Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models Manjeevan Seera a , Chee Peng Lim b,⇑ , Saeid Nahavandi b , Chu Kiong Loo a a Faculty of Computer Science and Information Technology, University of Malaya, Malaysia b Centre for Intelligent Systems Research, Deakin University, Australia article info Keywords: Condition monitoring Induction motor Motor Current Signature Analysis Fuzzy Min–Max neural network Random Forest abstract In this paper, a review on condition monitoring of induction motors is first presented. Then, an ensemble of hybrid intelligent models that is useful for condition monitoring of induction motors is proposed. The review covers two parts, i.e., (i) a total of nine commonly used condition monitoring methods of induc- tion motors; and (ii) intelligent learning models for condition monitoring of induction motors subject to single and multiple input signals. Based on the review findings, the Motor Current Signature Analysis (MCSA) method is selected for this study owing to its online, non-invasive properties and its requirement of only single input source; therefore leading to a cost-effective condition monitoring method. A hybrid intelligent model that consists of the Fuzzy Min–Max (FMM) neural network and the Random Forest (RF) model comprising an ensemble of Classification and Regression Trees is developed. The majority voting scheme is used to combine the predictions produced by the resulting FMM–RF ensemble (or FMM–RFE) members. A benchmark problem is first deployed to evaluate the usefulness of the FMM–RFE model. Then, the model is applied to condition monitoring of induction motors using a set of real data samples. Specifically, the stator current signals of induction motors are obtained using the MCSA method. The sig- nals are processed to produce a set of harmonic-based features for classification using the FMM–RFE model. The experimental results show good performances in both noise-free and noisy environments. More importantly, a set of explanatory rules in the form of a decision tree can be extracted from the FMM–RFE model to justify its predictions. The outcomes ascertain the effectiveness of the proposed FMM–RFE model in undertaking condition monitoring tasks, especially for induction motors, under different environments. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction In the manufacturing environment, condition monitoring is important for machine maintenance, with the aim to safeguard the reliability and efficiency of machinery for production purposes (Venugopal, Wagstaff, & Sharma, 2007). A proper maintenance strategy is important to avoid machine and/or process failures (Cooney, Mann, & Winkless, 2003); therefore minimizing produc- tion cost and time (Portioli-Staudacher & Tantardini, 2012). Traditionally, fault diagnostic techniques in complex machines or processes use either prior knowledge or historical data (Cholette, Liu, Djurdjanovic, & Marko, 2012). However, detecting, locating, and isolating faults can be a challenging task, which is especially true in operations where dependent failures occur (Weber & Wotawa, 2012). In this aspect, the loss of output due to unplanned shutdown caused by machine or process failures cannot be recov- ered without incurring additional cost and time, e.g. wages for workers in overtime periods (Alsyouf, 2007). Besides that, as re- ported in Rockwell Automation (2012), enhancing the capabilities of detecting and monitoring machine faults can lead to reduction of maintenance cost as well as improvement of process uptime by up to 25%. Therefore, condition monitoring has become an inte- gral part in modern production planning and operations. In general, machine maintenance can be in the form of reactive, preventive, or predictive maintenance (Chen, Ding, Jin, & Ceglarek, 2006). The fix-upon-failure strategy is considered as reactive main- tenance, while the pre-planned strategy is related to preventive maintenance. Predictive maintenance, which is also known as con- dition-based maintenance (CBM), adopts the forecasting strategy. Owing to the immense practical implications of CBM, we focus on designing and developing a useful CBM system for induction motors using a hybrid intelligent model in this study. The goal of CBM is to minimize redundant maintenance activities and, at the http://dx.doi.org/10.1016/j.eswa.2014.02.028 0957-4174/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +61 3 5227 3307. E-mail address: chee.lim@deakin.edu.au (C.P. Lim). Expert Systems with Applications 41 (2014) 4891–4903 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa