Applied Soft Computing 11 (2011) 4203–4211 Contents lists available at ScienceDirect Applied Soft Computing journal homepage: www.elsevier.com/locate/asoc Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) P. Konar, P. Chattopadhyay ∗ Electrical Engineering Department, Bengal Engineering and Science University, Shibpur, Howrah, West Bengal 711103, India article info Article history: Received 1 October 2009 Received in revised form 22 November 2010 Accepted 20 March 2011 Available online 25 March 2011 Keywords: Condition monitoring Induction motor Bearing fault Continuous wavelet transform (CWT) Support Vector Machine (SVM) abstract Condition monitoring of induction motors is a fast emerging technology in the field of electrical equip- ment maintenance and has attracted more and more attention worldwide as the number of unexpected failure of a critical system can be avoided. Keeping this in mind a bearing fault detection scheme of three-phase induction motor has been attempted. In the present study, Support Vector Machine (SVM) is used along with continuous wavelet transform (CWT), an advanced signal-processing tool, to analyze the frame vibrations during start-up. CWT has not been widely applied in the field of condition monitoring although much better results can been obtained compared to the widely used DWT based techniques. The encouraging results obtained from the present analysis is hoped to set up a base for condition monitor- ing technique of induction motor which will be simple, fast and overcome the limitations of traditional data-based models/techniques. © 2011 Elsevier B.V. All rights reserved. 1. Introduction Induction motors known as workhorse of modern industries are subjected to some undesirable stresses during their operating lifetime, causing some faults to develop leading to failures [5,16]. Heavy reliance of industry on these machines in critical applications makes catastrophic motor failures very expensive. Thus, finding an efficient and reliable fault diagnostic technique, especially for induction motors, is extremely important due to widespread use of automation and consequent reduction in direct man–machine interface to supervise the system operation. During the last decade different kinds of data-based models such as Neural Networks (NNs) have established a firm position in condition monitoring of electrical machinery. Vibration analysis has been used in rotating machines fault diag- nosis for decades [2–4,19,22]. In [4], it is claimed that vibration monitoring is the most reliable method of assessing the overall health of rotor system. Each fault in a rotating machine produces vibrations with distinctive characteristics that can be measured and compared with reference ones in order to perform the fault detection and diagnosis. Traditional techniques like Fast Fourier Transform (FFT) used for analysis of the vibration signal is not appropriate to analyze sig- ∗ Corresponding author. Tel.: +91 9231664811. E-mail addresses: pratyaymaithon@gmail.com (P. Konar), paramita chattopadhyay@yahoo.com (P. Chattopadhyay). nals that have a transitory characteristic. Moreover, the analysis is greatly dependent on the machine load and correct identification of very closed fault frequency components requires a very high- resolution data [10]. Wavelet a very powerful signal-processing tool can be used to analyze transients signal and thus eliminating load dependency. Variable window size allows the possibility to extract both low frequency as well as high frequency information as per requirement. Keeping these points in mind the investigation aims to design and develop an on-line monitoring and incipient fault detection scheme of induction motors by assessing the signa- ture of the motor frame vibrations (g frame ) signals during start-up [4,11]. Continuous wavelet transform (CWT) used to extract the local information content of the data has several advantages over the more commonly used DWT [28,29] which uses a set of orthogonal wavelet bases to obtain the most compact representation of the data mainly useful for image compression. The CWT on the other hand uses a set of non-orthogonal wavelet frames to provide highly redundant information that is very good for detection of various types of faults. Wavelet coefficient at each analysis scale can be obtained allowing us to characterize the local information content. Moreover, CWT is easier to interpret since its redundancy tends to reinforce the traits and makes all information more visible which is especially true for very subtle information. Thus, CWT analysis gains in “readability” and in ease of interpretation, what it losses in terms of saving space, which is immaterial in signal process- ing technique where very important distinct informative feature extraction is the most important. 1568-4946/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.asoc.2011.03.014