Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models
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 production 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 recovered without incurring additional cost and time, e.g. wages for workers in overtime periods (Alsyouf, 2007). Besides that, as reported 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 integral 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 maintenance, while the pre-planned strategy is related to preventive maintenance. Predictive maintenance, which is also known as condition-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 same time, prevent machine failures (Camci & Chinnam, 2010). As stated in Zhou et al. (2012), an established CBM method is able to avoid non-essential maintenance tasks and to reduce the maintenance cost. As a result, subject to an accurate forecasting technique, CBM offers practical benefits in terms of cost (as compared with reactive maintenance) and time (as compared with preventive maintenance) in machine maintenance. In this aspect, a combination of different intelligent models can be used in CBM to devise a robust forecasting technique and ensure a high predictive accuracy (Camci & Chinnam, 2010). As such, the use of CBM can help increase the availability and reliability of machines for production operations, which is of practical importance in the manufacturing industry.
In production facilities, induction motors are widely used in many processes, e.g. manufacturing machines, belt conveyors, cranes, lifts, compressors, trolleys, electric vehicles, pumps, and fans (Montanari, Peresada, Rossi, & Tilli, 2007). Owing to numerous advantages of induction motors, which include high reliability, high performance, and simple design (Almeida, 2006), they are used in many critical applications where the motor reliability must be at a high level (Ayhan, Trussell, Chow, & Song, 2008). Indeed, as reported in Almeida (2006) and by Commission of the European Communities (2009), three-phase induction motors make up 87% of the total AC motors used in Europe. While induction motors are the workhorses in a lot of production processes, the running cost of induction motors actually greatly exceeds their initial purchase prices (Nagornyy, Wallace, & Jouanne, 2004). Therefore, it is vital to minimize the running cost of induction motors. One useful way is to employ an effective condition monitoring system so that unexpected induction motor failures can be minimized (Siddique, Yadava, & Singh, 2005); therefore reducing maintenance costs as well as unscheduled downtimes (Martins, Pires, & Amaral, 2011). As such, the main motivation of this research is to design and develop a highly reliable intelligent model for condition monitoring of induction motors.
In the CBM domain, intelligent learning models have been applied to tackle many different problems. These include monitoring of hydrostatic self-levitating bearings using a feedforward neural network (Garcia, Rolle, Gomez, & Catoira, 2012), monitoring of water and wastewater facilities using intelligent networks (Davis, Sullivan, Marlow, & Marney, 2012), monitoring of nuclear power plant reactor cores using intelligent systems (West, McArthur, & Towle, 2012), and monitoring of an aircraft air conditioning system using decision trees and a genetic algorithm (Gerdes, 2013). Other successful CBM applications include fault diagnosis of the Tennessee Eastman process using a hidden Markov model (Li, Fang, & Xia, 2014) and fault detection in industrial plants using the self-organizing map network (Domínguez et al., 2012). Besides that, intelligent learning models are useful for monitoring machine conditions through various sensor measurements, ranging from common malfunctions to rare emergency situations (Nadakatti, Ramachandra, & Santosh Kumar, 2008). From the literature, it can be concluded that neural networks with learning capabilities are useful models for tackling CBM problems (Tallam, Habetler, & Harley, 2003). They possess a number of advantages, such as the capability of learning from data samples, and the learning procedure does not require an exact mathematical model.
Among different neural network-based models, the Fuzzy Min–Max (FMM) network is designed specifically for solving data classification (Simpson, 1992) and data clustering (Simpson, 1993) problems. FMM is a hybrid model of neural network and fuzzy system. It inherits the advantages of both its constituents, i.e., the learning capabilities based on data samples (from neural networks) and the inference capabilities based on vague and imprecise information (from fuzzy systems). Besides that, FMM possesses several salient features for tackling data classification problems (Simpson, 1992), which include online learning, short learning time, and establishment of nonlinear decision boundaries. However, one of the key FMM limitations is its inability to provide explanation for its predictions. This is known as the black-box phenomenon (Kolman & Margaliot, 2005) – a problem suffered by many neural network models. One effective way to solve this black-box phenomenon is through rule extraction. In this aspect, decision trees offer a good rule extraction solution (Mitra, Konwar, & Pal, 2002). In particular, the Classification and Regression Tree (CART) is useful for handling large and noisy data samples (Breiman, Friedman, Olshen, & Stone, 1984) while the Random Forest (RF) model is beneficial for improving the performance of a learning model using an ensemble technique (Verikas, Gelzinis, & Bacauskiene, 2011). As reported in Park and Lee (2013), the ensemble technique is useful to improve the performance of constituent classifiers and/or predictors. Therefore, FMM is combined with an RF ensemble (RFE) comprising multiple CART decision trees to form a hybrid intelligent model known as FMM–RFE in this study.
The main contributions of this study are two-fold: a review of different condition monitoring methods for induction motors and a case study to demonstrate the applicability of the proposed FMM–RFE model using real data sets. It is worth mentioning that the case study covers two significant aspects pertaining to condition monitoring of induction motors. Firstly, we examine efficacy of FMM–RFE in monitoring multiple incipient faults from induction motors using information from only one source (i.e., stator currents) in both noise-free and noisy environments. The use of single input source leads to a cost-effective condition monitoring system. It should be noted that not many reports pertaining to monitoring multiple induction motor faults using information from only one source are available in the literature, owing to complexity of the task. Secondly, the ability of FMM–RFE to explain its prediction to domain users with a decision tree is another important aspect of this study. Again, it should be noted that the explanatory facility is absent from many condition monitoring systems reported in the literature (as explained in Section 3).
The organization of this paper is as follows. A total of nine common methods for condition monitoring of induction motors are explained in Section 2. They are compared in terms of the online/offline and invasive/non-invasive characteristics. Then, a review on condition monitoring of induction motors using intelligent learning models is presented in Section 3. The hybrid FMM–RFE model is described in detail in Section 4. To evaluate the effectiveness of FMM–RFE, a benchmark study is conducted, and the results are compared with those from other methods in the literature, as reported in Section 5. In Section 6, the applicability of FMM–RFE to condition monitoring of induction motor is evaluated empirically using real data sets. Concluding remarks and suggestions for further work are presented in Section 7.
Section snippets
Condition monitoring methods for induction motors
In condition monitoring, the role of intelligent sensors and sensor-based systems is important (Teti, Jemielniak, O’Donnell, & Dornfeld, 2010). Different sensing methods are applicable to condition monitoring of electrical motors in two ways: offline or online. On one hand, offline methods often require motor operations to be disturbed, or shutdown. On the other hand, online methods provide warnings of motor failures in advance. As such, the necessary replacement parts can be prepared before a
Intelligent systems for condition monitoring of induction motors
A survey of intelligent learning models for condition monitoring of induction motors is presented in the following section. A number of key input sources of induction motors, i.e., current signals, vibration signals, and combination of multiple input signals, are covered.
The hybrid intelligent model
In our previous work (Seera & Lim, 2014), an initial investigation on combining FMM, CART, and RF has been fruitful, and the resulting hybrid model has been shown to be useful in tackling medical data classification problems. Leveraging on the previous findings, we further improve the initial model and formulate the proposed FMM–RFE model in this study. A number of useful modifications are introduced in FMM–RFE in order to ensure that its constituents are integrated efficiently. Table 2 shows
A benchmark problem
Before conducting the evaluation using real induction motors, a benchmark study related to the steel plate faults was first carried out. The purpose of the benchmark study was to compare FMM–RFE with its constituents as well as other models in the literature. Obtained from the UCI Machine Learning Repository (Bache & Lichman, 2013), the steel plate data set comprised 1941 samples, each with 27 features. The outputs comprised 7 types of faults, i.e., pastry, z-scratch, k-scratch, stains,
Condition monitoring of induction motors
In this study, we propose a hybrid intelligent model, namely FMM–RFE, for condition monitoring of induction motors using the MCSA method. The strengths and weaknesses of the proposed approach are analyzed, as follows. FMM–RFE possesses a number of advantages, which include its online learning and rule extraction capabilities. These two capabilities are important to tackle condition monitoring problems with comprehensible and convincing predictions (through rule extraction) in changing
Conclusions
In this paper, a review on condition monitoring of induction motors has been presented. A case study pertaining to condition monitoring of induction motors using the FMM–RFE model has also been demonstrated. The usefulness of FMM–RFE is first evaluated using a benchmark problem. The results indicate its effectiveness as compared with those reported in the literature. A series of real experiments on condition monitoring of induction motors is conducted. The MCSA method is deployed to obtain
Acknowledgement
This research is supported partially by UMRG Research Subprogram (Project Number RP003D-13ICT).
References (95)
The role of maintenance in improving companies’ productivity and profitability
International Journal of Production Economics
(2007)- et al.
Monitoring industrial processes with SOM-based dissimilarity maps
Expert Systems with Applications
(2012) - et al.
Mixed-fault diagnosis in induction motors considering varying load and broken bars location
Energy Conversion and Management
(2010) - et al.
Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: A review
Mechatronics
(2009) Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning
Expert Systems with Applications
(2013)- et al.
A new approach to intelligent fault diagnosis of rotating machinery
Expert Systems with Applications
(2008) - et al.
Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis
Expert Systems with Applications
(2014) - et al.
Machinery fault diagnosis based on fuzzy measure and fuzzy integral data fusion techniques
Mechanical Systems and Signal Processing
(2009) - et al.
Induction motor fault detection and diagnosis using a current state space pattern recognition
Pattern Recognition Letters
(2011) - et al.
Rotor fault condition monitoring techniques for squirrel-cage induction machine—a review
Mechanical Systems and Signal Processing
(2011)
Software diversity: Practical statistics for its measurement and exploitation
Information and Software Technology
Eigenvector/eigenvalue analysis of a 3D current referential fault detection and diagnosis of an induction motor
Energy Conversion and Management
A modified fuzzy min–max neural network with rule extraction and its application to fault detection and classification
Applied Soft Computing
A simplified scheme for induction motor condition monitoring
Mechanical Systems and Signal Processing
Dynamic eccentricity in squirrel cage induction motors – simulation and analytical study of its spectral signatures on stator–currents
Simulation Modelling Practice and Theory
A validated model for the prediction of rotor bar failure in squirrel-cage motors using instantaneous angular speed
Mechanical Systems and Signal Processing
A hybrid intelligent system for medical data classification
Expert Systems with Applications
Detection of stator winding faults in induction motors using three-phase current monitoring
ISA Transactions
Vibration behavior of stators of electrical machines, Part II: Experimental study
Journal of Sound and Vibration
A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings
Tribology International
Advanced monitoring of machining operations
CIRP Annals-Manufacturing Technology
Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference
Expert Systems with Applications
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Industrial implementation of intelligent system techniques for nuclear power plant condition monitoring
Expert Systems with Applications
Wavelet support vector machine for induction machine fault diagnosis based on transient current
Expert Systems with Applications
A novel fault diagnosis method of bearing based on improved fuzzy ARTMAP and modified distance discriminant technique
Expert Systems with Applications
Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base
Expert Systems with Applications
Introduction to machine learning
Application of AI tools in fault diagnosis of electrical machines and drives-an overview
IEEE Transactions Energy Conversion
Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors
IEEE Transactions Energy Conversion
On the use of a lower sampling rate for broken rotor bar detection with DTFT and AR-based spectrum methods
IEEE Transactions Industrial Electronics
An effective neural approach for the automatic location of stator interturn faults in induction motor
IEEE Transactions on Industrial Electronics
Health-state estimation and prognostics in machining processes
IEEE Transactions Automation Science and Engineering
Vibration and current monitoring for detecting air gap eccentricity in large induction motors
IEE Proceedings Electrical Power Applications
Rule extraction: From neural architecture to symbolic representation
Connection Science
Integration of process-oriented tolerancing and maintenance planning in design of multistation manufacturing processes
IEEE Transactions Automation Science and Engineering
Monitoring of complex systems of interacting dynamic systems
Applied Intelligence
Case study applying the TRIZ methodology to machine maintenance
TRIZ Journal
Fault detection techniques for induction motors
IEEE Compatibility Power Electronics
A selection framework for infrastructure condition monitoring technologies in water and wastewater networks
Expert Systems with Applications
Approximate statistical tests for comparing supervised classification learning algorithms
Neural Computation
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
Machine Learning
Cited by (116)
Multi-input CNN based vibro-acoustic fusion for accurate fault diagnosis of induction motor
2023, Engineering Applications of Artificial IntelligenceClassification of motor faults based on transmission coefficient and reflection coefficient of omni-directional antenna using DCNN
2022, Expert Systems with ApplicationsCitation Excerpt :As per the EPRI (Electric Power Research Institute) reports (Naha, Samanta, Routray, & Deb, 2016), 41% of the induction motor failure is caused by bearing defects, 9% by rotor failures and 36% by stator failures. A condition monitoring and fault diagnosis system (Cai et al., 2021; Seera, Lim, Nahavandi, & Loo, 2014; Tran, Yang, Oh, & Tan, 2009) is necessary as it is easier to maintain the induction motor and replace the faults, rather than allowing the faulty motor to shut down the operation. For safe and efficient operation of the rotating machinery, proper maintenance and condition monitoring is necessary.
Importance of condition monitoring in mechanical domain
2022, Materials Today: ProceedingsPredictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
2021, Reliability Engineering and System SafetyCitation Excerpt :In electric vehicles, induction motors are widely used. In this context, Seera et al. [132] developed a hybrid condition monitoring model that consists of an ensemble of a fuzzy min–max ANN and a random forest. They examined the efficacy of their model in monitoring multiple incipient faults from induction motors using information from only one source (i.e., stator currents) in both noise-free and noisy environments.
Selection parameters and synthesis of multi-input converters for electric vehicles: An overview
2021, Renewable and Sustainable Energy Reviews