Fault identification and severity analysis of rolling element bearings using phase space topology

Mohamad, T. Haj; Nataraj, C.

doi:10.1177/1077546320926293

Cited by 13 publications

(12 citation statements)

References 65 publications

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“…Condition monitoring (CM) has received considerable attention and is being implemented prosperously in fault diagnosis and prognosis as it ensures operational safety, improves reliability, and reduces the premature breakdowns of the gearboxes. [4][5][6] The implementation of vibrationbased CM is significant as the vibration signal is dynamic, and the presence of a defect results in amplitude and distribution modulations.…”

Section: Background and Literature Surveymentioning

confidence: 99%

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An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

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The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.

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Section: Background and Literature Surveymentioning

confidence: 99%

“…Thus, each successive iteration (i) yields to an ordered dataset of (2 i21 , 2 i21 * SD i ). Further, the log-log plot is plotted between SD and the size of the group (k) and the Hurst exponent (H) is obtained by fitting the power law, 35 which is represented using the equation (6).…”

Section: Dispersion Analysismentioning

confidence: 99%

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

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“…An notable approach is improving and generalizing the information content of input of a machine learning model by physics-informed feature extraction. This idea has been explored for a variety of problems including fault diagnostics of prototypical nonlinear mechanical systems [3,4] bearing fault diagnostics [5][6][7][8], gear diagnostics [9], crack detection [10], flood modeling [11] and bifurcation analysis of nonlinear dynamic systems [12]. Other ideas include synthetic data generation by simulation of a physics-based model to amplify a machine learning model input domain [13]; formulating a hybrid loss function for the learning process and penalizing the model for deviation from physical constraints [2]; pre-training a machine learning model using physics-based simulation data and fine-tuning based on a limited number of observations [14]; customizing the topology of a machine learning model by adding intermediate physics informed components [15]; designing the structure of a machine learning model using physical laws [16]; uncertainty reduction in machine learning using physics [17]; and, integrating machine learning models with first principle physical laws for solving partial differential equations [18].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling of a Multidimensional Coupled Nonlinear System With Integration of Hamiltonian Mechanics

Abbasi

Kambali

Nataraj

2023

Preprint

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This study concerns hybrid modeling of a multidimensional coupled nonlinear system. The underlying basis for the model is derived from Hamiltonian mechanics capitalizing on the broad utility and efficiency of energy-based reasoning in modeling high-dimensional systems. The hybrid model is essentially an artificial neural network with a computational graph that is modified from conventional neural networks in a few significant ways. The first modification includes incorporating an intermediate scalar function representing the Hamiltonian learned from data. The second modification enhances input/output channels for capturing the multidimensional dynamics of the system. The main goal of such hybrid reasoning is to improve the extrapolation capability of the model by enforcing conformance with some key aspects of the underlying physics in the form of a bias. The results demonstrate that incorporating this physics-based bias into the hybrid model empowers it to produce long-term and physically plausible predictions. The proposed modeling approach also shows high scalability for energy-based modeling of multidimensional dynamic systems in general.

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“…have been proposed to recognize bearing faults. [6][7][8][9][10][11][12][13] However, these statistical measures often suffer from certain disadvantages. For example, RMS is capable of detecting early stage bearing faults but may be sensitive to outliers.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder

Prasad

Dantreliya

Chande

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Rotating machines (RMs) have vast applicability in almost all the industries in mechanical domain. Rolling element bearings (RBs) are the key elements to ensure that the RMs perform efficiently. RBs are highly prone to wear and tear which could have devastating consequences such as massive economic losses and accidents. In the past, many time-domain based condition-indicators such as root mean square (RMS), skewness and kurtosis, etc. have been proposed by researchers to diagnose the bearing faults and prevent RM failures. However, they are often insensitive to early stage faults, affected by outliers and possess poor degradation tracking characteristics. To overcome these shortcomings, this paper proposes a novel statistical feature extraction technique called as multiscale statistical moment (MSM) analysis, in combination with sparse autoencoder to detect the incipient faults as well as track the progression of wear. Firstly, the vibration signal are acquired from the bearings to be monitored. Secondly, the MSM features are extracted from the vibration signals. Thirdly, the MSM features corresponding to normal conditions are utilized to train the sparse autoencoder network. Fourthly, the MSM features corresponding to test conditions are supplied to the pre-trained sparse autoencoder model. The MSM technique offers the advantage that it extracts the fault properties contained in multiple time-scales of the vibration signals instead of a single time-scale only. Finally, the dissimilarity between the actual and predicted output is measured to obtain the bearing health indicator (BHI). The experimental results demonstrate that the suggested BHI detects the faults at early stages, possess better sensitivity and trends the bearing degradation more accurately as compared to the traditional techniques such as RMS, kurtosis, and BHI obtained with statistical moment features at single-scale only.

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Fault identification and severity analysis of rolling element bearings using phase space topology

Cited by 13 publications

References 65 publications

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Hybrid Modeling of a Multidimensional Coupled Nonlinear System With Integration of Hamiltonian Mechanics

An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder

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