Bi-AAE: A binary adversarial autoencoder deep neural network model for anomaly detection in system-levels marine diesel engines

Abstract

Marine diesel engines typically use multivariate time series for health condition monitoring, the anomaly detection of which is fundamental and critical for an entity's operation and management. However, detecting anomalies in multivariate time series remains a grand challenge due to the complex behavior of marine machinery. Therefore, this research proposes a binary adversarial autoencoder model to overcome this issue. The framework is built upon the principles of reconstruction models by fusing Generative Adversarial Networks and Adversarial Autoencoder approaches to analyze the degree of anomaly in multivariate time series. Additionally, it identifies anomalies through a threshold obtained by a statistical distribution-based and unsupervised setting method. To emphasize the model's performance, multivariate condition parameters from actual ship diesel engines are utilized for validation. Two membership functions are recognized as the model metrics to characterize its best performance, evidenced by scores of 0.937 for F1 scores and 0.910 for precision. The proposed strategy can be applied to different types of monitoring systems in the ship's engine room to realize system-level operation and maintenance.