Failure prediction using artificial intelligence for heavy duty equipment

Abstract

Failure analysis of heavy-duty equipment is becoming one of the largest areas where artificial intelligence is finding application. The process of detecting the conditions of fault varies widely across various approaches of the diagnostic system to different working conditions of equipment. The application of decision-making for fault detection methods allows in-depth analysis by creating a reasoning system that works like human being. Many faults detection decision-making systems have been rule-based where slight changes in prediction data affect the entire result. On the other hand, the prediction of the failure analysis includes real-time data or various modelling techniques on which effective algorithms have been made. A combination of AI systems with other advanced prediction models can be used for optimum maintenance methodologies for heavy-duty equipment.