Fault diagnosis of marine machinery via an intelligent data-driven framework

Large amounts of ship accidents caused by marine machinery indicate the significance of fault diagnosis for marine machinery. Since numerous onboard sensor signals contain feature information that reflects the status of marine machinery and considering the time-consuming and experience-dependent of previous data-driven fault diagnosis methods, an intelligent data-driven framework based on a multi-head attention neural network (MANet) is proposed for the fault diagnosis of marine machinery in this work. MANet integrates the multi-head attention (MHA) mechanism, convolutional layers, and residual (Res) structure. Therein, the global correlation features in long-term operating signal sequences extracted by MHA can effectively characterize the status variation of marine machinery; the local fault features in operating signals extracted by convolutional layers can effectively characterize status information at certain moments for marine machinery; the application of Res structure in MANet is conducive to alleviating the gradient propagation anomaly. With a case study for the fault diagnosis of marine machinery, the superiority of MANet in terms of the accuracy and reliability of fault diagnosis is validated by comparing it with two recently published methods. Moreover, the results under different cases of engine loads and application scenarios validate that MANet possesses promising generalization performance for fault diagnosis. Hence, the proposed method provides a new approach for the real-time fault diagnosis of marine machinery in engineering applications.