Motor quality intelligent detection method and system based on data difference feedback

By using a smart motor quality detection method based on data difference feedback, the comprehensive weight of motor detection items is dynamically calculated, and the detection technology sequence is optimized using game theory. This solves the problem of insufficient statistics on low-frequency, high-loss faults in traditional motor detection, and improves detection efficiency and accuracy.

CN120993192BActive Publication Date: 2026-09-11PU YUAN DIAN JI ZHI ZAO (SU ZHOU) YOU XIAN GONG SI
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Patent Information

Application Number
CN202511101341.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-07
Publication Date
2026-09-11
Estimated Expiration
2045-08-07

AI Technical Summary

Technical Problem

Traditional motor testing solutions rely on a fixed ratio of historical data and real-time parameters, resulting in insufficient statistics on low-frequency, high-loss faults. This makes it impossible to dynamically adapt to motor aging or sudden parameter anomalies, leading to biases in fault risk assessment data and a double loss in testing efficiency and accuracy.

Method used

By collecting real-time operating parameters of the motor and comparing them with the rated parameters, detection feature information is generated. Combined with the fault database, weights are calculated to construct the comprehensive weight of the detection items, and detection technologies are dynamically screened. A game theory decision model is used to optimize the detection technology sequence to achieve dynamic fault risk assessment.

Benefits of technology

It improves the detection rate of low-frequency, high-loss faults, reduces over-testing of low-risk parameters, optimizes detection accuracy and efficiency, and achieves precise matching of fault risks.

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Abstract

The application discloses a motor quality intelligent detection method and system based on data difference feedback, relates to the technical field of motor operation quality detection, and provides the following scheme, which comprises collecting real-time operation parameters of current, vibration and temperature of the same type motor, and obtaining rated parameter benchmarks of the type; by comparing the real-time operation parameters with the rated parameter benchmarks, marking the real-time operation parameters that appear to be abnormal as real-time detection data, taking each parameter item as a detection item, and generating motor detection feature information; and calculating the historical fault weight of each detection item according to a motor fault database of the type. A detection technology conflict optimization model is constructed through game theory, a three-dimensional scoring system and a double-interval dynamic screening mechanism are established, high comprehensive weight detection items are automatically matched with optimal technology combinations, the average detection steps are reduced, the resource conflict rate is reduced, and the collaborative optimization of detection accuracy and efficiency is realized.
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