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

In the oil drilling environment, permanent magnet synchronous motors (PMSM) are constantly exposed to high temperatures, strong vibrations, and variable velocity. There is little real-world Inter-turn 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 for diagnosis neural network model training. To address these challenges, we propose a zero real-world fault training data diagnosis (ZRFTD) framework. In this framework, the diagnosis network is trained by physical fault model data. Initially, the internal physical construction of PMSM-ITSC is modeled to generate trainset data. Secondly, the proposed pre-diagnosis mechanism accurately decides when to start the diagnosis 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. Thirdly we constructed a simplified local chunking parallel transformer neural network (SLCPTN) as a fault diagnosis network, greatly reducing computational consumption. Finally, an oil-drilling up-ground experimental platform is established to generate the testset data which 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.