Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review

Abstract

Abstract Artificial intelligence (AI) researchers created new techniques, developed and applied them to solve engineering problems since two decades. Although lots of AI techniques and approaches are available in mechanical engineering, there isn’t any survey aiming to review the existing works, systems and applications in the field of fracture mechanics. In this paper, the state of the art of five AI methods which are used in the field of fracture mechanics, is surveyed. This review is performed from the technical point of view on particular applications of artificial neural networks, Bayesian networks, genetic algorithms, fuzzy logic and case-based reasoning. After an overview of AI methods, sub-domains of engineering fracture mechanics with respect to the fault and failure analysis are described. The existing works from 1990 to 2016 are analysed and discussed in four categories as sub-domains of fracture mechanics: (a) failure mode and failure mechanism identification, (b) damage and failure detection and diagnosis, (c) fault and error detection, diagnosis and (d) mechanical fracture and fracture parameters. We analyse literature based on a classification of these five AI methods in order to highlight their main concepts and explain how they are applied in these sub-domains of fracture mechanics. Our analysis and discussion in this paper shows the advantages, limitations and research gaps in this field. Finally, perspectives and future research directions are outlined.