score. The level mapping layer establishes the correspondence between the score and the warning level.

[0015] Specifically, the process of generating targeted fault handling suggestions for different levels is as follows: Based on the severity and development trend of partial discharge faults corresponding to each warning level, and combined with the key points of handling various types of faults and motor operation and maintenance specifications, fault handling principles and countermeasures are matched for different warning levels. Pre-determine the specific implementation steps, operational requirements, and precautions for each response measure; Based on the actual operability of on-site operation and maintenance, specific fault handling suggestions are formulated for each warning level.

[0016] Specifically, the process of the preset multi-terminal adaptive access is as follows: Obtain the display specifications, operation logic, and adaptation characteristics of various access terminals; Build a basic framework for web access that is compatible with various terminals, and pre-set the logic for automatic terminal identification and intelligent parameter matching in the framework; When a terminal initiates an access request, the system automatically identifies the terminal type and quickly matches the corresponding display specifications, operation mode, and data display format.

[0017] Specifically, the process of real-time bidirectional synchronization of parameters between the edge terminal and the web terminal is as follows: Establish a parameter synchronization transmission channel between the edge terminal and the web terminal, and pre-set fixed logic for real-time parameter collection, detection and updating; When parameters at the edge are modified, the updated parameters are immediately pushed to the web end and updated synchronously. When a parameter setting command is issued from the web terminal, the command is quickly synchronized to the edge terminal and the parameter adjustment is executed, achieving real-time bidirectional synchronization of parameters between the two terminals.

[0018] Specifically, the process of generating the fault source tracing trajectory is as follows: After an early warning is triggered, all historical monitoring data and partial discharge spectrum information of the monitoring nodes are quickly retrieved. The dynamic changes of partial discharge characteristics were analyzed in chronological order, and the core characteristic data and fault development nodes of each key time node were extracted. By connecting and integrating the nodes in sequence according to time, and combining the correspondence between historical data and map features, a visualized partial discharge fault tracing trajectory is formed.

[0019] The beneficial effects of this invention are as follows: (1) By setting up a fusion acquisition unit and an edge analysis unit, the multi-source signal features are synchronized by relying on joint calibration in the time and frequency domains. The interference filtering logic is constructed with temperature and vibration parameters to accurately remove the interference of partial discharge signals. After noise filtering and normalization, standardized feature data is formed, which greatly improves the accuracy of partial discharge core feature extraction and reduces the error of subsequent spectrum analysis and fault judgment from the source. At the same time, the edge analysis unit completes partial discharge spectrum pre-analysis and anomaly marking based on standardized feature data. Data is stored locally in an orderly manner through classification caching and retrieval label setting. Combined with the breakpoint resume mechanism, the continuity of data transmission is guaranteed. This avoids data loss caused by network interruption and reduces the bandwidth pressure of direct transmission of original data to the cloud, thereby improving the overall data analysis efficiency and data management stability of the system. (2) By setting up a cloud analysis unit and a WEB interaction unit, the cloud analysis unit achieves accurate fault type determination through layer-by-layer and dimension-by-dimensional feature matching. Relying on a multi-dimensional comprehensive evaluation model with a four-layer architecture including an input layer and a weight allocation layer, it scientifically determines the warning level after quantitative analysis. Combined with the severity of the fault and the operation and maintenance specifications, it generates targeted and practical handling suggestions, which solves the problems of traditional warnings being singular and handling suggestions being general. The distributed architecture built by the WEB interaction unit enables multi-terminal adaptive access. Through a dedicated transmission channel, it ensures real-time bidirectional synchronization of parameters between the edge end and the WEB end. Combined with the graph interactive analysis and screen comparison functions, it improves the efficiency of remote analysis. After triggering the warning, it can sort out the feature changes in time sequence and generate a visual fault tracing trajectory, making remote operation and maintenance more convenient and fault tracing more intuitive. It provides complete data and functional support for cross-regional and group-based motor equipment operation and maintenance. Attached Figure Description

[0020] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0021] Figure 1 This is a system architecture diagram of a motor partial discharge spectrum analysis and early warning system that supports remote WEB access according to the present invention; Figure 2 This is a data flow diagram of a motor partial discharge spectrum analysis and early warning system that supports remote WEB access according to the present invention; Figure 3 This is the partial discharge measurement settings interface in this invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0023] Please see Figure 1-3 A partial discharge spectrum analysis and early warning system for motors that supports remote web access; include: The fusion acquisition unit acquires multi-source signals of motor operation and performs joint time-frequency domain calibration on various signals; temperature and vibration parameters are used as interference removal factors to filter out partial discharge signal interference, and multi-source signals are time-domain aligned and feature fused; core features of partial discharge are extracted and preprocessed to form standardized feature data; The edge parsing unit pre-parses the partial discharge spectrum and marks abnormal feature points based on the standardized feature data; it caches the pre-parsed spectrum data and partial discharge feature parameters locally, and uploads them to the cloud in an orderly manner according to the real-time network status, and automatically resumes the transmission if the network is interrupted. The cloud-based analysis unit receives pre-analyzed data to reconstruct a full partial discharge spectrum, performs feature matching with the built-in standard spectrum, and determines the fault type; it constructs a multi-dimensional comprehensive evaluation model, quantitatively analyzes and comprehensively determines the early warning level, and generates targeted fault handling suggestions for different levels. The WEB interaction unit builds a distributed WEB access architecture with preset multi-terminal adaptive access; through graph interactive analysis and on-screen comparison, it synchronizes parameters between the edge end and the WEB end in real time; when an alert is triggered, it automatically backtracks historical data to generate a fault tracing trajectory.

[0024] In this embodiment, the multi-source signals for motor operation refer to various related electrical and physical signals collected by monitoring the motor's operating status. The core of these signals includes partial discharge signals, temperature and vibration fusion signals, and mechanical vibration signals. The temperature and vibration fusion signals cover temperature monitoring signals and vibration monitoring signals during motor operation. These various signals are collected from different monitoring points on the motor and together reflect the overall operating status of the motor, serving as the basic data source for subsequent signal processing and feature extraction.

[0025] In this embodiment, the core features of partial discharge are extracted from the partial discharge signal after filtering out interference and completing fusion. These are key features that can accurately reflect the partial discharge state and development trend of the motor. They are effective features obtained after full-domain noise filtering and normalization. Specifically, they include the numerical features and spectral evolution features of partial discharge, as well as phase features, amplitude features, and pulse features related to partial discharge. These features are the core basis for constructing standardized feature data and analyzing partial discharge spectra.

[0026] In this embodiment, the fault type is determined by matching the features of the reconstructed partial discharge full spectrum with the built-in standard spectrum layer by layer and dimension by dimension. This determines various insulation faults and related operational faults of the motor caused by partial discharge, providing a clear direction for targeted handling of motor operation and maintenance. Specifically, it includes motor stator winding insulation deterioration faults (such as insulation aging and insulation damage), insulation surface discharge faults, corona discharge faults, air gap discharge faults, etc. All types of faults are highly correlated with the characteristics and development patterns of partial discharge.

[0027] In this embodiment, the synchronization parameters between the edge terminal and the web terminal are the core configuration and operating parameters that support the system's edge resolution, cloud analysis, and remote interaction. They provide a unified standard for collaborative work and data exchange between the two ends, and support bidirectional real-time synchronous modification. Edge parameters: mainly include the map pre-analysis parameters of the edge parsing unit, the abnormal feature point judgment range parameters, the local data cache configuration parameters, the network transmission related parameters, and the basic configuration parameters for partial discharge signal acquisition, etc. Web-side parameters: These mainly include terminal adaptation parameters for web access, operation configuration parameters for interactive graph analysis and on-screen comparison, parameters related to warning level display, as well as remotely issued edge terminal operation command parameters and data display specification parameters.

[0028] Specifically, the process of performing time-frequency domain joint calibration on various signals is as follows: The time-domain and frequency-domain characteristics of the multi-source signals of motor operation are obtained, and a unified time-frequency calibration benchmark for the multi-source signals is determined. Time-domain phase calibration and frequency-domain band matching are performed separately for different types of signals; During the calibration process, the synchronization of signals is continuously verified, and the time-frequency deviations of various signals are corrected in real time, so that the multi-source signals maintain characteristic synchronization in the same time-frequency dimension.

[0029] Specifically, the process of using temperature and vibration parameters as interference removal factors to filter out partial discharge signal interference is as follows: The real-time characteristic values ​​of temperature and vibration parameters are obtained and a characteristic matrix is ​​constructed. The characteristic matrix is ​​then incorporated into the signal filtering logic as an interference removal factor. The partial discharge signal containing interference is introduced into the signal filtering logic system, and the characteristic intervals of temperature and vibration interference components in the signal are identified by factor matching. Interference components in the characteristic region are separated and eliminated, while the effective partial discharge signal characteristics without interference are fully preserved.

[0030] Specifically, the process of forming standardized feature data after preprocessing is as follows: The core features of partial discharge are subjected to global noise filtering to remove invalid noise feature points; the filtered effective features are normalized to eliminate dimensional differences between different features. A primary and secondary arrangement logic for the core features of partial discharge is preset, and the features are then integrated in an orderly manner according to the primary and secondary arrangement logic. The integrated feature data is transformed into a structured form with unified specifications, ultimately forming standardized feature data.

[0031] Specifically, the process of pre-analyzing the partial discharge spectrum and marking abnormal feature points is as follows: A pre-analysis logic for the partial discharge map is built based on standardized feature data. The feature data is then imported into the logic to generate the corresponding partial discharge map. The judgment range of the pre-defined map feature points is used to check all feature points of the map by traversing them point by point. Identify abnormal feature points that deviate from the judgment range, and mark each abnormal point with feature labels.

[0032] Specifically, the process of locally caching the pre-analyzed spectral data and partial discharge characteristic parameters is as follows: In the local storage area, a corresponding independent cache space is allocated to classify and organize the pre-analyzed spectral data and partial discharge characteristic parameters. Establish corresponding relationships based on data generation time and parameter attributes, set a retrieval identifier for each set of related data, and store the retrieval identifier, corresponding data, and parameters in the cache space in an orderly manner.

[0033] Specifically, the process of performing feature matching and determining the fault type is as follows: The core features and feature distribution patterns of the full partial discharge spectrum after pre-reconstruction are defined. Retrieve the standard features and distribution features corresponding to various faults from the built-in standard map library, and perform a layer-by-layer and dimension-by-dimensional comparison and analysis between the actual map features and the standard map features; Calculate the feature similarity matching degree, and determine the specific fault type based on the fault interval corresponding to the feature similarity matching degree.

[0034] Specifically, the multi-dimensional comprehensive evaluation model includes a model input layer, a weight allocation layer, a quantization calculation layer, and a level mapping layer, and the specific construction process is as follows: The core evaluation dimensions are partial discharge values, spectrum evolution characteristics, and equipment operating status. The input consists of multi-dimensional monitoring parameters, and the total output is a comprehensive evaluation score and a matching warning level. The model input layer receives partial discharge characteristic parameters and equipment operating status parameters. The weight allocation layer assigns appropriate weights to each dimension according to operation and maintenance requirements. The quantization calculation layer normalizes and quantizes the parameters and completes the comprehensive score. The level mapping layer establishes the correspondence between the score and the warning level.

[0035] Specifically, the process of generating targeted fault handling suggestions for different levels is as follows: Based on the severity and development trend of partial discharge faults corresponding to each warning level, and combined with the key points of handling various types of faults and motor operation and maintenance specifications, fault handling principles and countermeasures are matched for different warning levels. Pre-determine the specific implementation steps, operational requirements, and precautions for each response measure; Based on the actual operability of on-site operation and maintenance, specific fault handling suggestions are formulated for each warning level.

[0036] Specifically, the process of the preset multi-terminal adaptive access is as follows: Obtain the display specifications, operation logic, and adaptation characteristics of various access terminals; Build a basic framework for web access that is compatible with various terminals, and pre-set the logic for automatic terminal identification and intelligent parameter matching in the framework; When a terminal initiates an access request, the system automatically identifies the terminal type and quickly matches the corresponding display specifications, operation mode, and data display format.

[0037] Specifically, the process of real-time bidirectional synchronization of parameters between the edge terminal and the web terminal is as follows: Establish a parameter synchronization transmission channel between the edge terminal and the web terminal, and pre-set fixed logic for real-time parameter collection, detection and updating; When parameters at the edge are modified, the updated parameters are immediately pushed to the web end and updated synchronously. When a parameter setting command is issued from the web terminal, the command is quickly synchronized to the edge terminal and the parameter adjustment is executed, achieving real-time bidirectional synchronization of parameters between the two terminals.

[0038] Specifically, the process of generating the fault source tracing trajectory is as follows: After an early warning is triggered, all historical monitoring data and partial discharge spectrum information of the monitoring nodes are quickly retrieved. The dynamic changes of partial discharge characteristics were analyzed in chronological order, and the core characteristic data and fault development nodes of each key time node were extracted. By connecting and integrating the nodes in sequence according to time, and combining the correspondence between historical data and map features, a visualized partial discharge fault tracing trajectory is formed.

[0039] In this embodiment, the specific implementation of time-frequency domain joint calibration for various signals is as follows: First, the time-domain and frequency-domain characteristics of the multi-source signals of motor operation are obtained through coupling capacitor sensors, high-frequency partial discharge sensors, temperature measuring elements, and vibration sensors. The detection frequency band of the partial discharge signal covers 1MHz~20MHz and 0.3MHz~30MHz, and the detection frequency band of the vibration signal is 4~4000Hz. 50Hz is determined as the unified time-frequency calibration benchmark for the multi-source signals. Time-domain phase calibration and frequency-domain band matching are carried out for different types of signals. High-frequency discharge pulse signals are synchronously acquired through three channels with a sampling rate of 100MS / s and a resolution of 14 bits. The same trigger signal is used to trigger each acquisition end simultaneously, and built-in fiber optic time base synchronization or current sensing synchronization is adopted. During the calibration process, the signal synchronization is continuously checked, and the time-frequency deviation of various signals is corrected in real time, keeping the synchronization time difference within 100ns, so that the multi-source signals maintain characteristic synchronization in the same time-frequency dimension.

[0040] In this embodiment, the specific implementation of pre-analyzing partial discharge spectra and marking abnormal feature points is as follows: A spectra pre-analysis logic is built based on standardized feature data. The feature data is imported into the logic to generate corresponding partial discharge spectra such as PRPD spectra, PRPS spectra, and phase line spectra. A preset judgment range for spectra feature points is established, including attention thresholds, alarm thresholds, and counting thresholds. The attention thresholds and alarm thresholds can be set as needed within the range of 0~3000pC, and the counting threshold can be adjusted within the range of 0~160. A 50-period effective sampling period is used as the baseline verification period, and all feature points of the spectra are verified by traversing the points one by one. Abnormal feature points deviating from the above judgment range are identified, and each abnormal point is marked with a differentiated feature. Signals below the attention threshold are displayed as green dots, signals above the attention threshold are represented by dots of different shades from light yellow to dark brown, and signals above the alarm threshold are represented by red pulses. Simultaneously, a warning indication response is triggered, completing the marking of abnormal feature points.

[0041] In this embodiment, the specific implementation of the preset multi-terminal adaptive access is as follows: First, the display specifications, operation logic, and adaptation characteristics of various mainstream browsers such as Chrome, Sogou High-Speed, 360 Extreme Speed, QQ, and UC are analyzed to clarify that no dedicated client needs to be installed on any type of terminal; system access can be achieved solely through the browser. A web access framework based on TCP / IP communication and WEB technology is built. This framework supports simultaneous detection of up to 200 online monitoring terminals and has the characteristics of automatic terminal identification and flexible expansion. The framework presets automatic terminal identification and intelligent parameter matching logic. When various terminals initiate access requests, the system automatically identifies the browser type of the terminal and quickly matches the corresponding display specifications, operation mode, and data display format. Each terminal can simultaneously view core content such as real-time monitoring data, historical partial discharge maps, and early warning alarm information, adapting to the access needs of different terminals.

[0042] In this embodiment, the specific implementation of real-time bidirectional synchronization of parameters between the edge terminal and the web terminal is as follows: A parameter synchronization transmission channel between the edge terminal and the web terminal is established based on optical fiber and LAN / WAN. The maximum transmission distance between the two monitoring nodes can reach 20km, ensuring the stability of parameter transmission. Simultaneously, a fixed logic for real-time parameter acquisition, detection, and updating is preset, and the data acquisition frequency and transmission interval can be flexibly set as needed. When the edge terminal modifies parameters such as attention threshold, alarm threshold, filter frequency band, and phase offset, the updated parameters are immediately pushed to the web terminal and synchronized. When the web terminal issues parameter setting commands such as acquisition time interval, rotation interval, and synchronization method, the commands are quickly synchronized to the edge terminal and parameter adjustments are executed. It also supports WIFI wireless connection mode, achieving real-time bidirectional synchronization of parameters between the two ends in wireless scenarios.

[0043] In this embodiment, the specific implementation of generating the fault tracing trajectory is as follows: After triggering the early warning, all historical monitoring data and partial discharge spectrum information such as PRPD spectrum, PRPS spectrum, and phase line spectrum of the monitoring node are quickly retrieved. The system relies on a large-capacity database to stably store 10 years of monitoring data, providing complete data support for the tracing analysis; the dynamic changes of characteristics such as partial discharge peak value, discharge quantity, phase distribution, and discharge frequency are sorted out in chronological order, and the core feature data and fault development nodes of each key time node are accurately extracted, including key nodes such as the first time the partial discharge value exceeds the attention threshold, the continuous exceedance of the alarm threshold, and the appearance of obvious abnormalities in the discharge feature spectrum; the key nodes are connected and integrated in sequence according to time, and combined with the correspondence between historical monitoring data and partial discharge spectrum characteristics, a visualized partial discharge fault tracing trajectory including partial discharge trend line graph and on-screen comparison of spectrums at different time periods is generated. At the same time, the relevant tracing data and spectrum can be sorted and exported as a fault summary in PDF format to assist in the completion of fault tracing analysis and cause determination.

[0044] In this embodiment, the system is applied to the online monitoring and remote early warning of partial discharge of 6kV and above high-voltage motors in the oil and petrochemical industry's refining and chemical plant. This scenario has stringent requirements for the continuous and stable operation of the motors, and the equipment is distributed throughout the plant. It is necessary to realize cross-regional remote operation and maintenance and early warning of faults. The specific implementation process is as follows: At monitoring points such as the stator leads and neutral line of the high-voltage motor in the refinery area, coupling capacitor sensors, high-frequency partial discharge sensors, temperature measuring elements, and vibration sensors were deployed. Each sensor was connected to a fusion acquisition unit via coaxial cables. The unit synchronously acquired multi-source signals from the motor's partial discharge, temperature, and vibration through three channels at a sampling rate of 100 MS / s and a resolution of 14 bits. Subsequently, 50 Hz was determined as the unified time-frequency calibration benchmark. A built-in fiber optic time base synchronization method was used to perform time-domain phase calibration and frequency-domain band matching on various signals, continuously verifying signal synchronization and correcting deviations, keeping the synchronization time difference within 100 ns to complete the joint time-frequency domain calibration. Then, temperature and vibration parameters were used as interference removal factors. Based on time-domain and frequency-domain signal analysis techniques, filtering logic was constructed, selecting a 1-20 MHz suitable filtering band to identify and separate temperature and vibration interference components in the partial discharge signal, retaining the minimum detectable 5 PC. The effective partial discharge signal is used to complete the time-domain alignment and feature fusion of multi-source signals. Finally, the core features of partial discharge, such as discharge quantity, discharge peak value, and discharge phase, are extracted. After full-domain preprocessing such as high-order digital filtering, signal amplification, and analog-to-digital conversion, the differences in the dimensions between features are eliminated and integrated according to the main and secondary logic, and transformed into structured data of unified specifications to form standardized feature data. The edge analysis unit receives standardized feature data output from the fusion acquisition unit, builds a spectrum pre-analysis logic based on this data, and generates partial discharge spectra such as PRPD spectra, PRPS spectra, and phase line spectra after importing the feature data. Simultaneously, it presets the spectrum feature point judgment range, configures attention thresholds and alarm thresholds within the range of 0~3000pC, and configures a counting threshold within the range of 0~160 points, with a 50... Using a valid sampling period as the verification benchmark, all feature points of the spectrum are verified by traversing the spectrum point by point. Abnormal feature points that deviate from the judgment range are marked with different colors. Note that points above the threshold are displayed as light yellow to dark brown heat points, and points above the alarm threshold are displayed as red pulses. This completes the marking of abnormal feature points. Subsequently, an independent cache space is allocated in the local storage area of ​​the unit. The pre-analyzed spectrum data and partial discharge feature parameters such as discharge quantity and phase are classified and organized according to the generation time and parameter attributes. Association retrieval tags are established and stored in the cache space in an orderly manner. Finally, according to the real-time network status of the plant's local area network / wide area network, the cached spectrum data and feature parameters are uploaded to the cloud in an orderly manner. If a network interruption occurs, the breakpoint resume mechanism is automatically triggered to ensure the integrity of data transmission and avoid data loss. The cloud-based analysis unit receives pre-analyzed data uploaded by the edge parsing unit, first integrating and processing the data to reconstruct the full partial discharge spectrum; then, it retrieves the standard features and distribution patterns corresponding to various motor faults such as corona discharge, insulation degradation, surface discharge, and air gap discharge from the built-in standard spectrum library, and performs a layer-by-layer, dimension-by-dimensional comparison analysis between the reconstructed actual full spectrum features and the standard spectrum features to calculate the feature similarity matching degree between the two. Based on the fault interval corresponding to the matching degree, it accurately determines the specific fault type of the motor; subsequently, it constructs a system including an input layer, a weight allocation layer, a quantization calculation layer, and a level... The multi-dimensional comprehensive evaluation model of the mapping layer takes partial discharge values, spectrum evolution characteristics, and motor temperature and vibration operation status as core evaluation dimensions. The input layer receives various multi-dimensional monitoring parameters, the weight allocation layer assigns appropriate weights to each dimension according to the operation and maintenance needs of petroleum and petrochemical motors, the quantification calculation layer normalizes and quantifies the parameters and completes the comprehensive score, and the level mapping layer establishes the correspondence between the score and the three-level warning level of green, yellow, and red. Green indicates that the partial discharge value is below the attention threshold, yellow indicates that it exceeds the attention threshold and needs attention, and red indicates that it exceeds the alarm threshold and there is a serious risk of failure. Finally, combined with the motor operation and maintenance specification of "GB / T 20833.2-2016" and the actual production of the refining and chemical plant, targeted fault handling suggestions are generated for different warning levels. The yellow warning is matched with the response measures of on-site data verification and collaborative analysis of the spectrum, and the red warning is matched with the handling plan of on-site emergency investigation and shutdown maintenance when necessary. The web interaction unit first establishes a distributed web access architecture based on TCP / IP communication. This architecture supports simultaneous access from up to 200 online monitoring units. It analyzes the display specifications and operational characteristics of various mainstream browsers such as Chrome, 360 Extreme, and Sogou High-Speed ​​Browser, and pre-sets terminal automatic identification and intelligent parameter matching logic within the architecture to achieve multi-terminal adaptive access. Plant maintenance personnel do not need to install a dedicated client; they can access the system simply through a browser. Simultaneously, a fiber-optic-based parameter synchronization transmission channel between the edge and web ends is established to ensure transmission stability within a 20km range between monitoring nodes. Pre-set logic for real-time parameter acquisition, detection, and updating is implemented. When the edge parsing unit modifies parameters such as filter frequency band, phase offset, and threshold, the changes are immediately pushed to the web end for synchronous updates. When the web end issues commands such as acquisition interval, rotation interval, and synchronization method, the changes are quickly synchronized to the edge end and parameter adjustments are executed, achieving real-time bidirectional parameter synchronization between the two ends. Maintenance personnel can access the system via the web... The terminal can interactively analyze and compare the partial discharge spectrum of the motor on the same screen, and view the operating data, spectrum information and warning status of each monitoring node in real time. When the system triggers a yellow or red warning, the unit automatically retrieves the historical monitoring data and partial discharge spectrum information of the monitoring node for up to 10 years, sorts out the dynamic change process of the partial discharge characteristics in chronological order, extracts the key nodes of fault development such as the first time the partial discharge value exceeds the threshold and the discharge spectrum shows obvious abnormalities, and integrates each node in sequence. Combining the correspondence between historical data and spectrum characteristics, it generates a visualized fault tracing trajectory that includes a partial discharge trend line chart and multi-time period spectrum comparison on the same screen. At the same time, it can organize the tracing data and spectrum into a fault summary in PDF format to help maintenance personnel quickly locate the cause of the fault and formulate maintenance plan.

[0045] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A system for analyzing and warning of partial discharge patterns in motors that supports remote web access, characterized in that, include: The fusion acquisition unit acquires multi-source signals from motor operation and performs joint time-frequency domain calibration on various signals; Temperature and vibration parameters are used as interference removal factors to filter out partial discharge signal interference, and multi-source signal time-domain alignment and feature fusion are performed. Extract the core features of partial discharge, and then preprocess them to form standardized feature data; The edge parsing unit pre-parses the partial discharge spectrum and marks abnormal feature points based on the standardized feature data; it caches the pre-parsed spectrum data and partial discharge feature parameters locally, and uploads them to the cloud in an orderly manner according to the real-time network status, and automatically resumes the transmission if the network is interrupted. The cloud-based analysis unit receives pre-analyzed data to reconstruct a full partial discharge spectrum, performs feature matching with the built-in standard spectrum, and determines the fault type; it also constructs a multi-dimensional comprehensive evaluation model to comprehensively determine the warning level and generate targeted fault handling suggestions for different levels. The WEB interaction unit builds a distributed WEB access architecture with preset multi-terminal adaptive access; through graph interactive analysis and on-screen comparison, it synchronizes parameters between the edge end and the WEB end in real time; when an alert is triggered, it automatically backtracks historical data to generate a fault tracing trajectory.

2. The system according to claim 1, characterized in that, The specific process for performing time-frequency domain joint calibration on various signals is as follows: The time-domain and frequency-domain characteristics of the multi-source signals of motor operation are obtained, and a unified time-frequency calibration benchmark for the multi-source signals is determined. Time-domain phase calibration and frequency-domain band matching are performed separately for different types of signals; During the calibration process, the synchronization of signals is continuously verified, and the time-frequency deviations of various signals are corrected in real time, so that the multi-source signals maintain characteristic synchronization in the same time-frequency dimension.

3. The system according to claim 1, characterized in that, The specific process of using temperature and vibration parameters as interference removal factors to filter out partial discharge signal interference is as follows: The real-time characteristic values ​​of temperature and vibration parameters are obtained and a characteristic matrix is ​​constructed. The characteristic matrix is ​​then incorporated into the signal filtering logic as an interference removal factor. The partial discharge signal containing interference is introduced into the signal filtering logic system, and the characteristic intervals of temperature and vibration interference components in the signal are identified by factor matching. Interference components in the characteristic region are separated and eliminated, while the effective partial discharge signal characteristics without interference are fully preserved.

4. The system according to claim 1, characterized in that, The specific process for generating standardized feature data after preprocessing is as follows: The core features of partial discharge are subjected to global noise filtering to remove invalid noise feature points; the filtered effective features are normalized to eliminate dimensional differences between different features. A primary and secondary arrangement logic for the core features of partial discharge is preset, and the features are then integrated in an orderly manner according to the primary and secondary arrangement logic. The integrated feature data is transformed into a structured form with unified specifications, ultimately forming standardized feature data.

5. The system according to claim 1, characterized in that, The specific process of pre-analyzing the partial discharge spectrum and marking abnormal feature points is as follows: A pre-analysis logic for the partial discharge map is built based on standardized feature data. The feature data is then imported into the logic to generate the corresponding partial discharge map. The judgment range of the pre-defined map feature points is used to check all feature points of the map by traversing them point by point. Identify abnormal feature points that deviate from the judgment range, and mark each abnormal point with feature labels.

6. The system according to claim 1, characterized in that, The specific process of locally caching the pre-analyzed spectral data and partial discharge characteristic parameters is as follows: In the local storage area, a corresponding independent cache space is allocated to classify and organize the pre-analyzed spectral data and partial discharge characteristic parameters. Establish corresponding relationships based on data generation time and parameter attributes, set a retrieval identifier for each set of related data, and store the retrieval identifier, corresponding data, and parameters in the cache space in an orderly manner.

7. The system according to claim 1, characterized in that, The specific process of performing feature matching and determining the fault type is as follows: The core features and feature distribution patterns of the full partial discharge spectrum after pre-reconstruction are defined. Retrieve the standard features and distribution features corresponding to various faults from the built-in standard map library, and perform a layer-by-layer and dimension-by-dimensional comparison and analysis between the actual map features and the standard map features; Calculate the feature similarity matching degree, and determine the specific fault type based on the fault interval corresponding to the feature similarity matching degree.

8. The system according to claim 1, characterized in that, The multi-dimensional comprehensive evaluation model includes a model input layer, a weight allocation layer, a quantization calculation layer, and a level mapping layer. The specific construction process is as follows: The core evaluation dimensions are partial discharge values, spectrum evolution characteristics, and equipment operating status. The input consists of multi-dimensional monitoring parameters, and the total output is a comprehensive evaluation score and a matching warning level. The model input layer receives partial discharge characteristic parameters and equipment operating status parameters. The weight allocation layer assigns appropriate weights to each dimension according to operation and maintenance requirements. The quantization calculation layer normalizes and quantizes the parameters and completes the comprehensive score. The level mapping layer establishes the correspondence between the score and the warning level.

9. The system according to claim 1, characterized in that, The specific process for generating targeted fault handling suggestions for different levels is as follows: Based on the severity and development trend of partial discharge faults corresponding to each warning level, and combined with the key points of handling various types of faults and motor operation and maintenance specifications, fault handling principles and countermeasures are matched for different warning levels. Pre-determine the specific implementation steps, operational requirements, and precautions for each response measure; Based on the actual operability of on-site operation and maintenance, specific fault handling suggestions are formulated for each warning level.

10. The system according to claim 1, characterized in that, The specific process of the preset multi-terminal adaptive access is as follows: Obtain the display specifications, operation logic, and adaptation characteristics of various access terminals; Build a basic framework for web access that is compatible with various terminals, and pre-set the logic for automatic terminal identification and intelligent parameter matching in the framework; When a terminal initiates an access request, the system automatically identifies the terminal type and quickly matches the corresponding display specifications, operation mode, and data display format.

11. The system according to claim 1, characterized in that, The specific process of real-time bidirectional synchronization of parameters between the edge terminal and the web terminal is as follows: Establish a parameter synchronization transmission channel between the edge terminal and the web terminal, and pre-set fixed logic for real-time parameter collection, detection and updating; When parameters at the edge are modified, the updated parameters are pushed to the web end and updated synchronously. When a parameter setting command is issued from the web terminal, the command is quickly synchronized to the edge terminal and the parameter adjustment is executed, achieving real-time bidirectional synchronization of parameters between the two terminals.

12. The system according to claim 1, characterized in that, The specific process for generating the fault source tracing trajectory is as follows: After an early warning is triggered, all historical monitoring data and partial discharge spectrum information of the monitoring nodes are quickly retrieved. The dynamic changes of partial discharge characteristics were analyzed in chronological order, and the core characteristic data and fault development nodes of each key time node were extracted. By connecting and integrating the nodes in sequence according to time, and combining the correspondence between historical data and map features, a visualized partial discharge fault tracing trajectory is formed.

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