Sound based induction motor fault diagnosis using Kohonen self-organizing map Emin Germen, Murat Başaran n , Mehmet Fidan Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir, Turkey article info Article history: Received 30 January 2013 Received in revised form 19 June 2013 Accepted 2 December 2013 Available online 31 January 2014 Keywords: Induction motors Fault detection Kohonen SOM Acoustic signal processing abstract The induction motors, which have simple structures and design, are the essential elements of the industry. Their long-lasting utilization in critical processes possibly causes unavoidable mechanical and electrical defects that can deteriorate the production. The early diagnosis of the defects in induction motors is crucial in order to avoid interruption of manufacturing. In this work, the mechanical and the electrical faults which can be observed frequently on the induction motors are classified by means of analysis of the acoustic data of squirrel cage induction motors recorded by using several microphones simultaneously since the true nature of propagation of sound around the running motor provides specific clues about the types of the faults. In order to reveal the traces of the faults, multiple microphones are placed in a hemispherical shape around the motor. Correlation and wavelet-based analyses are applied for extracting necessary features from the recorded data. The features obtained from same types of motors with different kind of faults are used for the classification using the Self-Organizing Maps method. As it is described in this paper, highly motivating results are obtained both on the separation of healthy motor and faulty one and on the classification of fault types. & 2014 Elsevier Ltd. All rights reserved. 1. Introduction Due to their simple construction, cost effectiveness and easy maintenance, the squirrel cage induction motors are the most preferable electrical motors in the industry. In order not to interrupt the industrial processes caused by unexpected failures of induction motors, preventive maintenance strategies are essential. Early diagnostics of incipient faults in induction motors are important to ensure safe operation and help to recognize and fix the problems with low costs and time. Significant amount of research have been focused on the methods for the early detection of the mechanical and electrical faults in induction motors [1]. Among all the methods in literature, motor current signature analysis (MCSA) is one of the most popular ones, which provides an effective way to detect incipient faults. MCSA mainly focuses on the analysis of the current data that is supplied from the ac network to the induction motor with time–frequency analysis techniques like Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform or Wavelet Packet Transform [2]. However there is a bottleneck to apply this technique to induction motors in their working environment since in most cases obtaining data is a cumbersome process because of the additional circuitry like isolators or data acquisition cards and interface that should be added between the supply and the test motors. Also it may not be possible to detach load from motor and run motor under no load condition. In order to get rid of disadvantages of current based techniques like MCSA, the acoustic and Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/ymssp Mechanical Systems and Signal Processing 0888-3270/$ - see front matter & 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ymssp.2013.12.002 n Corresponding author. E-mail addresses: egermen@anadolu.edu.tr (E. Germen), muratb@anadolu.edu.tr (M. Başaran), mfidan@anadolu.edu.tr (M. Fidan). Mechanical Systems and Signal Processing 46 (2014) 45–58