Azzedine Bakdi: Weakly supervised machine learning for event data analysis: application to ship predictive maintenance

Maintenance plays a crucial role in ships and especially in the vital electric propulsion system. Intelligent predictive maintenance idealistically aims at preventing system failures and minimizing needless repairs, i.e., predicting failure likelihood and time to failure while providing the crew explainable predictions and recommending the best action for timely intervention. This presentation will cover a relevant work in collaboration with Sensor Systems in BigInsight, particularly a paper published under https://doi.org/10.1109/TII.2022.3144177. The failure prediction approach is driven by event logs, which include warnings, alarms, and operational information that describe all the happenings onboard the ship. The failure prediction objective is turned into classification and regression tasks; however, the training data pose three challenges. The events are irregular textual messages. The training data samples are not labelled. The datasets are extremely imbalanced, due to sparse failure events and multiple failure modes. The problem is casted into a weakly supervised machine learning framework. In a multiple instance learning process, the ungiven data labels are learned recursively while fitting the model parameters using deterministic annealing. The overall approach was tested on real ship data, and it successively forecasted few propulsion failures with explainable causes.