A health image for deep learning-based fault diagnosis of a permanent magnet synchronous motor under variable operating conditions: Instantaneous current residual map. (October 2022)

• Record Type: Journal Article
• Authors: Park, Chan Hee; Kim, Hyeongmin; Suh, Chaehyun; Chae, Minseok; Yoon, Heonjun; Youn, Byeng D.
• Is Part Of: Reliability engineering & system safety. Volume 226(2022)
• Publication Date: 2022-10
• Subjects: Permanent magnet synchronous motor -- Motor stator current signal -- Fault diagnosis -- Variable operating condition -- Deep learning -- Convolutional neural network -- Health image; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108715
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Instantaneous current residual map (ICRM) is proposed for CNN-based fault diagnosis. ICRM highlights fault-related physical behaviors of stator motor current signals. A motor fault type can be figured out by using ICRM as the 2D input to a CNN model. The effectiveness of ICRM for fault diagnosis of a PMSM is experimentally validated. Abstract: To take full advantage of a convolutional neural network (CNN) for deep learning-based fault diagnosis, many studies have examined the transformation of sensory signals into a two-dimensional (2D) input image. An important question to consider is: how can fault-related signatures in motor stator current signals be incorporated into the 2D input image to a CNN model for fault diagnosis of a permanent magnet synchronous motor (PMSM)? To answer the question, this study newly proposes a novel health image, namely instantaneous current residual map (ICRM). Inspired by the idea that the phase and amplitude modulations in motor stator current signals are related to faulty states of a PMSM, the overall procedure for constructing ICRM includes two key steps: (1) to calculate current residuals (CRs); and (2) to spread the scaled CR pairs into a 2D matrix. A type of faults can be figured out by analyzing a degree or shape of spreading of the CRs in ICRM. Moreover, ICRM is robust to variable operating conditions in practical settings because the scaled CRs that the effects of the operating conditions are reduced can highlight fault-induced irregularities. To demonstrate the effectiveness of ICRM, it was experimentally validated using a surface mounted PMSM, operated under variable-speed and different load torque conditions.