Applied Soft Computing 1 (2001) 73–81 Soft computing methods in motor fault diagnosis X.Z. Gao * , S.J. Ovaska Institute of Intelligent Power Electronics, Helsinki University of Technology, Otakaari 5 A, FIN-02150 Espoo, Finland Received 8 February 2001; received in revised form 26 March 2001; accepted 10 May 2001 Abstract During the last decade, soft computing (computational intelligence) has attracted great interest from different areas of research. In this paper, we give an overview on the recent developments in the emerging field of soft computing-based electric motor fault diagnosis. Several typical fault diagnosis schemes using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms, with descriptive diagrams as well as simplified algorithms are presented. Their advantages and disadvantages are compared and discussed. We conclude that soft computing methods have great potential in dealing with difficult fault detection and diagnosis problems. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Soft computing; Motor fault diagnosis; Neural networks; Fuzzy logic; Genetic algorithms 1. Introduction The ac and dc motors are intensively applied in var- ious industrial applications [1]. Changing working en- vironment and dynamical loading always strain and wear motors and cause incipient faults such as shorted turns, broken bearings, and damaged rotor bars [2]. These faults can result in serious performance degra- dation and eventual system failures, if they are not properly detected and handled. Improved safety and reliability can be achieved with appropriate early fault diagnosis strategies leading to the concept of pre- ventive maintenance. Furthermore, great maintenance costs are saved by applying advanced detection meth- ods to find those developing failures. Motor drive mon- itoring, fault detection and diagnosis are, therefore, very important and challenging topics in the electrical engineering field [3]. * Corresponding author. Tel.: +358-9-451-2434; fax: +358-9-460-224. E-mail addresses: gao@csc.fi (X.Z. Gao), ovaska@ieee.org (S.J. Ovaska). Soft computing is considered as an emerging ap- proach to intelligent computing, which parallels the re- markable ability of the human mind to reason and learn in circumstances with uncertainty and imprecision. In contrast with hard computing methods that only deal with precision, certainty, and rigor, it is effective in ac- quiring imprecise or sub-optimal, but economical and competitive solutions to real-world problems. As we know, qualitative information from practicing opera- tors may play an important role in accurate and robust diagnosis of motor faults at early stages. Therefore, introduction of soft computing to this area can provide us with the unique features of adaptation, flexibility, and embedded linguistic knowledge over conventional schemes [4–6]. An up-to-date presentation of motor fault detection and diagnosis methods was recently published on a special section in [7]. This overview is organized as follows. First, we give a concise introduction to the conventional motor fault diagnosis in Section 2. Soft computing-based approaches, including operating principles, system structures, and computational algorithms, are then discussed in the following sections. We present a few 1568-4946/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved. PII:S1568-4946(01)00008-4