Enhancing Anomaly Detection in Maritime Operational IoT Time Series Data with Synthetic Outliers

Abstract

Detecting anomalies in engine and machinery data during ship operations is crucial for maintaining the safety and efficiency of the vessel. We conducted experiments using device data from the maritime industry, consisting of time series records from IoT (Internet of Things) datasets such as cylinder and exhaust gas temperatures, coolant temperatures, and cylinder pressures collected from various sensors on the ship's equipment. We propose data enrichment and validation techniques by generating synthetic outliers through data degradation and data augmentation with a Transformer backbone, utilizing the maritime operational data. We extract a portion of the input data and replace it with synthetic outliers. The created anomaly data are then used to train the model via a self-supervised learning approach. Synthetic outliers are generated using methods such as the arithmetic mean, geometric mean, median, local scale, global scale, and magnitude warping. With our methodology, we achieved a 17.23% improvement in F1 performance compared to existing state-of-the-art methods across five publicly available datasets and actual maritime operational data collected from the industry.