A review on diagnostic and prognostic approaches for gears

Abstract

Prognostics and health management has become a significant part of component life-cycle in modern industries. The prognostics and health management framework is implemented in the industries to identify the fault type, assess fault severity, and predict the future state or remaining useful life to optimize the maintenance activities. Three significant aspects of a prognostics and health management framework are diagnostics, prognostics, and decision making. This article presents a review of different types of diagnostic and prognostic approaches (i.e. physics-based, data-driven, and hybrid approaches) developed for the gears. The flow of information between diagnostics and prognostics parts of the framework is briefly discussed. Regarding the physics-based approaches, this article discusses different physics-based diagnostic and prognostic models developed for different types of gear failure modes such as crack, pitting, and wear. In the data-driven approaches, the article attempts to summarize the data processing techniques used for extracting fault-related information from the recorded raw vibration signal, health indicators developed for different kinds of gear failure modes, processing/selection approaches for best health indicators, fault classification, and fault prognostic models particularly developed for the gear. The article discusses how a hybrid approach can be developed by the integration of a data-driven diagnostics approach and a physics-based prognostics approach. Finally, uncertainty quantification of prognostic approaches, performance evaluation metrics, decision-making strategies, and future research and development perspectives are discussed. This article focuses on the diagnostic and prognostic approaches developed for gears, given the fact that these approaches for other components such as bearing and batteries are reviewed in the past.