A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system  

作　　者：Liuxuan Wei Linfang Qian Manyi Wang Minghao Tong Yilin Jiang Ming Li 

机构地区：[1]School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China [2]Northwest Institute of Mechanical and Electrical Engineering,Xianyang 712099,China

出　　处：《Defence Technology（防务技术）》2024年第4期225-237,共13页Defence Technology

基　　金：supported by the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20210347)。

摘　　要：The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

关 键 词：Fault diagnosis Deep learning Multi-scale convolution Open-circuit Convolutional neural network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] E924[自动化与计算机技术—控制科学与工程]