Early Anomaly Detection of LNG-Carrier Main Engine System based on Multivariate Time-Series Boundary Forecasting and Confidence Evaluation Technique

Donghyun Kim, Taigon Kim, Minji An, Yunju Baek

http://doi.org/10.5626/JOK.2023.50.5.429

Recently, a variety of studies have been conducted to detect abnormal operation of ships and their causes and in the marine and shipbuilding industries. This study proposed a method for early anomaly detection of the main engine system using a multivariate time series sensor data extracted from LNG carriers built at a shipyard. For early anomaly detection, the process of predicting the future value through the sensor data at present is necessary, and in this process, the prediction residual, which is the difference between the actual future value and the predicted value, is generated. Since the generated residual has a significant effect on the early anomaly detection results, a compensating process is necessary. We propose novel loss functions that can learn the upper or lower prediction boundary of a time-series forecasting model. The time-series forecasting model trained with the proposed loss function improves the performance of the early anomaly detection algorithm by compensating the prediction residual. In addition, the real-time confidence of the predicted value is evaluated through the newly proposed confidence model by utilizing the similarity between time-series forecasting residual and confidence residual. With the early anomaly detection algorithm proposed in this study, the prediction model, which learns the upper boundary, outputs the upper limit of the predicted value that can be output by the baseline prediction model learned with the MSE loss function and can predict abnormal behavior that threshold-based anomaly discriminator could not predict because the future prediction of the baseline model is lower than the actual future value. Based on the results of this study, the performance of the proposed method was improved to 0.9532 compared to 0.4001 of the baseline model in Recall. This means that robust early anomaly detection is possible in various operating styles of the actual ship operations.