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.

Failure Modes and Effect Analysis of Gear Pair

“…The American Gear Manufacturing Association (AGMA) classified 36 failure mechanisms of gears into seven categories: wear, scuffing, surface fatigue, plastic flow, cracking, bending fatigue, and breakage. The gear failure is classified into two categories: lubricated and non-lubricated [2,[67][68][69][70]. Sliding wear, scoring, plastic flow, surface fatigue, cracking, fracture, and bending fatigue are the most common causes of gear failure.…”

“…The gears are the most efficient way to transmit the power [1]. It is found that most gears are failed 74% of time due to the service-related causes and 23% due to design and manufacturing errors [2]. The servicesrelated failures are due to consistent overloading, torque fluctuations, improper installation, improper lubrication, contamination of foreign particles, operational errors and so on.…”

“…The terminology and the definition of the expected gear failures are provided in the ANSI/AGMA 1010-F14 [3] and ISO 10825:1995 [4]. The failure of the gears is the primary reason for the transmission of the helicopter (19.1%) and wind turbine system (9.8%) and also results in the loss of human lives [2]. The modification in the gear profile and improper alignment leads to the stress concentration, increased vibration and noise, and the connecting system's failure [5][6][7].…”

Novel Instantaneous Wavelet Bicoherence for Vibration Fault Detection in Gear Systems

“…These indicators signalise potential machinery faults and are subjected to classification processes (e.g., k nearest neighbours or principal component analysis) [26,30]. References [31,32] propose correlation-based degradation parameters extracted from gearbox residual vibration signals. This approach allows the identification of multiple stages of natural pitting progression.…”

A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

“…In the literature, the prognostic approaches in a PHM framework can be implemented in three different ways: physics-based approaches, data-driven approaches, and hybrid approaches (a combination of data-driven and physics-based approaches) [ 1 ]. By solving a set of equations based on the physical laws and the knowledge of engineering and science, the physics-based prognostic approaches assess the component health and predict when the damage crosses a predefined failure threshold based on the mathematical modeling of the degradation process for a particular failure mode [ 2 ]. However, with high accuracy and efficiency requirements in component RUL prediction, physical model-based life prediction methods are difficult to meet modern needs due to their complexity, time-consuming, and nonuniversality.…”