Multiple Instance Learning With Random Forest for Event Logs Analysis and Predictive Maintenance in Ship Electric Propulsion System

Abstract

In this article, a novel weakly supervised machine learning approach is proposed for intelligent predictive maintenance (IPdM). It employs balanced random forest and multiple instance learning based on event logs from ships’ electric propulsion systems. The objectives are predicting failure likelihood, time to failure, and explainable predictions to ensure timely crew intervention. The IPdM approach uncovers, then learns, and classifies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sequences of</i> events that represent early causes or symptoms to forecast imminent failures. In particular, this article contributes effective solutions to irregular, imbalanced, and unlabeled data issues where conventional methods become obsolete. First, the events occur at irregular intervals; they include alarms, warnings, and operational information collected across multiple units and control systems. Second, the datasets exhibit extreme imbalance due to few failures and multiple failure modes; this entails biased predictions. Third, the training datasets are weakly labeled; only the failure timestamp is known without any expert input on prior causes or early symptoms. Temporal random indexing is proposed to transform textual log messages into a numerical lower dimensional space where timeseries analyses are applicable. Balanced random-forest models are developed for unbiased classification and regression. The overall approach learns recursively the ungiven data labels while training the base learners. The IPdM approach is validated through millions of events of multithousand types collected from two years of seagoing vessels. It successfully forecasts actual propulsion failures and performs better when compared with contemporary methods.