Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset

TL;DR

This paper introduces a simple, interpretable rule-based method for predicting train failures in Porto, Portugal, emphasizing explainability over complex models, and demonstrates its effectiveness using sensor data.

Contribution

It proposes a straightforward online rule-based explainability approach with interpretable features for failure prediction in metro trains.

Findings

Three sensors suffice for failure prediction

Simple rules achieve high accuracy

Enhanced interpretability of failure causes

Abstract

Due to their high predictive performance, predictive maintenance applications have increasingly been approached with Deep Learning techniques in recent years. However, as in other real-world application scenarios, the need for explainability is often stated but not sufficiently addressed. This study will focus on predicting failures on Metro trains in Porto, Portugal. While recent works have found high-performing deep neural network architectures that feature a parallel explainability pipeline, the generated explanations are fairly complicated and need help explaining why the failures are happening. This work proposes a simple online rule-based explainability approach with interpretable features that leads to straightforward, interpretable rules. We showcase our approach on MetroPT2 and find that three specific sensors on the Metro do Porto trains suffice to predict the failures present in the dataset with simple rules.