A Review on Intelligent Fault Detection in Rolling Element Bearings

Balraj, Robin Raj; Barla, Madhavi; Tingarikar, Govardhan

doi:10.1051/e3sconf/202018401044

Cited by 1 publication

(1 citation statement)

References 50 publications

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“…Particularly, for the analysis of stator monitoring [13] and rotor analysis [14]. Vibration monitoring have been the area of interest for various researchers using different transformation methods for analysis [15][16][17][18]. The vibration bearing as well as fan faults are classified using a set of features which are extracted from the raw vibration signals [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

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The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

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