Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study. (1st November 2021) - Tan, Yanghui; Zhang, Jundong; Tian, Hui; Jiang, Dingyu; Guo, Lei; Wang, Gaoming; Lin, Yejin
Abstract: Fault diagnosis of marine machinery is of utmost importance in modern ships. The widely used machine learning techniques have made it possible to realize intelligent diagnosis by using large amounts of sensory data. However, the detection of simultaneous faults is still a challenge in the absence of simultaneous fault data. Multi-label classification has recently gained popularity in simultaneous fault diagnosis with promising results. The contribution of this work is to carry out a comparative study of several state-of-the-art multi-label classification algorithms for simultaneous fault diagnosis of marine machinery based on single fault data. The proposed method is experimentally validated with a dataset generated from a real data validated simulator of a Frigate. The experimental results show the effectiveness of the proposed method, which can provide decision support for the application of multi-label classification in the simultaneous fault diagnosis of similar marine systems. Highlights: Several representative multi-label classifiers are investigated for simultaneous fault diagnosis of marine machinery. Only single fault data is required in the training process, making the approach relatively realistic. It is unnecessary to clear all fault types before training.