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 |  موضوع: بحث بعنوان A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals الخميس 27 أكتوبر 2022, 12:37 am | |
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A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals
Muhammad Altaf 1, Tallha Akram 1, Muhammad Attique Khan 2 , Muhammad Iqbal 1,
M Munawwar Iqbal Ch 3 and Ching-Hsien Hsu 4,5,6,*
1 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah 47000, Pakistan;
mohammadaltaf@gmail.com (M.A.); tallha@ciitwah.edu.pk (T.A.); miqbal1976@gmail.com (M.I.)
2 Department of Computer Sciences, HITEC University Taxila, Taxila 47080, Pakistan;
attique.khan@hitecuni.edu.pk
3 Institute of Information Technology, Quaid-i-Azam University, Islamabad 44000, Pakistan; mmic@qau.edu.pk
4 Department of Computer Science and Information Engineering, Asia University, Taichung 400-439, Taiwan
5 Department of Medical Research, China Medical University Hospital, China Medical University,
Taichung 400-439, Taiwan
6 Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology,
School of Mathematics and Big Data, Foshan University, Foshan 528000, China
* Correspondence: chh@cs.ccu.edu.tw
These authors contributed equally to this work.
و المحتوى كما يلي :
Abstract: 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.
Conclusions
In this work, the vibration signals were analysed to detect and classify faults in rotating
machinery. The signal was recorded and its statistical features, such as Average, Kurtosis,
Skewness and RMS, were calculated in the time domain and the frequency domain. These
features were also calculated by first finding the second derivative of the raw time domain
signal. The features were then fed to different machine learning algorithms and were
analysed for different patterns due to different faults and were used to train these machine
learning models, resulting in successful detection and classification into ball, inner race
and outer race faults. The Power Spectral Density features showed the best results for
KLDA, followed by the statistical features using KLDA. This result was compared with
that of the EMD, Fourier Transform and Power Spectral Density, in which the former one
is time-frequency while the latter two are frequency domain representation. It is also
important to note that the sizes of our proposed features are much less than those of the
EMD, Fourier and Power Spectral Density, showing the computational efficiency of our
proposed techniques. The proposed technique can be extended to time-frequency analyses
like Short Term Fourier Transform and Wavelet Transform and so forth; also other bearing
faults can be added, such as cage fault, which is not addressed here.
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