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

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