System Design for Early Fault Diagnosis of Machines using Vibration Features

Khan, Muhammad Umar; Imtiaz, Muhammad; Aziz, Sumair; Kareem, Zeeshan; Waseem, Athar; Akram, Muhammad Ammar

doi:10.1109/pgsret.2019.8882726

Cited by 20 publications

(2 citation statements)

References 35 publications

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“…These features were fed to SVM and CNN training algorithms for the classification of faults into the outer race, inner race and ball faults. In another study, Khan et al used EMD with KNN to classify the different machine states into normal, cracking, offset pulley and wear states [40]. In [41], the EMD is combined with deep neural networks to classify the faults into roller, inner and outer races with an accuracy of 98.5%.…”

Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

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In condition based maintenance, different signal processing techniques are used to sense the faults through the vibration and acoustic emission signals, received from the machinery. These signal processing approaches mostly utilise time, frequency, and time-frequency domain analysis. The features obtained are later integrated with the different machine learning techniques to classify the faults into different categories. In this work, different statistical features of vibration signals in time and frequency domains are studied for the detection and localisation of faults in the roller bearings. These are later classified into healthy, outer race fault, inner race fault, and ball fault classes. The statistical features including skewness, kurtosis, average and root mean square values of time domain vibration signals are considered. These features are extracted from the second derivative of the time domain vibration signals and power spectral density of vibration signals. The vibration signal is also converted to the frequency domain and the same features are extracted. All three feature sets are concatenated, creating the time, frequency and spectral power domain feature vectors. These feature vectors are finally fed into the K- nearest neighbour, support vector machine and kernel linear discriminant analysis for the detection and classification of bearing faults. With the proposed method, the reduction percentage of more than 95% percent is achieved, which not only reduces the computational burden but also the classification time. Simulation results show that the signals are classified to achieve an average accuracy of 99.13% using KLDA and 96.64% using KNN classifiers. The results are also compared with the empirical mode decomposition (EMD) features and Fourier transform features without extracting any statistical information, which are two of the most widely used approaches in the literature. To gain a certain level of confidence in the classification results, a detailed statistical analysis is also provided.

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Section: Introductionmentioning

confidence: 99%

A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals

Altaf

Akram

Khan

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…EMD is a versatile time-frequency method suitable for signal analysis tasks where the input signal is non-linear and non-stationary in nature. EMD decomposes the original signal into its time-space components, known as Intrinsic Mode Functions (IMFs) [17]. EMD algorithm splits the original signal into IMFs using an iterative data-driven process known as "sifting".…”

Section: B Preprocessing -Empirical Mode Decompositionmentioning

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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