Machine Bearing Fault Diagnosis System using Tri-Axial Accelerometer

Aziz, Sumair; Ahmed, Muhammad; Naqvi, Syed Zohaib Hassan; Khan, Muhammad Umar; Imtiaz, Atif; Waseem, Athar

doi:10.1109/icecce49384.2020.9179326

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Signal segmentation was performed using EMD and classification of normal state, offset pulley fault, wear fault, and cracking faults were done through knearest neighbors. In another work [34], vibration signals were collected from rotating machinery (motor) using AX-3DS wireless tri-axial accelerometer. Three machine states namely, normal, inner race bearing fault, outer race bearing fault were discussed in study.…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

Self Cite

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“…Three-axis signals were used to extract two features, Skewness (SK) and Root mean square (RMS), which were then fed into the Support vector machine (SVM) classi er. The method proposed produces results with an average accuracy of 99.8% [15].…”

Section: Introductionmentioning

confidence: 99%

System Design for the Detection of Induction Motor Faults using Multi-Domain Feature Fusion

Usman

Aziz

Khan

2023

Preprint

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Accurate and early detection of the bearing faults would make it possible to solve the problem at a lower cost by replacing relevant parts. In this article, a machine learning technique has been proposed for the detection of inner-race and outer-race bearing faults of the motor. A test bench is developed for experimentation in which easy insertion and removal of the faulty and healthy bearing of an induction motor is possible. A vibration sensor was employed to diagnose the motor faults. A dataset of 3-dimensional vibration signals is acquired from the motor representing inner-race and outer-race bearing faults. Vibrational signal data is pre-processed using empirical mode decomposition (EMD) to decompose the raw signals into its integral components called Intrinsic Mode Functions (IMFs) and to find the region of interest. A new scheme for the selection of discriminated features in bearing fault detection application is presented here in which firstly, Multi-domain features were extracted from pre-processed, and were serially fused. Then, Principle Component Analysis (PCA) technique was employed for the reduction of feature vector dimension which resulted a set of only ten features. The proposed features have positive repercussions on the performance of various existing classifiers used in motor bearing fault detection applications. However, Support Vector Machine with a linear kernel (SVM-L) is proposed to recognize the healthy, inner-race fault and outer-race fault states of the motor bearing due to its linear basis. The results of extensive experimentation appreciate the performance of the proposed technique.

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A novel rolling bearing fault diagnosis method based on marginal spectrum

Tang

2023

Trans. Can. Soc. Mech. Eng.

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Fault diagnosis is an important method for maintaining the stable and safe running state of mechanical equipment. As most mechanical equipment faults are induced by the bearing assembly, bearing fault diagnosis is of considerable importance. At present, the mainstream intelligent diagnostic techniques include supervised learning and unsupervised learning. Supervised learning requires manual labeling and data classification, which is unfavorable for massive data amounts. Therefore, how to effectively use labeled data to increase the accuracy of diagnosis is critical, especially when the bearing failure cannot be labeled at the very beginning. This paper proposes a time–frequency analysis of the short-time Fourier transform and wavelet transform methods based on unsupervised learning. The time axis was integrated to obtain the marginal frequency of two frequency domains as a diagnostic feature, and then two clustering centroids were established automatically by the K-means of unsupervised learning. The signals were divided into two classes based on the nearest clustering centroid as the criteria for diagnosis. Finally, other bearings in different positions were classified and diagnosed using the nearest clustering centroid in the same experiment to verify the effectiveness of the method proposed in this study.

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Machine Bearing Fault Diagnosis System using Tri-Axial Accelerometer

Cited by 3 publications

References 32 publications

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

System Design for the Detection of Induction Motor Faults using Multi-Domain Feature Fusion

A novel rolling bearing fault diagnosis method based on marginal spectrum

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