Motor manufacturing quality intelligent detection system

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a motor manufacturing quality intelligent detection system which comprises a sensor data acquisition module, a data processing module, an artificial intelligence analysis module, a decision feedback module and a report generation module. Aiming at electromagnetic interference and mechanical noise generated by multiple processes, a self-adaptive signal filtering mechanism is established, transient interference components of data collected by a sensor can be reduced in real time, the extraction integrity of key characteristic parameters such as vibration and temperature is ensured, the misjudgment risk caused by signal distortion is reduced, the problem that the omission ratio is too high in traditional detection is solved, and the detection accuracy is improved. And a dynamic association rule base is constructed, so that the system can capture early fault symptoms which cannot be recognized by a traditional single-dimensional model, prediction blind areas caused by multi-field data splitting are reduced, and the product life prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent detection system for motor manufacturing quality. Background Art

[0002] Artificial intelligence is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems.

[0003] The intelligent inspection system for motor manufacturing quality is a complex system that integrates sensing technology, artificial intelligence, and the Industrial Internet of Things. It aims to achieve intelligent quality monitoring of the entire motor production process. Its core lies in accurately identifying manufacturing defects through multi-source data fusion. Traditional inspection mainly relies on contact measuring instruments and manual sampling. Although high-precision sensors can collect key motor parameters, there are still defects in actual applications.

[0004] At present, since motor production involves the coordination of multiple processes such as winding embedding, rotor dynamic balancing, and assembly testing, sensor signals in real-time quality inspection are easily affected by electromagnetic interference and mechanical noise. When dynamic noise suppression is not performed, feature extraction will be distorted, resulting in an increase in the missed detection rate. At the same time, defect identification relies on preset threshold rules and cannot adapt to material batch differences and process parameter drift, and the positioning accuracy is limited to a two-dimensional plane. More importantly, motor quality requires simultaneous analysis of the thermal-mechanical-electrical multi-physical field coupling effects. Existing technologies use a single-dimensional detection model, and no dynamic association rules are established for multi-field data, resulting in the inability to predict hidden defects, seriously affecting product reliability and life.

[0005] Therefore, an intelligent detection system for motor manufacturing quality is proposed to solve the above problems. Summary of the Invention

[0006] (1) Technical problems solved In view of the shortcomings of the existing technology, the present invention provides an intelligent detection system for motor manufacturing quality, which solves the problems raised in the above background technology.

[0007] (2) Technical solution To achieve the above objectives, the present invention provides the following technical solutions: an intelligent detection system for motor manufacturing quality, comprising: Sensor data acquisition module: real-time collection of key parameters in the motor manufacturing process, including temperature, vibration, current, voltage and dimensional accuracy data; Data processing module: pre-processes the data collected by the sensor, including noise filtering, data normalization and feature extraction, to generate a standardized data set; Artificial Intelligence Analysis Module: This module uses deep learning models to build a virtual map of motor manufacturing quality based on standardized data sets, and identifies and classifies defects in motor components. Decision feedback module: Based on the defect probability and severity output by the artificial intelligence analysis module and the real-time working conditions of the production line, it generates quality adjustment instructions and feeds them back to the manufacturing execution system; Report generation module: automatically generates quality inspection reports, including defect location, type and improvement suggestions, and integrates with the enterprise resource planning system.

[0008] Preferably, the specific process of the sensor data acquisition module is: Multiple sensor nodes are set up at key workstations on the motor production line, including the winding assembly section, rotor balancing section, and final assembly and testing section; Real-time data transmission to cloud database via IoT protocol; The parameter acquisition frequency is set to 30 times per second to ensure high-precision synchronous sampling.

[0009] Preferably, the feature extraction process of the data processing module includes: Perform frequency domain analysis on vibration signals to extract main frequency harmonic characteristics; Perform time series segmentation on temperature data and calculate temperature rise gradient; The principal component analysis method is used to reduce the dimension and generate a multidimensional feature vector.

[0010] Preferably, the deep learning model construction method of the artificial intelligence analysis module is: Build a defect recognition model based on a convolutional neural network, with training data including historical manufacturing defect samples and simulation anomaly data; The model input is a standardized data set, and the output is the probability distribution of defect categories; The model is optimized through transfer learning, and the network weights are adjusted based on the motor type and material properties.

[0011] Preferably, the defect identification and classification process further includes: Set the defect threshold range. When the output probability is greater than 0.8, it is judged as a high-risk defect. Spatial positioning of identified defects to generate a three-dimensional heat map; By matching the cosine similarity with the historical defect library, the potential failure mode is predicted. The formula is: in, is the element of the current defect feature vector, is the element of the historical defect feature vector, is the feature vector dimension.

[0012] Preferably, the quality adjustment instruction generation process of the decision feedback module is: Obtain current production plan and equipment health status; The Pearson correlation coefficient is used to calculate the correlation between the health data group and the production plan data group. The formula is: in, is the observed value of the healthy data group, is the observation value of the production task data group, is the mean of the healthy data group, is the mean of the production task data group; When the correlation coefficient is lower than the preset threshold, real-time intervention instructions are triggered, including adjusting assembly parameters and pausing the workstation.

[0013] Preferably, a predictive maintenance submodule is also included: Build a virtual model of the motor manufacturing process based on digital twin technology; Analyze the evolution trend of quality data and predict future defect rates; Output maintenance recommendations, including preventative parts replacement and process optimization.

[0014] Preferably, the system integration method includes: Synchronize data with manufacturing execution systems in real time; Support edge computing deployment to reduce latency; Use blockchain technology to ensure that data cannot be tampered with.

[0015] Preferably, the system performance optimization process is: Dynamically adjust AI model parameters to adapt to different motor models; Optimize detection algorithm efficiency through reinforcement learning; Regularly update the defect library to improve the system's generalization capabilities.

[0016] Preferably, the output process of the report generation module is: Integrate test results and prediction data to generate structured reports; The report includes defect statistics, risk level and improvement timeline; Pushed to user terminals and cloud platforms through API interfaces.

[0017] (3) Beneficial effects Compared with the prior art, the present invention provides an intelligent detection system for motor manufacturing quality, which has the following beneficial effects: 1. In the present invention, by setting up a dynamic noise suppression module, an adaptive signal filtering mechanism is established for the electromagnetic interference and mechanical noise generated by multiple processes during the real-time detection of motor manufacturing quality. This can reduce the transient interference component of the sensor collected data in real time, ensure the extraction integrity of key characteristic parameters such as vibration and temperature, reduce the risk of misjudgment caused by signal distortion, solve the problem of high missed detection rate in traditional detection, and thus improve the reliability of defect identification.

[0018] 2. In the present invention, by setting up a multi-field coupling analysis engine, when performing hidden defect detection of motors, a dynamic association rule library is constructed based on the synchronous collection results of thermal-mechanical-electrical multi-physical field data, and the nonlinear coupling relationship between temperature rise gradient, current harmonics and mechanical vibration is analyzed in real time, so that the system can capture early fault signs that traditional single-dimensional models cannot identify, reduce prediction blind spots caused by multi-field data fragmentation, and improve the accuracy of product life prediction.

[0019] 3. In the present invention, by setting a three-dimensional spatial positioning algorithm, in the process of identifying motor assembly defects, the probability distribution output by the deep learning model is combined with the spatial coordinate mapping to generate a three-dimensional heat map of complex defects such as bearing deflection and rotor eccentricity, thereby achieving precise coordinate positioning in three-dimensional space. At the same time, the detection reference plane is automatically adjusted according to the real-time working conditions, solving the problem of assembly angle deviation caused by traditional two-dimensional positioning and ensuring the targeted accuracy of quality adjustment instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1 , the motor manufacturing quality intelligent detection system includes: Sensor data acquisition module: real-time collection of key parameters in the motor manufacturing process, including temperature, vibration, current, voltage and dimensional accuracy data; Data processing module: pre-processes the data collected by the sensor, including noise filtering, data normalization and feature extraction, to generate a standardized data set; Artificial Intelligence Analysis Module: This module uses deep learning models to build a virtual map of motor manufacturing quality based on standardized data sets, and identifies and classifies defects in motor components. Decision feedback module: Based on the defect probability and severity output by the artificial intelligence analysis module and the real-time working conditions of the production line, it generates quality adjustment instructions and feeds them back to the manufacturing execution system; Report generation module: automatically generates quality inspection reports, including defect location, type, and improvement suggestions, and integrates with the enterprise resource planning system; The specific process of the sensor data acquisition module is: Multiple sensor nodes are set up at key workstations on the motor production line, including the winding assembly section, rotor balancing section, and final assembly and testing section; Real-time data transmission to cloud database via IoT protocol; Set the parameter acquisition frequency to 30 times per second to ensure high-precision synchronous acquisition; The feature extraction process of the data processing module includes: Perform frequency domain analysis on vibration signals to extract main frequency harmonic characteristics; Perform time series segmentation on temperature data and calculate temperature rise gradient; Use principal component analysis to reduce dimensionality and generate multidimensional feature vectors; The deep learning model construction method of the artificial intelligence analysis module is: Build a defect recognition model based on a convolutional neural network, with training data including historical manufacturing defect samples and simulation anomaly data; The model input is a standardized data set, and the output is the probability distribution of defect categories; Optimize the model through transfer learning, adjusting the network weights based on motor type and material properties; The defect identification and classification process also includes: Set the defect threshold range. When the output probability is greater than 0.8, it is judged as a high-risk defect. Spatial positioning of identified defects to generate a three-dimensional heat map; By matching the cosine similarity with the historical defect library, the potential failure mode is predicted. The formula is: in, is the element of the current defect feature vector, is the element of the historical defect feature vector, is the feature vector dimension; The quality adjustment instruction generation process of the decision feedback module is as follows: Obtain current production plan and equipment health status; The Pearson correlation coefficient is used to calculate the correlation between the health data group and the production plan data group. The formula is: in, is the observed value of the healthy data group, is the observation value of the production task data group, is the mean of the healthy data group, is the mean of the production task data group; When the correlation coefficient is lower than the preset threshold, real-time intervention instructions are triggered, including adjusting assembly parameters and pausing the workstation; Also includes predictive maintenance submodules: Build a virtual model of the motor manufacturing process based on digital twin technology; Analyze the evolution trend of quality data and predict future defect rates; Output maintenance recommendations, including preventive parts replacement and process optimization; System integration methods include: Synchronize data with manufacturing execution systems in real time; Support edge computing deployment to reduce latency; Use blockchain technology to ensure that data cannot be tampered with; The system performance optimization process is: Dynamically adjust AI model parameters to adapt to different motor models; Optimize detection algorithm efficiency through reinforcement learning; Regularly update the defect library to improve the system's generalization capabilities; The output process of the report generation module is: Integrate test results and prediction data to generate structured reports; The report includes defect statistics, risk level and improvement timeline; Pushed to user terminals and cloud platforms through API interfaces.

[0023] Example 1: Specific implementation process of the motor manufacturing quality intelligent detection system During the startup phase of the motor production line, a multi-type sensor network is first deployed. High-precision current probes are installed at the winding embedding station, three-axis vibration sensors are configured in the rotor dynamic balancing section, and infrared thermal imagers are deployed in the assembly and testing area. When the motor enters the assembly process, the sensor data acquisition module synchronously captures the temperature gradient, electromagnetic vibration spectrum and current harmonic components at a frequency of 20 times per second, and uploads the original data stream to the edge computing node in real time through the industrial Internet of Things gateway. The data processing module uses wavelet packet decomposition technology to remove mechanical noise from the vibration signal, and performs sliding window mean filtering on the temperature data to reduce environmental disturbances, ultimately generating a standardized feature vector set.

[0024] The artificial intelligence analysis module calls a pre-trained convolutional neural network model, which completes transfer learning optimization based on 100,000 sets of historical defect samples. When the new permanent magnet motor enters the inspection station, the system inputs the rotor magnetic steel size deviation characteristics into the model, and the output layer generates a three-dimensional probability heat map to accurately locate the assembly offset defect of the 7th slot of the magnetic steel. At the same time, through the cosine similarity algorithm and matching with the historical defect library, the evolutionary correlation between this offset and early demagnetization faults is identified.

[0025] The decision-making feedback module is connected to the manufacturing execution system in real time. When it detects an abnormal stacking coefficient of a batch of silicon steel sheets, it automatically calculates the Pearson correlation coefficient between the current production plan and the quality risk. When the correlation coefficient is lower than the safety threshold, it immediately issues instructions: reduce the winding machine tension parameter by 15%, start the backup assembly line at station B, and automatically divert the batch of rotors to the re-inspection area. The entire process completes the decision-making closed loop within 200 milliseconds, avoiding unplanned shutdowns of the production line.

[0026] The predictive maintenance submodule builds a digital twin. When analysis reveals nonlinear deviations between the temperature rise curve and current harmonics of a certain motor model, it automatically deduces the quality evolution path. The system predicts that the insulation material has reached the critical aging point and generates a preventive maintenance plan: replace the stator slot wedge material during the night shift maintenance window, adjust the varnishing process parameters, and synchronize the optimization instructions to the enterprise resource planning system.

[0027] The report generation module integrates data from the entire process and produces a structured output of a test report containing the three-dimensional coordinates of defects, material degradation trends, and intervention effectiveness assessments. The report is then stored on the blockchain and pushed to the quality management terminal. The system dynamically updates the feature extraction weights every 24 hours through a reinforcement learning mechanism. When a new type of carbon fiber rotor material is detected, the defect identification dimension is automatically expanded to the composite material stratification direction, continuously improving the system's generalization capabilities.

[0028] Example 2: Implementation process on an asynchronous motor production line When the rotor casting station is started, the die-casting machine injects molten aluminum into the rotor mold. At this time, the temperature sensor array captures the thermal field distribution on the mold surface at a frequency of 15 frames per second. When a temperature difference mutation exceeding the process upper limit is detected in the lower right quadrant of mold No. 3, the data processing module immediately starts the adaptive filtering algorithm to remove the periodic thermal noise caused by the cooling water circulation and extract the abnormal heat dissipation hotspots caused by microcracks in the mold. The artificial intelligence analysis module calls the pre-trained residual neural network model, which is based on 5,000 sets of aluminum rotor defect samples, including casting defects such as pores and cold shuts, to complete transfer learning optimization. The output results show the probability of the current batch of rotors having 0.5mm-level micro-shrinkage cavities. The system automatically marks the batch as a high-risk product for inspection.

[0029] The decision-making feedback module synchronously obtains production scheduling information. When it is identified that this batch of rotors corresponds to an urgent export order, it immediately calculates the Pearson correlation coefficient between quality risk and delivery time. The critical state where the correlation coefficient is lower than the safety threshold of 0.3 triggers a two-level response mechanism: first, the low-pressure casting pressure parameters are adjusted to extend the holding time. At the same time, the X-ray non-destructive testing station is activated to perform a full tomography scan on the high-risk rotors. The detection data is transmitted back in real time through the edge computing node. The system completes the three-dimensional reconstruction of the defect and accurately locates the shrinkage cavity deep in the fifth groove of the rotor end ring.

[0030] Based on digital twin deduction, the predictive maintenance sub-module discovered that the current mold temperature difference is often strongly correlated with calcification deposition in the cooling water channel. The system predicts that continued production will lead to the risk of mold cracking, and thus generates a cross-process maintenance plan: high-pressure water jet cleaning is performed during the shift handover window, the pH value of the cooling water is adjusted, and the mold stress monitoring cycle is compressed. Maintenance instructions are directly connected to the factory equipment management system through the OPCUA protocol, and maintenance resources are automatically reserved for the night shift.

[0031] During the final assembly and testing phase, the system detected abnormal harmonic components in the no-load current of a certain type of motor. The multi-field coupling analysis engine synchronously compared the vibration spectrum with the electromagnetic noise and found that the amplitude of the characteristic frequency of the broken rotor bar was 6 dB offset from the standard value. The three-dimensional spatial positioning algorithm, combined with the infrared thermal image of the stator winding, locked the position of the broken bar in the spatial coordinate system at 127 mm from the drive-end bearing. The report generation module integrated the data of the entire process and generated a prediction report containing multiple thermal coupling evolution paths. The inspection records were bound to the blockchain hash value to ensure that quality traceability cannot be tampered with.

[0032] The system automatically starts a reinforcement learning optimization loop after producing every 500 motors. When the new nano-coated stator core is put into production, the feature extraction layer dynamically expands the coating peeling detection dimension within 8 hours, raising the detection sensitivity level. The latest optimization model is synchronized to all edge nodes via the 5G network, enabling uninterrupted upgrades across the entire production line.

[0033] Example 3: Implementation process on a DC motor armature production line When the armature winding process starts, distributed fiber optic sensors monitor the enameled wire tension fluctuations in real time with a spatial resolution of 0.1 mm. At the same time, high-frequency current sensors capture microsecond current transients during the commutator segment welding process. When the system detects a drop in winding tension at station No. 7 accompanied by harmonic distortion of the welding current, the data processing module immediately activates the multi-scale wavelet packet decomposition algorithm to separate the mechanical jamming characteristic frequency from the strong electromagnetic interference background. The artificial intelligence analysis module calls the deep residual network model. The model's pre-trained dataset contains 100,000 sets of armature defect samples, covering typical faults such as inter-turn short circuits and cold solder joints. The model outputs a real-time three-dimensional probability heat map showing that there is a risk of cold solder joints in the 24th slot of the commutator. Through trajectory similarity calculation, it matches the evolution path of motor fire accidents caused by cold solder joints in historical batches.

[0034] The decision-making feedback module simultaneously integrates production order data. When it identifies that the batch corresponds to an urgent order for medical equipment, the Pearson correlation coefficient engine calculates the dynamic correlation between quality risk and delivery time. If the correlation coefficient falls below the 0.25 threshold, the intelligent response chain is triggered: the winding machine servo parameters are automatically adjusted to compensate for tension loss, the laser welding quality re-inspection station is activated to perform a secondary penetration test on high-risk slots, and the armatures of the same batch are diverted to the helium mass spectrometer leak detection channel. The entire process is completed in a closed-loop control, avoiding the rework of medical motors worth millions of dollars.

[0035] The predictive maintenance sub-module builds a digital twin of armature manufacturing. Through thermal-mechanical coupling field simulation, it is found that fixture wear leads to abnormal winding tension. System deduction shows that continued production will cause a large number of commutator segments to be poorly welded. Therefore, a preventive maintenance plan is generated: the fixture positioning pins are replaced during the production line changeover gap, the welding robot posture angle is adjusted by 0.8 degrees, and the process parameter optimization instructions are directly transmitted to the manufacturing execution system through the OPCUA protocol. The maintenance plan is simultaneously pushed to the augmented reality glasses to guide maintenance personnel to accurately execute key items.

[0036] During the dynamic balancing test phase, the multi-physics field sensor array captured an imbalance of 0.05 grams per millimeter in a certain model of armature. The three-dimensional spatial positioning engine, combined with the electromagnetic noise spectrum and thermal imaging data, accurately located the missing counterweight at the third rib of the fan cover on the drive end. The system automatically generated a compensation plan: adding a 0.08-gram counterweight block at a specific coordinate point on the fan cover to improve the dynamic balancing accuracy to medical-grade standards. All quality data is encrypted and stored through blockchain to form an unalterable electronic quality passport.

[0037] The system starts an autonomous evolution cycle after completing the production of every 2,000 armatures. When it detects that a new type of graphene-enhanced enameled wire is put into use, the feature extraction layer adaptively expands the material performance detection dimension within 6 hours, improving the accuracy of insulation defect identification. The optimized model is synchronized to 12 production bases around the world through the 5G industrial private network in minutes, realizing the unification of quality control standards across factories.

[0038] Example 4: Hydrogen fuel cell air compressor motor production line The implementation process of the motor manufacturing quality intelligent detection system of the present invention on the ultra-high-speed permanent magnet synchronous motor production line is as follows: During the rotor magnet assembly stage, the quantum cascade laser sensor scans the gap between the magnet sheets in real time with high resolution, while the eddy current array sensor captures the micro-stress distribution of the silicon nitride ceramic sheath. When the system detects that the gap between the 9th slot magnets exceeds the tolerance by 4 microns and the local stress concentration factor exceeds 1.8, the data processing module immediately starts the tensor decomposition algorithm to separate the characteristic frequency of the assembly robot posture offset from the electromagnetic interference background. The artificial intelligence analysis module calls the three-dimensional convolutional neural network model, which is trained based on 50,000 sets of aerospace-grade motor defect samples. It generates a probability cloud map of magnet assembly defects in real time and uses the Mahalanobis distance matching algorithm to associate it with the electromagnetic howling failure mode caused by the gap tolerance in the historical data.

[0039] The decision-making feedback module simultaneously analyzes the priority of satellite navigation system component orders. When the Pearson correlation coefficient engine calculation shows that the batch quality risk is strongly correlated with the rocket launch window, a multi-level response is immediately triggered: the vacuum epoxy infusion pressure is adjusted to -95kPa to compensate for the gap error, the synchrotron radiation CT station is activated to perform submicron tomography on the high-risk rotor, and the same batch of products are automatically introduced into the plasma surface treatment channel to strengthen the bonding strength of the ceramic sheath. The closed-loop decision is completed to avoid the risk of single-point failure in the space environment.

[0040] The predictive maintenance submodule builds a material-structure coupled digital twin, and deduces the evolution trend of the hydrogen embrittlement effect through molecular dynamics simulation. The system predicts that intergranular corrosion cracks will occur in the silicon nitride sheath during continuous operation, and generates a space-grade prevention plan: in-situ nano-coating repair is performed in a space environment simulation cabin, hot isostatic pressing parameters are adjusted, and process reliability is remotely verified through the industrial metaverse platform. Maintenance instructions are directly connected to the space component quality chain system, and certification files are updated in real time.

[0041] During the overspeed test at 100,000 revolutions per minute, the fiber grating sensor array captured the rotor deformation offset, the multi-physics field coupling engine simultaneously analyzed the electromagnetic, thermal and fluid data streams, and the three-dimensional spatial positioning system accurately calibrated the deformation source to be located on the leeward side of the seventh rib of the cooling channel. By controlling the error range, the system automatically generated a zero-gravity compensation solution: a functional gradient material counterweight layer was deposited at specific coordinates of the titanium alloy casing, improving the high-speed dynamic balancing accuracy to spacecraft-level standards. The full-dimensional detection data was written to the interstellar file system IPFS after quantum encryption, constructing an unalterable deep space quality traceability chain.

[0042] The system initiates adaptive evolution of the space environment every time it completes the assembly of 300 air compressors. When it detects that the motors used in the lunar base are contaminated by lunar dust, the deep learning kernel autonomously expands the lunar dust composition analysis dimension, improves the sensitivity of foreign object recognition, and optimizes the model to be synchronized to the Earth-Moon space station in real time via the laser communication relay satellite to ensure the consistency of the extraterrestrial manufacturing quality system.

[0043] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0044] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent detection system for motor manufacturing quality, characterized by: include: Sensor data acquisition module: real-time collection of key parameters in the motor manufacturing process, including temperature, vibration, current, voltage and dimensional accuracy data; Data processing module: pre-processes the data collected by the sensor, including noise filtering, data normalization and feature extraction, to generate a standardized data set; Artificial Intelligence Analysis Module: This module uses deep learning models to build a virtual map of motor manufacturing quality based on standardized data sets, and identifies and classifies defects in motor components. Decision feedback module: Based on the defect probability and severity output by the artificial intelligence analysis module and the real-time working conditions of the production line, it generates quality adjustment instructions and feeds them back to the manufacturing execution system; Report generation module: automatically generates quality inspection reports, including defect location, type and improvement suggestions, and integrates with the enterprise resource planning system.

2. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The specific process of the sensor data acquisition module is as follows: Multiple sensor nodes are set up at key workstations on the motor production line, including the winding assembly section, rotor balancing section, and final assembly and testing section; Real-time data transmission to cloud database via IoT protocol; The parameter acquisition frequency is set to 10-50 times per second to ensure high-precision synchronous sampling.

3. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The feature extraction process of the data processing module includes: Perform frequency domain analysis on vibration signals to extract main frequency harmonic characteristics; Perform time series segmentation on temperature data and calculate temperature rise gradient; The principal component analysis method is used to reduce the dimension and generate a multidimensional feature vector.

4. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The deep learning model construction method of the artificial intelligence analysis module is: Build a defect recognition model based on a convolutional neural network, with training data including historical manufacturing defect samples and simulation anomaly data; The model input is a standardized data set, and the output is the probability distribution of defect categories; The model is optimized through transfer learning, and the network weights are adjusted based on the motor type and material properties.

5. The intelligent detection system for motor manufacturing quality according to claim 4, characterized in that: The defect identification and classification process also includes: Set the defect threshold range. When the output probability is greater than 0.8, it is judged as a high-risk defect. Spatial positioning of identified defects to generate a three-dimensional heat map; By matching the cosine similarity with the historical defect library, the potential failure mode is predicted. The formula is: in, is the element of the current defect feature vector, is the element of the historical defect feature vector, is the feature vector dimension.

6. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The quality adjustment instruction generation process of the decision feedback module is as follows: Obtain current production plan and equipment health status; The Pearson correlation coefficient is used to calculate the correlation between the health data group and the production plan data group. The formula is: in, is the observed value of the healthy data group, is the observation value of the production task data group, is the mean of the healthy data group, is the mean of the production task data group; When the correlation coefficient is lower than the preset threshold, real-time intervention instructions are triggered, including adjusting assembly parameters and pausing the workstation.

7. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: Also includes predictive maintenance submodule: Build a virtual model of the motor manufacturing process based on digital twin technology; Analyze the evolution trend of quality data and predict future defect rates; Output maintenance recommendations, including preventative parts replacement and process optimization.

8. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: System integration methods include: Synchronize data with manufacturing execution systems in real time; Support edge computing deployment to reduce latency; Use blockchain technology to ensure that data cannot be tampered with.

9. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The system performance optimization process is: Dynamically adjust AI model parameters to adapt to different motor models; Optimize detection algorithm efficiency through reinforcement learning; Regularly update the defect library to improve the system's generalization capabilities.

10. The intelligent detection system for motor manufacturing quality according to claim 1, characterized in that: The output process of the report generation module is as follows: Integrate test results and prediction data to generate structured reports; The report includes defect statistics, risk level and improvement timeline; Pushed to user terminals and cloud platforms through API interfaces.

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