A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Explainable Predictive Maintenance

Abstract

The role of maintenance in modern manufacturing systems is becoming a more significant contributor to organizational benefit. World-class enterprises are pushing forward with “predict-and prevent” maintenance instead of embracing the drawbacks of reactive maintenance (or a “fail-and fix” approach). The advancement towards Artificial Intelligence (AI), Internet of Things (IoT) and cloud computing has led to a shift in maintenance paradigms with the rising interest in Machine Learning (ML) and in particular deep learning. However, opaque box AI models are complex and difficult to understand and explain to the lay user. This limits the use of these models in predictive maintenance where it is crucial to understand and analyze the model before deployment and it is imperative to understand the logic behind any given decision. This paper introduces a Type-2 Fuzzy Logic System (FLS) optimized by the Big-Bang Big-Crunch algorithm that allows maximizing the interpretability of a model as well as its prediction accuracy for the faults which may occur in future. We tested the proposed type-2 FLS model on water pumps where data was collected in real-time by our proprietary hardware deployed at Aquatronic Group Management Plc. The observations indicate that the proposed system provides a highly interpretable and accurate model for predicting the faults in equipment for building services, process and water industries. The system predictions are used to understand why a particular fault may occur, leading to improved and better-informed service visits for the customers thus reducing the disruptions faced due to equipment failures.