Diagnosis of Oil-Drilling Permanent Magnet Synchronous Motor Interturn Short Circuit Under Physical Fault Model Training Sample

In the oil-drilling environment, permanent magnet synchronous motors (PMSMs) are constantly exposed to high temperatures, strong vibrations, and variable velocity. There is little real-world interturn short-circuit (ITSC) data from the oil well. These problems lead to most ITSC fault characterization types being invalid, and no real-world ITSC data are available to diagnose neural network model training. To address these challenges, we propose a zero real-world fault training data diagnosis (ZRFTD) framework. In this framework, the diagnostic network is trained by physical fault model data. Initially, the internal physical construction of PMSM-ITSC is modeled to generate trainset data. Second, the proposed prediagnostic mechanism accurately decides when to start the diagnostic neural network, avoiding unnecessary operation when the PMSM is healthy. And self-adjustment peak-to-peak alignment difference (APAD) method is proposed to extract the ITSC current characteristics stably. Third, we constructed a simplified local chunking parallel transformer neural network (SLCPTN) as a fault diagnostic network, greatly reducing computational consumption. Finally, an oil-drilling up-ground experimental platform is established to generate the test set data that are inputted into the trained SLCPTN for ITSC fault-level diagnosis to assess the efficacy of ZRFTD. The related theoretical derivation and experimental validation prove the feasibility of the ZRFTD framework and achieve an accuracy of 99.2% in classifying real-world PMSM-ITSC data under an oil-drilling environment.