Towards Explainable Deep Learning Models for Fault Prediction based on IoT Sensor Data

Mansouri, Taha

Abstract

This thesis addresses a pressing issue in the realm of IoT-based fault prediction using sensor data, focusing on the crucial yet challenging aspect of explainability within deep learning models. While deep learning has showcased remarkable advancements in fault prediction, its inherent black-box nature poses obstacles in understanding the rationale behind its predictions. This lack of transparency impedes the practical implementation and adoption of these models in critical decision-making scenarios. The thesis comprises a comprehensive investigation encapsulated within five published papers spanning over a decade, from 2011 to 2023. These papers collectively contribute to the domain of Explainable Artificial Intelligence (XAI), delving into various approaches aimed at shedding light on the inner workings of complex deep learning models. The earlier papers serve as building blocks, laying the groundwork for fundamental concepts explored and expanded upon in subsequent submissions. Each paper makes distinct contributions to the field of AI. These contributions include the introduction of a novel evolutionary algorithm, applying Fuzzy Cognitive Maps for failure and fault modelling, proposing an evolutionary algorithm for training Fuzzy Cognitive Maps, developing an explainable deep learning model for fault prediction, and utilizing insights derived from preceding research to explaining the inner processes of deep learning models. Through a meticulous analysis of these publications, this thesis effectively addresses the fundamental research questions posed. It offers insights into overcoming the challenges associated with the opacity of deep learning models, paving the way for more transparent and interpretable AI models, particularly in the domain of fault prediction using IoT sensor data.

• Thesis Type: Thesis
• Deposit Date: Jan 5, 2024
• Keywords: Deep Learning; Explainable AI; Fault Prediction; IoT; Predictive Preventive Maintenance
• Award Date: Jan 26, 2024