Anomaly Detection and Root Cause Analysis of Ship Main Engines: Explainable Artificial Intelligence-Based Methodology Considering Internal Sensors and External Environmental Factors

Abstract

The main engine of a ship plays a crucial role in providing propulsion. In recent times, there has been growing interest in a data-driven monitoring approach that utilizes sensor data to complement the preventive maintenance-centered maintenance strategy. Previous studies have proposed methodologies that apply anomaly detection algorithms to the sensor data within the main engine. However, these methodologies have limitations as they only focus on analyzing internal sensor data and fail to consider external factors such as operating conditions, marine environment, and weather. Additionally, the use of black-box approaches makes it challenging to determine the specific factors causing anomalies. To address these limitations, this study introduces a method that employs Explainable Artificial Intelligence (XAI) techniques to identify the causes of anomalies in ship main engines. The proposed method involves calculating anomaly scores using Variational AutoEncoder on collected sensor data and training a separate model to predict anomaly scores by considering external factors like operating conditions and weather. Furthermore, the SHAP (Shapley Additive Explanations) technique is utilized to quantify the contributions of external factors to the anomaly scores. This enables the analysis of individual data features and facilitates both local and global analysis for identifying the causes of anomalies and diagnosing faults. The proposed methodology was validated through a case study using data collected from a container ship over an 18-month period, demonstrating its effectiveness in identifying the causes of anomalies in the ship’s main engine.