Anomaly Detection of Marine Diesel Engines: A Novel Approach using Transformer Neural Networks for Reconstruction and Residual Analysis - Qin Liang; Erik Vanem; Knut Erik Knutsen; Vilmar Æsøy; Houxiang Zhang (International Journal of Prognostics and Health Management)
This paper proposes an unsupervised approach for anomaly detection in marine diesel engines using a transformer neural network based AutoEncoder (TAE) and residual analysis with Sequential Probability Ratio Test (SPRT) and Sum of Squares of Normalized Residuals (SSNR). This approach effectively captures temporal dependencies within normal time-series data, eliminating the need for labeled failure data. To assess the performance of the proposed methodology, faulty data is collected under the same operational profile as normal training data. The TAE is trained on the normal data, after which the faulty data is tested using the trained model. Subsequently, the SPRT and SSNR methods are used to analyze residuals from the observed (input) and reconstructed (output or tested) faulty data. Deviations exceeding a predefined threshold are identified as anomalous behavior. Furthermore, this study explores various architectures of transformer neural networks and other types of neural networks to conduct a comprehensive comparative analysis of the performance of the proposed approach. Insights and recommendations derived from the performance analysis are also presented, which offers valuable information for potential users to leverage. The test results demonstrate the ability of the proposed approach to accurately and efficiently detect anomalies in marine diesel engines. Specifically, it can detect anomalies more than 1000 time steps ahead of system alarms, outperforming other tested models.