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.

Early identification of rolling element defects is always a topic of interest for researchers and the industry. For early fault identification, a simple and effective dynamic degradation monitoring method using variational mode decomposition (VMD) based trigonometric entropy measure is developed. First, vibration signals are obtained and are further decomposed using VMD to obtain various frequency modes. Second, a trigonometric entropy measure is developed to monitor the dynamic change occurring in the health of bearing. Third, trigonometric entropy measure of various VMD modes is computed. Fourth, the variance of measure is computed and two modes having the highest variance are selected for principal component analysis (PCA). Thereafter, PCA of selected measures is carried out. Finally, dynamic degradation monitoring is carried out by observing the trend in the principal component having the highest diverse information. The testing of newly developed VMD based trigonometric entropy measure is carried out on the two different types of data set. One is from XJTU-SY Bearing datasets and another is from the Centre for Intelligent Maintenance Systems. The experimental study reveals that the proposed method is capable of raising the alarm about the initiation of defects at a very early stage. Compared to existing indicators such as kurtosis, RMS, and Shannon entropy, the proposed method is superior while carrying out defect degradation monitoring.

This article presents an ensemble decision tree–based random forest regression methodology for remaining useful life prediction of spur gears subjected to pitting failure mode. The random forest regression methodology does not require an elaborate statistics background knowledge and has an inbuilt health indicator selection capability compared to other existing data-driven remaining useful life prediction approaches. A correlation coefficient parameter based on the residual vibration signal is used for monitoring and detecting the pitting progression in spur gears. The effectiveness of the correlation coefficient of the residual vibration signal is assessed over the other existing health indicators for pitting fault progression. To show the inbuilt best health indicator selection capability of the random forest regression model, initially, eight indicators (existing seven and the correlation coefficient of the residual vibration signal) were used for model training. In addition, the effect of fusing the vibration sensor data from multiple positions on the gearbox on prediction accuracy of the random forest regression model is also evaluated. The accuracy in the remaining useful life prediction is found to increase after fusing the correlation coefficient of the residual vibration signal based health indicator derived from the accelerometers located at multiple positions on the gearbox in comparison to data from a single accelerometer. Furthermore, the accuracy of the proposed methodology is tested and proven using five accelerated run-to-failure experimental data collected from the specially built test rig.