Latest developments in gear defect diagnosis and prognosis: A review

Kumar, Anil; Gandhi, C.P.; Zhou, Yuqing; Kumar, Raj; Xiang, Jiawei

doi:10.1016/j.measurement.2020.107735

Cited by 158 publications

(54 citation statements)

References 130 publications

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“…Such failures could be disastrous or could lead to the shutdown of the entire production lines, potentially causing huge losses [3]. erefore, fault detection of bearing has been a great challenge in recent decades [4]. In order to obtain the fault feature, the vibration signal is one of the most important sources of information for monitoring conditions of rolling element bearing.…”

Section: Introductionmentioning

confidence: 99%

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

Wang

Sheng

Deng

et al. 2021

Mathematical Problems in Engineering

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The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.

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Section: Introductionmentioning

confidence: 99%

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

Wang

Sheng

Deng

et al. 2021

Mathematical Problems in Engineering

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“…They can be defined in the time domain [ 18 ] as the RMS, peak-to-peak, etc., or the energies/amplitudes of particular spectral components or frequency bands [ 19 ]. A review can be found in [ 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

Lipiński

Brzychczy

Zimroz

2020

Sensors

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Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.

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“…Gear systems commonly exist in mechanical equipment, such as wind turbines, shearer, and the motor vehicle [1]. Due to the excessive load, poor lubrication, and other harsh operating conditions, faults often occur in the gear systems.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributions of this paper are summarized as follows: 1 a new dynamical model that considers double-teeth spalling fault is proposed, and the dynamic characteristics considering the effect of the relative position of two faulty teeth are investigated. This investigation is proposed for the first time in previous studies 2 by integrating the VMD and the ALO, a novel parameter-adaptive VMD is proposed to eliminate the strong noise.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Characteristics of a Gear System with Double-Teeth Spalling Fault and Its Fault Feature Analysis

et al. 2020

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Tooth spalling is one of the most destructive surface failure models of the gear faults. Previous studies have mainly concentrated on the spalling damage of a single gear tooth, but the spalling distributed over double teeth, which usually occurs in practical engineering problems, is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, and the relative position of two faulty teeth. Moreover, to ensure the accuracy of dynamic model verification results and reduce the difficulty of fault feature analysis, a novel parameter-adaptive variational mode decomposition (VMD) method based on the ant lion optimization (ALO) is proposed to eliminate the background noise from the experimental signal. First, the ALO is used for the self-selection of the decomposition number K and the penalty factor â of the VMD. Then, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the ALO-VMD, and the IMFs whose effective weight kurtosis (EWK) is greater than zero are selected as the reconstructed signal. Combined with envelope spectrum analysis, the de-nosing ability of the proposed method is compared with that of the method known as particle swarm optimization-based variational mode decomposition (PSO-VMD), the fixed-parameter VMD, the empirical mode decomposition (EMD), and the local mean decomposition (LMD), respectively. The results indicate that the proposed dynamic model and background elimination method can provide a theoretical basis for spalling defect diagnosis of gear systems.

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Latest developments in gear defect diagnosis and prognosis: A review

Cited by 158 publications

References 130 publications

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults

Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

Dynamic Characteristics of a Gear System with Double-Teeth Spalling Fault and Its Fault Feature Analysis

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