Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines

With the development of smart factories, deep learning, which automatically extracts features and diagnoses faults, has become an important approach for fault diagnosis. In this article, a novel interturn short-circuit fault (ISCF) diagnosis approach using an attention-based recurrent neural network is proposed. An encoder-decoder architecture using an attention mechanism diagnoses the ISCF by estimating a fault indicator that directly reflects the severity of the fault, using currents and rotational speed signals as inputs. The attention mechanism helps the decoding process in accurate diagnosis and solves the long-term dependence problem of the encoder-decoder structure. The proposed algorithm uses only three-phase current and rotational speed as the inputs to evaluate the severity of the ISCF and enable early stage diagnosis of ISCF. The diagnosis of ISCF is achieved in various operating points and fault conditions, and no additional sensors, such as voltage and vibration sensors, are required. Experimental results for various operating and fault conditions demonstrate that the proposed method effectively diagnoses ISCFs.

Automated summary

With the development of smart factories, deep learning has become an important approach for fault diagnosis, and in this article, a novel ISCF diagnosis approach using an attention-based recurrent neural network is proposed. By utilizing an encoder-decoder architecture with an attention mechanism, the proposed method accurately diagnoses ISCF using only three-phase current and rotational speed signals as inputs.