An efficient method for bearing fault diagnosis

Geetha, G.; Geethanjali, P.

doi:10.1080/21642583.2024.2329264

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…These methods either directly extract or reconstruct signaling components in the time domain, often relying on specific iterative algorithms or model optimization techniques to achieve effective signal decomposition, or they adopt techniques such as wavelet transforms, which essentially employ filtering methods aimed at extracting waveform information of different frequencies from the signal. Among them, empirical mode decomposition (EMD) is currently the most prominent signal decomposition method [4]. However, EMD also presents numerous challenges, such as boundary effects, mode mixing, sensitivity to noise, and a lack of mathematical theoretical support.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network

Lin,

Wang,

Guo

et al. 2024

Symmetry

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The symmetry of vibration signals collected from healthy machinery, which gradually degenerates with the development of faults, must be detected for timely diagnosis and prognosis. However, conventional methods may miss spatiotemporal relationships, struggle with varying sampling rates, and lack adaptability to changing loads and conditions, affecting diagnostic accuracy. A novel bearing fault diagnosis approach is proposed to address these issues, which integrates the Gramian angular field (GAF) transformation with a parallel deep convolutional neural network (DCNN). The crux of this method lies in the preprocessing of input signals, where sampling rate normalization is employed to minimize the effects of varying sampling rates on diagnostic outcomes. Subsequently, the processed signals undergo GAF transformation, converting them into an image format that effectively represents their spatiotemporal relationships in a two-dimensional space. These images serve as inputs to the parallel DCNN, facilitating feature extraction and fault classification through deep learning techniques and leading to improved generalization capabilities on test data. The proposed method achieves an overall accuracy of 96.96%, even in the absence of training data within the test set. Discussions are also conducted to quantify the effects of sampling rate normalization and model structures on diagnostic accuracy.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for Bearing Fault Diagnosis Based on a Parallel Deep Convolutional Neural Network

Lin,

Wang,

Guo

et al. 2024

Symmetry

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“…It is noteworthy that the main advantage of the k-nearest neighbor-based fault detection techniques have the ability to deal with the aforementioned characteristics effectively including non-linearity, multimodality, and non-Gaussian data [37]. k-NN method is an efficient method for bearing fault diagnosis [38]. Many researchers have presented various process monitoring methods based on k-NN rule [39][40][41][42][43][44]…”

Section: Introductionmentioning

confidence: 99%

Comparative performance analysis of different types of k-nearest neighbor (k-NN) classifiers for fault diagnosis of air compressor setup

Dhakar,

Singh,

Gupta

2024

Eng. Res. Express

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This paper presents a method for identifying Reciprocating Air Compressor (RAC) faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with Uni-directional microphones using a LABVIEW-based data collection interface and data acquisition (DAQ) hardware unit that has several ports. Accumulated one healthy and seven unhealthy signals of RAC setup processed using signal processing technique called Local Mean Decomposition (LMD). Further, six Statistical Properties (SPs) have been evaluated in or-der to extract fault features namely: mean (US), variance (σs2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and Kurtosis index (Ki). Extracted fault features are classified using various types of k-NN classifiers namely: fine (f-kNN), medium (m-kNN), coarse (c-kNN) and weighted (w-kNN). It has been found that LMD along with 6 statistical properties and form different type of k-NN classifiers, the weighted k-NN classifier has a greater accuracy of 86.74%, which is quite accurate as compared to other k-NN classifiers.

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