Fault pattern recognition of rolling bearings based on wavelet packet and support vector machine

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

Download Full-text

Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i2.pp1180-1187 ◽

2021 ◽

Vol 23 (2) ◽

pp. 1180

Author(s):

Firas Saaduldeen Ahmed ◽

Noha Abed-Al-Bary Al-jawady

Keyword(s):

Pattern Recognition ◽

Support Vector Machine ◽

Wavelet Packet ◽

Support Vector ◽

Prosthetic Hand ◽

Hand Gestures ◽

Prosthetic Devices ◽

Prosthetic Hands ◽

Average Accuracy ◽

And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

Download Full-text

A NEW FAULT PATTERN RECOGNITION METHOD BASED ON WPT AND DAGSVM

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026809002631 ◽

2009 ◽

Vol 08 (03) ◽

pp. 345-353 ◽

Cited By ~ 1

Author(s):

GUANG-MING XIAN ◽

BI-QING ZENG

Keyword(s):

Pattern Recognition ◽

Fault Diagnosis ◽

Wavelet Packet ◽

Roller Bearing ◽

Support Vector ◽

Second Phase ◽

Pattern Recognition Method ◽

Recognition Method ◽

Fault Pattern ◽

Model Setup

A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.

Download Full-text

An integrated fault pattern recognition method of satellite control system using kernel principal component analysis and support vector machine

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference ◽

10.1109/cgncc.2014.7007462 ◽

2014 ◽

Cited By ~ 1

Author(s):

Keqiang Xia ◽

Baohua Wang ◽

Ganhua Li

Keyword(s):

Principal Component Analysis ◽

Pattern Recognition ◽

Support Vector Machine ◽

Control System ◽

Principal Component ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Pattern Recognition Method ◽

Recognition Method ◽

Fault Pattern

Download Full-text

Fault Pattern Recognition of Bearing Based on Principal Components Analysis and Support Vector Machine

2009 Second International Conference on Intelligent Computation Technology and Automation ◽

10.1109/icicta.2009.363 ◽

2009 ◽

Cited By ~ 5

Author(s):

Lu Shuang ◽

Yu Fujin

Keyword(s):

Pattern Recognition ◽

Support Vector Machine ◽

Principal Components Analysis ◽

Principal Components ◽

Support Vector ◽

Fault Pattern ◽

Components Analysis

Download Full-text

Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.199-200.927 ◽

2011 ◽

Vol 199-200 ◽

pp. 927-930

Author(s):

Zi Fa Li ◽

Jin Guo Li

Keyword(s):

Pattern Recognition ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Rolling Bearings ◽

Data Set ◽

Vibration Signals ◽

Principle Components Analysis ◽

Components Analysis ◽

Pure Quaternion

A new method had been proposed in this paper of fault diagnosis for rolling bearings based on multichannel vibration signals and QPCA-SVM-based method. The vibration signals were obtained by some multi-sensors with three channels X, Y, Z, that were orthogonal axes. The three orthogonal axes signals were constructed a pure quaternion sequences as samples for processing. The pure quaternion sequences data set was processed by quaternion principle components analysis (QPCA) for feature extraction, and then combined with pattern recognition tools support vector machine (SVM) for classifying some faults patterns. The experimental results indicated its efficiency, and it provided a method for fault diagnosis on multichannel vibration signals.

Download Full-text