Soft computing methods in motor fault diagnosis

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 various industrial applications [1]. Changing working environment 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 degradation 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 preventive maintenance. Furthermore, great maintenance costs are saved by applying advanced detection methods to find those developing failures. Motor drive monitoring, 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: [email protected] (X.Z. Gao), [email protected] (S.J. Ovaska).

Soft computing is considered as an emerging approach to intelligent computing, which parallels the remarkable 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 acquiring imprecise or sub-optimal, but economical and competitive solutions to real-world problems. As we know, qualitative information from practicing operators 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

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interesting motor fault diagnosis schemes using soft computing methods, such as neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms (GAs) in Sections 3–6, respectively. Their advantages and disadvantages are also briefly reviewed and compared. Some conclusions are finally drawn at end of the paper.

logic, and GAs (evolutionary computation) [12,13]. In our paper, we discuss the recent progresses of soft computing methods-based motor fault diagnosis. The applications of neural networks, fuzzy logic, and GAs together with their fusion, e.g. neuro-fuzzy, in this motor fault detection and diagnosis area will be presented in the following sections, respectively.

2. Conventional motor fault diagnosis methods

3. Neural networks-based motor fault diagnosis

There exist numerous conventional approaches for motor fault detection and diagnosis [8]. The most straightforward method is the direct inspection. It requires careful check-over of the condition of individual motor components to find defective faults. A similar procedure is named particle analysis of lubricate oil of the motor, if the motor has a gear box with oil lubrication. The oil is first sampled and then taken for laboratory check, which detects the possible faults. This will, however, result in a time consuming and costly examination. The above two approaches are more suitable, on the other hand, for routine maintenance [9,10]. Classical parameter estimation methods can also be reasonably applied for motor fault detection and diagnosis problems [11]. The underlying idea is that based on some measurement signals from the actual motor, we use parameter identification techniques to estimate relevant information of the motor working condition. Fig. 1 illustrates this kind of fault detection process. The parameter estimation strategy is well-suited for real-time cases. Nevertheless, it requires a deep understanding of the operating principle of the motor as well as an accurate mathematical model. In addition, with the aging of the motor, the original model becomes less accurate. During the past few years, soft computing has been employed to overcome the aforementioned difficulties that conventional diagnosis strategies are facing. In general, soft computing methods consist of three essential paradigms: neural networks, fuzzy

Due to their powerful nonlinear function approximation and adaptive learning capabilities, neural networks have drawn great attention in the motor fault diagnosis field. Chow and his colleagues have carried out comprehensive investigation on various neural networks-based fault detection schemes [14–17]. They proposed a typical Back-propagation (BP) neural network structure for incipient motor faults diagnosis, as illustrated in Fig. 2 [17]. The incipient faults here refer to the turn-to-turn insulation and bearing wear in a split-phase squirrel-cage induction motor. In Fig. 2, I is the steady-state current of the stator, ω the rotor speed, and Nc and Bc are the conditions of the motor winding insulation and bearing. From the characteristic equations of an induction motor, we know that the relationships between inputs (I, ω) and outputs (Nc , Bc ) are highly nonlinear. Thus, a BP neural network is applied to approximate this relationship. The

Fig. 2. BP neural network for incipient fault detection.

Fig. 1. Motor fault diagnosis using parameter estimation scheme.

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Fig. 3. Training phase for neural network-based motor fault detection.

training structure is shown in Fig. 3. The values of I and ω can be obtained easily from the on-line measurement data. In fact, the inputs of the BP neural network in Fig. 2 could be further expanded to include higher orders of I and ω, e.g. I2 and ω2 , which would increase the convergence speed [17]. On the other hand, Nc and Bc should be evaluated by a human expert as Fig. 3 shows. More precisely, based on the observation of the working condition and qualitative fault diagnosis knowledge of a training motor, the values of Nc and Bc , which quantitatively describe the motor, are classified into three condition levels, {good, fair, bad}, to yield Nc and Bc , respectively. After the neural network has been trained to learn diagnosis experience from the expert, it is employed on-line as illustrated in Fig. 4. Judging from the motor operating condition, stator current and rotor speed, the neural network can indicate incipient faults according to the above three fault levels. Filippetti et al. proposed a similar BP neural network-based motor fault diagnosis scheme to detect the number of broken rotor bars in [18]. The training data for the neural network is acquired from healthy as well as simulated faulty machines. Their promising scheme has the diagnosis accuracy of 100% in simulations. Besides BP neural network, other unsupervised learning neural networks are also used in motor fault diagnosis [19,20]. Penman and Jin applied the

Kohonen’s self-organizing map (SOM) to classify multiple faults in a 3 kW, 4-pole, 3-phase, cage induction motor [19]. It is well known that different faults, such as stator voltage unbalance, stator interturn short-circuit, and misalignment, may occur simultaneously. They can be separated and detected from their representing clusters in the feature map. An SOM network with 20 nodes in the input layer is set up. The output nodes are arranged into a 20 × 20 grid lattice. Operational data is gathered from an induction machine running with different load levels. Two faulty conditions are considered in their paper: mechanical looseness in the mountings of the motor and unbalanced line voltage supply. In [19], the authors have demonstrated that after about 2000 training cycles, the SOM can successfully separate these two faults by mapping them onto those corresponding output clusters. Unlike BP neural network, this SOM-based approach has the practical advantage of learning and producing fault classifications without any supervision. Therefore, it could be advantageous to utilize SOM in constructing automated motor fault diagnosis systems. In many cases, however, fault data for training the neural network is not easy to collect, because suspicious motors have already been periodically repaired during the routine maintenance before those faults really appear. To solve this problem, Tanaka et al.

Fig. 4. Neural network-based motor fault detection.

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introduced another SOM-based motor fault diagnosis scheme in [20], which can be briefly described as follows. First, a typical SOM network is trained with data from motors working in regular conditions only. Next, the trained SOM is tested using undetected measurement data. Based on the characteristics clusters in the feature mappings, the data will be classified into categories of possible fault and normal data by SOM. Real fault data can then be identified by an expert, and applied for SOM re-training afterwards. In this way, under the feedback supervision of experts, the proposed method can perform efficient fault diagnosis work, where no explicit fault data for learning is available. In [20], it has been examined on a rotating machine to detect incipient faults in the bearings. Interesting performance comparison is also made with the diagnosis approach using BP neural network. Simulations show that in the case of a relatively small range of input signal, SOM can correctly detect fault conditions, while BP neural network fails. From the discussions above, it is concluded that the motivation of employing neural networks for motor fault diagnosis is due to their self-adaptation and nonlinear approximation abilities, which can set up the relationship between the indication of faults and available measurement signals. We emphasize, nevertheless, that the learning procedure is usually guided by human experts. Basically, neural networks-based fault diagnosis is a general purpose solution. No prior knowledge about the motor diagnosis model is needed. Only the training data should be obtained in advance. The inherent drawbacks of neural network learning, such as problem dependent selection of neural network structure, over- and under-training, and slow

convergence speed, may also affect the diagnosis performance. However, the critical shortcoming of neural networks-based motor fault diagnosis is that qualitative and linguistic information from the operator of motors cannot be directly utilized or embedded in the neural networks because of their numerically oriented black-box structures. Additionally, it is even difficult to interpret the input and output mapping of a trained neural network into meaningful fault diagnosis rules.

4. Fuzzy logic-based motor fault diagnosis To take advantage of linguistic fault diagnosis knowledge explicitly, numerous motor fault diagnosis methods using fuzzy logic have been studied [21–23]. In [21], Nejjari and Benbouzid applied fuzzy logic to the diagnosis of induction motor stator and phase conditions. Their diagnosis structure, whose kernel is just a representative fuzzy reasoning system including a fuzzification interface, inference engine, fuzzy rule base, and a defuzzification unit, is illustrated in Fig. 5. Two faults are considered here, i.e. stator voltage unbalance and open phase. The diagnosis procedure is carried out based on the analysis of the amplitude characteristics of stator currents. Three stator current amplitudes Ia , Ib , and Ic are selected as the inputs to the fuzzy fault diagnosis system. Four fuzzy triangular and trapezoidal membership functions, namely, zero, small, medium, and big, are assigned to these inputs. The conditions of the stator and phases are represented with three rectangular membership functions, i.e. good, damaged, and seriously damaged. Totally, there are 12 heuristic IF–THEN fuzzy inference rules

Fig. 5. Fuzzy logic-based motor fault diagnosis.

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applied to detect the two aforementioned faults, for instance 1. IF Ib is small THEN the stator is damaged. 2. IF Ic is medium THEN the stator is in good condition. Generally, there are two basic sources of such fuzzy rules [24]. The first heuristic approach is based on the linguistic experience from practicing motor operators. A typical example includes an interactive interrogation of experts with well-organized questionnaires. The other method is using some self-organizing algorithms [25] to derive fuzzy fault diagnosis rules from measurement signals of healthy and faulty motors. The proposed scheme is verified with experimental data in computer simulations, which reveal that it has a good capability of fault diagnosis. The reported diagnosis accuracy rates for good conditions, bad conditions, and severe conditions are 100, 100, and 94%, respectively. In [22], Filippetti et al. described a broken rotor bar diagnosis system using fuzzy logic in a squirrel cage induction motor. Not only the occurrence but also number of broken bars can be detected in their system. More precisely, there are five kinds of conditions for the rotor bars: no broken bar, an incipient fault, one broken bar, one or two broken bars, and two broken

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bars. It is known that in an induction motor with rotor asymmetry caused by broken rotor bars, the spectrum lines of harmonics are present at (1 ± 2s)f1 , where s is the slip and f1 the stator excitation frequency. The amplitudes of the harmonic components at these two frequencies are denoted here by A1 and A2 in dB. The broken bar fault, therefore, can be detected based on these two variables. A typical fuzzy rule used is as follows: • IF A1 is large and A2 is small THEN one broken bar exists where large and small are predefined fuzzy terms. Note that the possible faults, such as one broken bar, are described by fuzzy membership functions instead of numerical values as in the neural network-based fault diagnosis methods. They are shown in Fig. 6. There is always some overlapping between adjacent membership functions. The above fault diagnosis method has been successfully employed on a 5.5 kW 2-pole induction motor. Goddu et al. proposed another similar fuzzy logic-based motor bearing fault (bearing looseness, defects of the inner raceway, and defects on the rolling element) diagnosis scheme in [23]. The principle is to analyze the frequency spectrum of the bearing

Fig. 6. Membership functions for the broken bar faults (no, no broken bars; incipient, incipient fault; one, one broken bar; one-or-two, one or two broken bars; two, two broken bars).

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vibration signal using fuzzy reasoning rules. As we know, the working condition of bearings is implicitly reflected in its vibration signals. Here, let {xloose , xdamage , xtime } denote the amplitudes of the bearing characteristic signals in both time and frequency domains. More details on how to collect these signals and further calculate the feature vector {xloose , xdamage , xtime } can be found in the same reference paper. Totally, eight fuzzy diagnosis rules are applied in their scheme to detect the bearing damage and loose faults. The following two representative rules are illustrated: 1. IF xloose is high and xtime is medium or high THEN there is a bearing loose fault. 2. IF xdamage is high and xtime is medium THEN the bearing damage fault is severe. This diagnosis approach achieves 91.7% accuracy in detecting ‘severe’ conditions and 100% accuracy at both ‘good’ and ‘bad’ conditions of the bearing. Fuzzy logic-based motor fault diagnosis methods have the advantages of embedded linguistic knowledge and approximate reasoning capability. However, the design of such a system heavily depends on the intuitive experience acquired from practicing operators. The fuzzy membership functions and fuzzy rules cannot be guaranteed to be optimal in any sense. Furthermore, fuzzy logic systems lack the ability of self-learning, which is compulsory in some highly demanding real-time fault diagnosis cases. The above two drawbacks can be partly overcome by the fusion of neural networks and fuzzy logic–neural-fuzzy technique.

5. Motor fault diagnosis using neural-fuzzy technique As we know, both neural networks and fuzzy logic have their own advantages and disadvantages. The major drawbacks of BP neural network are its black-box data processing structure and slow convergence speed. On the other hand, fuzzy logic has a similar inference mechanism to the human brain, while it lacks an effective learning capability. Auto-tuning the fuzzy rules and membership functions may be difficult in a classical fuzzy logic system. In a word, neural networks are regarded as model free numerical approaches, and fuzzy logic only deals with rules

and inference on a linguistic level. Therefore, it is natural to merge neural networks and fuzzy logic into a hybrid system–neural-fuzzy, so that both of them can overcome their individual drawbacks as well as benefit from each other’s merits. In fact, neural-fuzzy technique has found many promising applications in the field of motor fault diagnosis [26–29]. In [28], Filippetti et al. investigated an adaptive network-based fuzzy inference system (ANFIS)-based motor fault diagnosis scheme. Actually, ANFIS is an implementation of a fuzzy logic inference system with the architecture of a five-layer feedforward network [13]. Detailed discussions of the structure and learning algorithm of ANFIS are beyond the scope of our paper. The explored diagnosis problem here is the same as discussed above [22]. The selection of input and output variables is also similar. However, nine Sugeno-type instead of Mamdani-type fuzzy rules are used in ANFIS, for example: • IF A1 is large and A2 is small THEN f = pA1 + qA2 + r where f is the output of the current fuzzy rule, and p, q, and r adaptive parameters, which can be adjusted with ANFIS learning algorithm. Numerical output that indicates the final diagnosis decision, i.e. the number of broken rotor bars, is calculated from individual outputs of these nine rules. Simulations demonstrate that this ANFIS-based motor fault diagnosis method can achieve the equivalent diagnosis performance as the pure fuzzy logic system in [22]. Nevertheless, in ANFIS, fuzzy membership functions and fuzzy rules are obtained ‘automatically’ from the training data instead of by trial and error. This alternative saves considerable efforts, and thus speeds up the initial design procedure. Altug et al. discussed another fuzzy neural network–fuzzy adaptive learning control/decision network (FALCON)-based three-phase induction motor friction fault detection method [29]. Their scheme can provide good diagnosis/detection under varying load torque. Besides the desired diagnosis results, consistent heuristic knowledge in terms of IF–THEN fuzzy rules can be exacted from the trained FALCON. As a matter of fact, rule acquisition is one of the distinguishing features of fuzzy neural networks. In [29], comparison has also been made between FALCON and ANFIS. It is illustrated that diagnosis/detection

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acquired using ANFIS is more accurate than FALCON. Furthermore, the convergence speed of fault detection with ANFIS is faster than with FALCON. However, the extraction of diagnosis knowledge in ANFIS is not as straightforward as in FALCON. As a conclusion, fuzzy neural networks can provide better motor fault detection performance than both neural networks and fuzzy logic, and it is expected that an ideal neural-fuzzy-based fault diagnosis method should combine the fuzzy rule extraction capability of FALCON with the fast training speed of ANFIS. Although fuzzy neural networks own the advantages from both neural networks and fuzzy logic, most of the existing models, such as ANFIS, cannot deal with fuzzy input/output information directly. In [30], Gao and Ovaska proposed a modified ANFIS, which is proven suitable to handle with fuzzy output information in a straightforward way, to overcome this drawback. The modified ANFIS-based motor fault diagnosis scheme is also presented in the same paper. This fault diagnosis scheme has the characteristic of learning the diagnosis knowledge from supervising experts without any information conversion. In addition, the diagnosis results acquired can be easily interpreted in a linguistic way, e.g. ‘some’ and ‘severe’. A bearing fault diagnosis problem is employed as a testbed for this approach. Simulations demonstrated that their method cannot only successfully detect bearing damages faults but also provide a corresponding linguistic description.

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6. Genetic algorithms-based motor fault diagnosis A GA is a derivative-free and stochastic optimization method [31]. Its orientation comes from ideas borrowed from the natural selection as well as evolutionary process. As a general purpose solution to demanding problems, it has the unique features of parallel search and global optimization. In addition, GA needs less prior information about the problems to be solved than the conventional optimization schemes, such as the steepest descent method, which often require the derivative of objective functions. Hence, it is attractive to employ a GA to optimize the parameters and structures of neural networks and fuzzy logic systems instead of using the BP learning algorithm alone. In principle, the training of all the motor fault diagnosis methods discussed above (including both soft computing-based and conventional solutions) can be implemented using GAs. For instance, in [5], Vas introduced GA into the parameter estimation of an induction motor. In [32], Betta et al. discussed the use of GA to optimize a neural network-based induction motor fault diagnosis scheme, which is conceptually illustrated in Fig. 7. Actually, there are two GAs applied for the design and training of the neural network: designer GA and trainer GA. To put it into more details, the design parameters of the neural network, i.e. number and dimension of hidden layers, as well as characteristics of the neuron transfer function, are optimized by the designer GA. Again, the connecting weights inside the

Fig. 7. Application of GA in neural network-based motor fault diagnosis.

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neural network are optimized with the trainer GA. The application of these two GAs in the tuning procedure leads to the optimal neural network structure and parameters for motor fault diagnosis. Particularly, this scheme is implemented on a TMS320C40 DSP chip to accelerate the GA calculation, which results in a diagnosis response time less than 300 ␮s. The diagnosis performance is encouraging: the percentage of correct single-fault detection is higher than 98%. Moreover, it can also cope with double-fault, with correct diagnosis of both faults in about 66% of the considered cases and of at least one fault in about 100% of the cases. In [33], Gao et al. proposed a motor fault detection scheme using Elman neural network with GA-aided training. Different from BP neural network, Elman neural network has a powerful time series prediction capability because of its memory nodes and local recurrent connections. Therefore, it is applied here to give one-step-ahead prediction of the motor feature signals. Faults are detected from remarkable changes in the expectation of prediction error. A GA-based training strategy is further introduced to optimize the initial outputs of the context nodes inside Elman neural network in order to improve its prediction accuracy, and thus achieve better detection performance. Computer simulations of a practical automobile transmission gear with an artificial fault (a tooth cut) are carried out to verify the effectiveness of this method. Accurate fault detection results have been obtained without any prior information of the gear model. Since GA is only an auxiliary optimization method, it cannot be applied independently in practice. The combination of GA with other motor fault diagnosis schemes has demonstrated enhanced performance in global and near-global minimum search. However, optimization with GA often evolves heavy computation, and is therefore quite time-consuming. Targeted at real-time fault diagnosis, fast GAs with parallel implementation to improve the convergence speed have to be developed.

7. Conclusions In this paper, we gave an overview on the recent progresses of soft computing methods-based motor fault diagnosis systems. Several motor fault diagnosis techniques using neural networks, fuzzy logic,

neural-fuzzy, and GAs were concisely summarized. Their advantages and drawbacks were discussed as well. Based on our observations, we conclude that emerging soft computing methods can provide us with improved solutions over classical strategies to challenging motor fault diagnosis problems. However, they are not supposed to compete with conventional methods. Instead, more accurate and robust diagnosis approaches should be developed based on the fusion of these two categories of methodologies, soft computing and hard computing [34]. This overview paper is the starting point for our future research activities in the field of soft computing-based fault diagnosis of electric motors.

Acknowledgements The authors would like to thank the anonymous reviewer for his insightful comments and constructive suggestions that have improved the paper. This research work was funded by the Academy of Finland.

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