[Paper Review] Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

Zhaoyi Xu, Joseph H. Saleh|arXiv (Cornell University)|Aug 19, 2020

Anomaly Detection Techniques and ApplicationsComputer Science138 references20 citations

TL;DR

This paper reviews the current state and future potential of machine learning (ML) in reliability engineering and safety applications, synthesizing fragmented literature across domains like fault detection, risk assessment, and accident analysis. It outlines ML categories, highlights deep learning's growing role, and identifies key opportunities for improving safety decision-making through data-driven insights from complex accident datasets.

ABSTRACT

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.

Motivation & Objective

• To synthesize the fragmented literature on machine learning applications in reliability and safety engineering.
• To provide a structured overview of ML categories, tasks, and algorithms relevant to reliability and safety.
• To examine the use of deep learning in enhancing accident data analysis and risk prediction.
• To identify key challenges and future research opportunities for integrating ML into safety-critical systems.
• To support better-informed decision-making in safety and reliability through improved data-driven insights.

Proposed method

• Systematic review of existing literature on ML applications in reliability and safety, categorized by task type (e.g., classification, anomaly detection).
• Classification of ML models into broad categories such as supervised, unsupervised, and reinforcement learning, with focus on relevant sub-tasks.
• Analysis of case studies and publications demonstrating ML use in failure prediction, system reliability, and accident reconstruction.
• Emphasis on deep learning techniques (e.g., neural networks) for handling complex, high-dimensional safety data.
• Identification of methodological trends and limitations in current ML applications within safety-critical domains.
• Roadmapping of future research directions based on identified gaps and emerging capabilities.

Experimental results

Research questions

• RQ1 How has machine learning been applied to reliability and safety engineering across different domains?
• RQ2 What are the key ML categories and algorithms most relevant to reliability and safety applications?
• RQ3 In what ways does deep learning improve upon traditional methods in analyzing accident and failure data?
• RQ4 What are the major challenges and limitations in adopting ML for safety-critical systems?
• RQ5 What future research opportunities exist for advancing reliability and safety through machine learning?

Key findings

• Machine learning enables more accurate insights from accident datasets than traditional analysis tools, leading to improved decision-making.
• Deep learning has gained popularity in safety applications due to its ability to model complex, non-linear relationships in high-dimensional data.
• The integration of ML into reliability engineering offers novel opportunities for predictive maintenance, fault detection, and risk assessment.
• Despite progress, the literature remains fragmented, necessitating a unified framework for knowledge synthesis and application.
• Future applications of ML in safety are expected to enhance system resilience, reduce human error, and support proactive safety management.
• The paper identifies a strong potential for ML to transform safety analysis by uncovering hidden patterns in failure data that are difficult to detect with conventional statistical methods.