Motor fault incipient stage whole-cycle real-time prevention and control system and method based on stable isotope tracing and multi-modal sensing

Abstract

The application provides a motor fault germination period full-cycle real-time prevention and control system and method based on stable isotope tracing and multi-modal sensing, relates to the technical field of motor state monitoring and fault diagnosis, and comprises an interlocking protection unit, the interlocking protection unit comprises a vacuum circuit breaker and an inert gas arc extinguishing device, and the interlocking protection unit performs full-cycle real-time prevention and control on the motor fault. The stable isotope tracing and multi-modal sensing motor fault germination period full-cycle real-time prevention and control system and method successfully realizes full-cycle real-time prevention and control of the motor fault germination period by being based on stable isotope tracing and multi-modal sensing technology, combining a convolutional neural network and an edge computing processing unit, and performing feature extraction and multi-source fusion on multidimensional data in the motor operation process. The method can realize real-time monitoring of the motor state, early identification of potential faults, and thus improve the reliability and safety of the motor.

Description

Technical Field

[0001] This invention relates to the field of motor condition monitoring and fault diagnosis technology, specifically to a real-time prevention and control system and method for the entire lifecycle of motor fault initiation based on stability isotope tracing and multimodal sensing. Background Technology

[0002] Current engineering practices primarily rely on online detection, with vibration analysis, current characteristic analysis, and temperature monitoring forming the basic framework. Partial discharge and insulation resistance are used for insulation assessment. Multi-sensor fusion and edge computing are becoming increasingly common, forming predictive maintenance platforms. For penetrating detection of conductors and iron cores, TOF-SIMS enables nanoscale elemental imaging and works in conjunction with high-frequency CT triggering, compressing the total latency to approximately 27ms and establishing a relatively complete online diagnosis and treatment process.

[0003] The most critical flaw lies in the lack of verifiable, quantifiable, and localizable online identification and early intervention capabilities for the "budding" stage of faults. The breaking of insulating molecular bonds and the trace release of metal ions at this stage are at the micrometer and submicrometer scale, and macroscopic vibration, current, and temperature signals are difficult to form an in-situ traceability evidence chain. In particular, micro-short circuits and dendrite formation between iron chips require ppb-level isotope tracing to confirm the formation of conductive channels. Furthermore, the arc development time is much shorter than that of traditional responses, with many schemes still in the hundreds of milliseconds range, making it difficult to lock the total energy within the safety threshold for timely intervention. An integrated scheme combining atomic-molecular level evidence and millisecond-level interlocking is urgently needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a real-time prevention and control system and method for the entire lifecycle of motor fault initiation based on stable isotope tracing and multimodal sensing. The technical problem this invention aims to solve is: how to combine isotope tracing and multimodal sensing technologies to address the issue that traditional motor fault detection methods cannot provide early warning and real-time prevention and control during the fault initiation stage.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a real-time, full-cycle prevention and control system for motor fault initiation based on stability isotope tracing and multimodal sensing, comprising: An isotope tracing unit is disposed on the surface of the motor coil conductor. 65 Cu isotope layer, the isotope tracer unit is formed on the surface of the iron core. 54 Fe2O3 nano-coating, the isotope tracing unit generates characteristic signals during motor operation; The multimodal sensing unit includes a Raman spectroscopy probe, a μ-XRF micro-area fluorescence probe, a TOF-SIMS nano-imaging module, and a high-frequency current transformer. The multimodal sensing unit acquires multidimensional data of the motor. The data fusion and analysis unit includes a convolutional neural network and an edge computing processor. The data fusion and analysis unit performs feature extraction and multi-source fusion on the multi-dimensional data to generate a motor fault risk level. A prevention and control execution unit, which blocks the formation of a conductive channel based on the motor fault risk level; The interlocking protection unit includes a vacuum circuit breaker and an inert gas arc extinguishing device, and the interlocking protection unit provides real-time prevention and control of motor faults throughout the entire cycle.

[0006] Preferably, the 65 The Cu isotope layer has a thickness of 10 μm ± 1 μm and an abundance > 90%. 54 The Fe2O3 nano-coating has a thickness of 20nm-1000nm on the outer surface of the iron core. 54 The Fe2O3 nano-coating has a thickness of 0.1 μm-1 μm on the lamination surface of the sheet. 54 The Fe2O3 nano-coating has a thickness of 50nm-500nm on the tank wall. 54 The Fe2O3 nano-coating is not applied to the magnetic permeability main body region of the iron core lamination. The characteristic signal characterizes the micro-damage state of the motor coil and the iron core region, realizing atomic-level damage tracing.

[0007] Preferably, the TOF-SIMS nanoimaging module has a spatial resolution of 0.1 μm and a mass resolution of >10000. The high-frequency current transformer collects the high-frequency current signal of the motor winding to identify the local metal ion migration trajectory between iron chips.

[0008] Preferably, the data fusion and analysis unit extracts features from the multi-dimensional data using the convolutional neural network to generate feature data. The feature data is then fused and processed by the edge computing processor to output the motor fault risk level. The motor fault risk level includes the insulation degradation stage, the interferometric micro-short circuit stage, and the dendrite critical stage.

[0009] Preferably, the total response delay of the interlocking protection unit is ≤27ms, the breaking time of the vacuum circuit breaker is ≤2ms, the arc extinguishing speed of the inert gas arc extinguishing device is 100 times that of air, and the inert gas arc extinguishing device is adapted to high-speed tripping operation during the fault initiation stage.

[0010] A real-time, full-cycle prevention and control method for motor faults during their incipient stage, based on stability isotope tracing and multimodal sensing, includes: S1. Using TOF-SIMS to analyze the motor coil conductors and core area 65 Cu + Ions and54 Fe + The distribution of ions is calculated, a fault index is output, and spatial positioning information is generated based on the fault index. S2. The strength of the CF bond in the insulation layer is monitored to generate the CF bond decay rate. The strength monitoring uses Raman spectroscopy and a μ-XRF probe. The fault index and the CF bond decay rate are jointly judged to output the insulation degradation judgment result. When the insulation degradation judgment result is in the degradation stage, an intervention trigger command is generated. S3. Monitor the electrical signals during motor operation, and trigger circuit breaker protection and tiered tripping when the electrical signals are abnormal.

[0011] Preferably, the 65 The Cu isotope layer was prepared by an electroplating process, wherein the plating solution in the electroplating process is composed of the... 65 Cu + Ions are generated through a chlorination reaction. The parameters of the electroplating process include a current density of 2A / dm², a plating solution temperature of 25℃±2℃, and a stirring rate of 200rpm to ensure uniform electroplating layer thickness.

[0012] Preferably, the joint determination includes the following steps: S21. Compare the CF bond decay rate with a preset threshold to obtain a preliminary degradation judgment; S22. Combine the preliminary degradation assessment with the fault index to obtain the comprehensive degradation level; S23. When the overall degradation level reaches the set value, the insulation degradation judgment result is generated.

[0013] Preferably, the insulation degradation determination result includes calculating an aging index, which is calculated based on the ratio of the intensity of the Raman spectrum to the fluorescence intensity of the μ-XRF probe. The formula for the aging index is: .

[0014] in, The intensity of the Raman spectral characteristic peak is expressed in a.u. The fluorescence intensity of the μ-XRF probe is expressed in cps. AI is the aging index, which is dimensionless. When AI < 0.75, the insulation degradation is determined to be in the degradation stage.

[0015] Preferably, the circuit breaker protection includes a three-stage blocking sequence. The three-stage blocking sequence is as follows: in the first stage, a high-frequency CT detects an abnormal current with a response time of no more than 5ms; in the second stage, a trip signal is issued and the circuit breaker is driven to operate with a response time of no more than 20ms; and in the third stage, the main circuit is disconnected, with a total time of no more than 27ms.

[0016] This invention provides a real-time, full-cycle prevention and control system and method for motor faults in their incipient stage based on stable isotope tracing and multimodal sensing. It has the following beneficial effects: This system and method for real-time prevention and control of motor faults during their incipient stage, based on stable isotope tracing and multimodal sensing technologies, combined with convolutional neural networks and edge computing processing units, successfully achieves real-time prevention and control of motor faults during their incipient stage by extracting features and fusing multiple sources from multi-dimensional data during motor operation. This method enables real-time monitoring of motor status, early identification of potential faults, and thus improves motor reliability and safety.

[0017] use 65 Cu isotope layers and 54 Multimodal sensors, including Fe2O3 nanocoatings, Raman spectroscopy probes, μ-XRF micro-area fluorescence probes, and TOF-SIMS nanoimaging modules, are used to accurately monitor micro-damage, insulation degradation, and metal ion migration in the motor coils and core areas. The comprehensive application of these technologies effectively improves the accuracy of early fault diagnosis and significantly reduces the risk of motor failure through precise control mechanisms, thereby ensuring the safe and stable operation of the motor system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram showing the correspondence between failure modes and detection methods in implementing an invention; Figure 2 This is a schematic diagram of a hierarchical system structure for implementing an invention; Figure 3 This is a diagram illustrating the relationship between the risk level of an invention and the corresponding prevention and control measures. Figure 4 It is a flowchart for the entire lifecycle of fault prevention and control in an invention; Figure 5 This is a schematic diagram illustrating the cloud-edge collaborative data flow implementation of the invention; Figure 6 This is a schematic diagram of the interlocking protection response for implementing an invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 like Figure 1-6 As shown, this embodiment of the invention provides a real-time, full-cycle prevention and control system for motor fault initiation based on stable isotope tracing and multimodal sensing, including an isotope tracing unit, wherein the isotope tracing unit is disposed on the surface of the motor coil conductor. 65 Cu isotope layer, isotope tracer units are formed on the surface of the iron core. 54 Fe2O3 nanocoating, isotope tracer unit generates characteristic signals during motor operation. 65 The Cu isotope layer has a thickness of 10 μm ± 1 μm and an abundance of >90%. 54 The Fe2O3 nano-coating has a thickness of 20nm-1000nm on the outer surface of the iron core. 54 The Fe2O3 nano-coating has a thickness of 0.1 μm-1 μm on the lamination surface of the sheet. 54 The Fe2O3 nano-coating has a thickness of 50nm-500nm on the tank wall. 54 The Fe2O3 nano-coating is not applied to the magnetic permeability core region of the iron core lamination. The characteristic signal characterizes the micro-damage state of the motor coil and iron core region, enabling atomic-level damage tracing.

[0021] Isotope tracing technology enables high-precision tracking of micro-damage in motor coils and core areas. It can capture atomic-level damage signals early in the motor's operation, avoiding the lag inherent in traditional methods. By utilizing the specific signals of isotopes, early warnings can be issued during the nascent stage of motor faults, providing a new perspective for fault prediction and maintenance.

[0022] The multimodal sensing unit comprises a Raman spectroscopy probe, a μ-XRF micro-area fluorescence probe, a TOF-SIMS nanoimaging module, and a high-frequency current transformer. This unit acquires multidimensional data from the motor. The TOF-SIMS nanoimaging module has a spatial resolution of 0.1 μm and a mass resolution >10000. The high-frequency current transformer acquires high-frequency current signals from the motor windings to identify localized metal ion migration trajectories between iron chips.

[0023] By combining multiple advanced sensing technologies, the system can acquire data from multiple dimensions for comprehensive monitoring. It not only provides rich fault information but also enables data complementarity, thereby significantly improving the accuracy and reliability of fault detection.

[0024] The data fusion and analysis unit, comprising a convolutional neural network and an edge computing processor, performs feature extraction and multi-source fusion on multi-dimensional data to generate a motor fault risk level. Specifically, the unit extracts features from multi-dimensional data using a convolutional neural network to generate feature data. This feature data is then fused and processed by the edge computing processor to output the motor fault risk level, which includes the insulation degradation stage, the interferometric micro-short circuit stage, and the dendrite critical stage.

[0025] The data fusion unit uses convolutional neural networks for feature extraction and analysis, while the edge computing processor performs real-time calculations locally, avoiding problems caused by data transmission latency. This combination not only accelerates fault identification but also ensures the system can operate independently and efficiently without relying on the cloud.

[0026] The prevention and control execution unit blocks the formation of conductive channels based on the motor failure risk level.

[0027] The interlocking protection unit includes a vacuum circuit breaker and an inert gas arc extinguishing device. It provides real-time, full-cycle protection against motor faults. The total response delay of the interlocking protection unit is ≤27ms, the breaking time of the vacuum circuit breaker is ≤2ms, and the arc extinguishing speed of the inert gas arc extinguishing device is 100 times that of air, making it suitable for high-speed tripping operations during the fault initiation stage.

[0028] The interlocking protection unit is designed to ensure an extremely fast response in the event of a motor fault, with a response time of no more than 27ms, which is crucial for protecting the motor from major damage. Through the synergistic action of the vacuum circuit breaker and the inert gas arc extinguishing device, the system can quickly interrupt the current and reduce secondary damage caused by electrical faults.

[0029] A real-time, full-cycle prevention and control method for motor faults during their incipient stage, based on stability isotope tracing and multimodal sensing, includes: S1. Using TOF-SIMS to analyze the motor coil conductors and core area 65 Cu + Ions and 54 Fe + The distribution of ions is calculated, a fault index is output, and spatial positioning information is generated based on the fault index. 65 The Cu isotope layer was prepared using an electroplating process, in which the plating solution consisted of... 65 Cu+ Ions are generated through a chlorination reaction. The electroplating process parameters include a current density of 2A / dm², a plating solution temperature of 25℃±2℃, and a stirring rate of 200rpm to ensure uniform plating layer thickness.

[0030] S2. Insulation layer CF bond strength is monitored to generate CF bond attenuation rate. Insulation monitoring uses Raman spectroscopy and a μ-XRF probe. The fault index and CF bond attenuation rate are jointly used for determination, outputting insulation degradation judgment results. When the insulation degradation judgment result indicates a degradation stage, an intervention trigger command is generated. The joint determination includes the following steps: S21. Compare the CF bond decay rate with the preset threshold to obtain a preliminary assessment of degradation.

[0031] S22. The preliminary deterioration assessment and the failure index are combined to calculate the comprehensive deterioration level.

[0032] S23. When the overall degradation level reaches the set value, an insulation degradation judgment result is generated.

[0033] The insulation degradation assessment results include the calculation of the aging index, which is calculated based on the ratio of the Raman spectrum intensity to the fluorescence intensity of the μ-XRF probe. The formula for the aging index is: .

[0034] in, This represents the intensity of the characteristic peak in the Raman spectrum, in a. u. The fluorescence intensity of the μ-XRF probe is expressed in cps. AI is the aging index, which is dimensionless. When AI < 0.75, the insulation degradation is judged to be in the degradation stage.

[0035] S3. Monitor electrical signals during motor operation. Trigger circuit breaker protection and tiered tripping when an electrical signal is abnormal. The circuit breaker protection includes a three-stage blocking sequence: the first stage involves a high-frequency CT detecting an abnormal current with a response time of no more than 5ms; the second stage sends a trip signal and drives the circuit breaker to operate with a response time of no more than 20ms; and the third stage completes the main circuit disconnection, with a total time of no more than 27ms.

[0036] By combining multiple parameters such as fault index, CF bond attenuation rate, and aging index for comprehensive judgment, the system can accurately determine various fault states such as motor insulation deterioration and micro-short circuits, and issue intervention commands in a timely manner, effectively improving the accuracy and efficiency of motor maintenance.

[0037] Example 2 This embodiment presents a real-time, full-cycle prevention and control system and method for motor faults during their incipient stage, based on stability isotope tracing and multimodal sensing. It utilizes an isotope tracing unit to monitor micro-damage in the motor coil and core regions in real time, enabling early warning and prevention of motor faults. The specific implementation method is as follows: 1. 65 Preparation of Cu isotope layer Function: 65 A Cu isotope layer is coated on the surface of the motor coil conductor, primarily for monitoring micro-damage during motor operation.

[0038] Isotope abundance: 65 The Cu isotope abundance must be greater than 90% to ensure its stability and reproducibility.

[0039] Layer thickness: 65 The thickness requirement for the Cu isotope layer is 10 μm ± 1 μm.

[0040] Electroplating process: Current density: 2A / dm².

[0041] Plating solution temperature: 25℃±2℃.

[0042] Stirring speed: 200 rpm.

[0043] Electroplating solution formulation: The components of the electroplating solution 65 Cu + The ion concentration is 0.1 mol / L, and it is generated through a chlorination reaction.

[0044] Plating solution preparation: 65 Cu + The ions are mixed with the chloride solution to prepare the electroplating solution, ensuring that the pH value of the solution is between 6.5 and 7.5 to guarantee the stability of the reaction.

[0045] Electroplating operation: The operation is carried out in an electroplating tank. The current density is set to 2A / dm², the temperature of the plating solution is controlled at 25℃±2℃, and the stirring speed is 200rpm to ensure uniform distribution of the electroplating solution and avoid uneven concentration in some areas.

[0046] Coating thickness inspection: After electroplating is completed, the coating thickness is inspected by electron microscopy to ensure that it meets the requirement of 10μm±1μm.

[0047] 2. 54 Application of Fe2O3 nanocoating Function: 54 Fe2O3 nano-coatings are applied to the surface of motor cores to characterize micro-damage in the core region and to interact with... 65 Cu isotope layer-based collaborative monitoring of motor faults.

[0048] Solution preparation: 54 Fe2O3 powder is mixed with a solvent to form a homogeneous solution.

[0049] Coating process: A spray coating process is used to coat the product. 54 A Fe2O3 nano-coating was uniformly applied to the surface of the motor core. The spraying pressure was set to 0.3 MPa to ensure uniform coating.

[0050] Drying and curing: Dry the coated iron core in an oven at 80°C for 2 hours to ensure the coating is fixed.

[0051] 3. Generation and monitoring of characteristic signals Function: The isotope tracing unit passes through 65 Cu isotope layers and 54 The Fe2O3 nanocoating generates characteristic signals to monitor micro-damage in the motor coil and core areas, thereby enabling early warning of motor faults.

[0052] Data collection and analysis: Raman spectral probe: used to monitor the decay of CF bond strength in motor insulation. The CF bond decay rate is related to the aging index, which AI calculates by the ratio of the characteristic peak intensity of the Raman spectrum to the fluorescence intensity of the μ-XRF probe.

[0053] Formula for calculating aging index: .

[0054] in, This represents the intensity of the characteristic peak in the Raman spectrum, in a. u. The fluorescence intensity of the μ-XRF probe is expressed in cps. AI is the aging index, which is dimensionless. When AI < 0.75, the insulation degradation is judged to be in the degradation stage.

[0055] μ-XRF micro-area fluorescence probe: When used in conjunction with a Raman spectral probe, it further improves the accuracy of fault detection.

[0056] TOF-SIMS nanoimaging: used for high-resolution imaging of motor coils and core areas, with a resolution of 0.1 μm and a mass resolution greater than 10,000, enabling precise localization of micro-damage.

[0057] Signal acquisition: During motor operation, signal data of the motor coil and core region are collected using devices such as Raman spectroscopy, μ-XRF probe and TOF-SIMS.

[0058] Data processing and analysis: The collected signal data is input into the data processing system for feature extraction and fault assessment to determine the motor's operating status and degree of damage.

[0059] 4. Performance Verification and Debugging Verification process: 65 Cu isotope layer and 54 The Fe2O3 nanocoating underwent long-term stability testing to verify its reliability in the motor operating environment.

[0060] In a 1000-hour motor operation test, the performance changes of the isotope layer and nano-coating were tested, including the effects of environmental factors such as temperature and humidity.

[0061] Comparative experiments were conducted to ensure that the coating can continuously generate stable signals in actual working environments.

[0062] Debugging process: Adjust the signal acquisition frequency and threshold to ensure that the isotope tracer unit can detect the micro-damage of the motor in a timely manner and provide early warning before the fault occurs.

[0063] Through the above steps, the micro-damage status of the motor coils and core area can be accurately monitored during motor operation. Optimized electroplating processes and coating preparation techniques ensure the stability and reliability of the isotope layer and nano-coating, thereby enabling early diagnosis and risk assessment of motor faults. This system effectively improves the accuracy and response speed of motor fault detection, providing reliable technical support for motor fault prevention, helping to extend motor lifespan and reduce maintenance costs.

[0064] Example 3 This embodiment presents a real-time, full-cycle prevention and control system and method for motor faults during their incipient stage, based on stable isotope tracing and multimodal sensing. By utilizing stable isotope tracing and multimodal sensing technologies, it monitors the incipient stage of motor faults in real time. Through data fusion and analysis, it identifies potential motor fault risks in advance, achieving real-time prevention and control throughout the entire fault cycle. The specific implementation method is as follows: 1. Data Acquisition Phase Isotope tracing: In the motor coil conductor and iron core region, TOF-SIMS technology is used to... 65 Cu + and 54 Fe + The distribution of ions is calculated to generate a fault index and spatial location information. Specific parameters are as follows: Electroplating process parameters: Current density: 2A / dm².

[0065] Plating solution temperature: 25℃±2℃.

[0066] Stirring speed: 200 rpm.

[0067] Coating thickness: 10μm±1μm.

[0068] Isotope abundance: >90%.

[0069] Data Example: 65 Cu + Ion concentration: 1000 counts / μm².

[0070] 54 Fe + Ion concentration: 800 counts / μm².

[0071] Raman spectroscopy and μ-XRF probe: Raman spectroscopy is used to monitor the strength decay of CF bonds in the insulation layer and calculate the decay rate to assess the aging degree of the insulation material.

[0072] The μ-XRF probe is used to measure the elemental content on the surface of motor components, such as the distribution of metallic elements like iron and copper.

[0073] Data Example: CF bond decay rate: 30%.

[0074] XRF spectral intensity: 1200 cps.

[0075] Raman spectral intensity: 500 a.u.

[0076] TOF-SIMS and high-frequency current transformers: TOF-SIMS technology is used to analyze the ion distribution in the conductors and core regions of motor coils.

[0077] Data Example: 65 Cu + The ion concentration on the surface of the motor coil conductor is 1000 counts / μm².

[0078] High-frequency current transformers are used to detect abnormal current signals during motor operation.

[0079] Data example: The current signal frequency is 50kHz, and the peak current is 5A.

[0080] 2. Data Processing and Feature Extraction Convolutional Neural Network Feature Extraction: Input data from Raman spectroscopy, μ-XRF, TOF-SIMS, and high-frequency current transformers are used for feature extraction via a convolutional neural network. The specific steps are as follows: Feature extraction example: The CF bond attenuation rate extracted from Raman spectral data was 30%, indicating that the insulating material has begun to age.

[0081] Metal element concentrations, for example, iron concentration of 800 cps, are extracted from μ-XRF probe data.

[0082] Extracting from TOF-SIMS data 65 Cu + The ion concentration is 1000 counts / μm².

[0083] The frequency of the current signal extracted from the current transformer is 50kHz.

[0084] Data fusion: Feature data from different sensors are fused to generate a comprehensive degradation level for the motor. The fusion process is as follows: CF bond decay rate and 65 Cu + The overall degradation level is obtained by combining ion concentration calculations.

[0085] The abnormal current signal, combined with the migration of metal elements, further determines whether the motor has a micro-short circuit or dendrite phenomenon.

[0086] Risk level generation: The motor's failure risk level is calculated based on comprehensive data, including the following stages: Insulation degradation stage: The insulation degradation assessment results include the calculation of the aging index, which is calculated based on the ratio of the Raman spectrum intensity to the fluorescence intensity of the μ-XRF probe. The formula for the aging index is: .

[0087] in, This represents the intensity of the characteristic peak in the Raman spectrum, in a. u. The fluorescence intensity of the μ-XRF probe is expressed in cps. AI is the aging index, which is dimensionless. When AI < 0.75, the insulation degradation is judged to be in the degradation stage.

[0088] Assumption: The Raman spectral intensity is 450 a.u.

[0089] The XRF spectral intensity was 600 cps.

[0090] The calculation yields:

[0091] If AI < 0.75, it is determined to be in the insulation degradation stage.

[0092] Micro-short circuit stage between iron chips: When the frequency of the current signal exceeds 50kHz and the overall degradation level is greater than 0.85, it is judged to be in the micro-short circuit stage.

[0093] Dendrite critical stage: When the current signal is abnormal and the concentration of metal ions increases sharply, the motor enters the dendrite critical stage.

[0094] 3. Motor Failure Risk Assessment and Response Once the motor failure risk level is determined, the system will automatically activate the following response mechanism: Automatic circuit breaker protection: High-frequency CT detection: When the current signal is abnormal, the current change is first detected by high-frequency CT, and the response time is no more than 5ms.

[0095] Circuit breaker action: In the second stage, when the current signal exceeds the threshold, a trip signal is triggered, and the response time does not exceed 20ms.

[0096] Main circuit disconnection: In the third stage, the main circuit is disconnected, and the total time does not exceed 27ms.

[0097] Real-time prevention and control measures: The system will take real-time preventative measures during the early stages of motor failure, including: Block the current path.

[0098] Automatically triggers the circuit breaker.

[0099] It automatically records motor fault data, facilitating subsequent analysis and maintenance.

[0100] In summary, the system can accurately identify potential fault risks during motor operation. It comprehensively utilizes multi-source data, including Raman spectroscopy, μ-XRF probes, TOF-SIMS imaging modules, and high-frequency current signals, and processes and fuses the data through convolutional neural networks and edge computing to assess the motor fault risk level in real time. Based on the fault risk level, the system can make early detections of fault stages such as insulation degradation, micro-short circuits between iron ferrite cores, and dendrite criticality, and take corresponding preventative measures such as automatic circuit breaker protection and tripping responses. This system significantly improves the safety and stability of the motor and effectively reduces the potential risks caused by motor faults.

[0101] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time, full-cycle prevention and control system for motor fault initiation based on stable isotope tracing and multimodal sensing, characterized in that, include: An isotope tracing unit is disposed on the surface of the motor coil conductor. Isotope layer, wherein the isotope tracing unit is formed on the surface of the iron core. The nano-coating, wherein the isotope tracing unit generates characteristic signals during motor operation; A multimodal sensing unit includes a Raman spectral probe, a μ-XRF micro-area fluorescence probe, a TOF-SIMS nano-imaging module, and a high-frequency current transformer. The multimodal sensing unit acquires multidimensional data of the motor. The Raman spectral probe is used to monitor the attenuation of CF bond strength in the motor insulation layer. The μ-XRF micro-area fluorescence probe is used in conjunction with the Raman spectral probe to improve fault detection accuracy. The TOF-SIMS nano-imaging module is used for high-resolution imaging of the motor coil conductors and core region. The high-frequency current transformer is used to acquire high-frequency current signals from the motor windings. The data fusion and analysis unit includes a convolutional neural network and an edge computing processor. The data fusion and analysis unit performs feature extraction and multi-source fusion on the multi-dimensional data to generate a motor fault risk level. A prevention and control execution unit, which blocks the formation of a conductive channel based on the motor fault risk level; The interlocking protection unit includes a vacuum circuit breaker and an inert gas arc extinguishing device, and the interlocking protection unit provides real-time prevention and control of motor faults throughout the entire cycle.

2. The real-time prevention and control system for the entire lifecycle of motor fault initiation stage based on stable isotope tracing and multimodal sensing according to claim 1, characterized in that: The The isotope layer has a thickness of 10 μm ± 1 μm and an abundance of >90%. The nano-coating has a thickness of 20nm-1000nm on the outer surface of the iron core. The nano-coating has a thickness of 0.1 μm-1 μm on the lamination surface of the sheet. The nano-coating has a thickness of 50nm-500nm on the tank wall. The nano-coating is not applied to the magnetic permeability core area of ​​the iron core lamination.

3. The real-time prevention and control system for the entire lifecycle of motor fault initiation stage based on stable isotope tracing and multimodal sensing according to claim 1, characterized in that: The TOF-SIMS nano-imaging module has a spatial resolution of 0.1 μm and a mass resolution of >10000.

4. The real-time prevention and control system for the entire lifecycle of motor fault initiation stage based on stable isotope tracing and multimodal sensing according to claim 1, characterized in that: The data fusion and analysis unit extracts features from the multi-dimensional data using the convolutional neural network to generate feature data. The feature data is then fused and processed by the edge computing processor to output the motor fault risk level. The motor fault risk level includes the insulation degradation stage, the micro-short circuit stage between iron chips, and the dendrite critical stage.

5. The real-time prevention and control system for the entire lifecycle of motor fault initiation stage based on stable isotope tracing and multimodal sensing according to claim 1, characterized in that: The total response delay of the interlocking protection unit is ≤27ms, the breaking time of the vacuum circuit breaker is ≤2ms, the arc extinguishing speed of the inert gas arc extinguishing device is 100 times that of air, and the inert gas arc extinguishing device is adapted to high-speed tripping operation during the fault initiation stage.

6. A method for real-time prevention and control of motor faults throughout their entire lifecycle, based on stable isotope tracing and multimodal sensing, characterized in that... The method, applied to the real-time, full-cycle prevention and control system for motor fault initiation based on stable isotope tracing and multimodal sensing as described in any one of claims 1 to 5, comprises: S1. Using TOF-SIMS to analyze the motor coil conductors and core area Ions and The distribution of ions is calculated, a fault index is output, and spatial positioning information is generated based on the fault index. S2. The strength of the CF bonds in the insulation layer is monitored to generate the CF bond attenuation rate. The strength monitoring is performed using a Raman spectral probe and a μ-XRF micro-area fluorescence probe. The fault index and the CF bond attenuation rate are jointly determined to output the insulation degradation determination result. S3. Monitor the electrical signals during motor operation, and trigger the vacuum circuit breaker protection and tiered tripping when the electrical signals are abnormal.

7. The method for real-time prevention and control of motor faults in their incipient stage based on stable isotope tracing and multimodal sensing according to claim 6, characterized in that: The The isotopic layer is prepared by an electroplating process, wherein the plating solution in the electroplating process is composed of the... Ions are generated via a chlorination reaction. The parameters of the electroplating process include a current density of... The temperature of the plating solution is 25℃±2℃, and the stirring rate is 200rpm.

8. The method for real-time prevention and control of motor faults in their incipient stage based on stable isotope tracing and multimodal sensing according to claim 6, characterized in that: The joint determination includes the following steps: S21. Compare the CF bond decay rate with a preset threshold to obtain a preliminary degradation judgment; S22. Combine the preliminary degradation assessment with the fault index to obtain the comprehensive degradation level; S23. When the overall degradation level reaches the set value, the insulation degradation judgment result is generated.

9. The method for real-time prevention and control of motor faults in their incipient stage based on stable isotope tracing and multimodal sensing according to claim 6, characterized in that: The insulation degradation determination result includes an aging index, which is calculated based on the ratio of the spectral intensity of the Raman spectral probe to the fluorescence intensity of the μ-XRF micro-area fluorescence probe. The formula for the aging index is: in, This indicates the spectral intensity of the Raman spectral probe. The fluorescence intensity represents the μ-XRF micro-area fluorescence probe, and AI is the aging index. When AI < 0.75, the insulation degradation result is determined to be in the insulation degradation stage.

10. The method for real-time prevention and control of motor faults in their incipient stage based on stable isotope tracing and multimodal sensing according to claim 6, characterized in that: The vacuum circuit breaker protection includes a three-stage blocking sequence. The three-stage blocking sequence is as follows: in the first stage, a high-frequency current transformer detects an abnormal current; in the second stage, a trip signal is issued and the circuit breaker is driven to operate; and in the third stage, the main circuit is disconnected.

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