Neural Approach for Bearing Fault Detection in Three ...

Neural Approach for Bearing Fault Detection in Three Phase Induction Motors W. S. Gongora, H. V. D. Silva, A. Goedtel, W. F. Godoy, S. A. O. da Silva

The techniques of Model Predictive Control (MPC) are investigated since the 70s due to the development of preventive maintenance [6]. The traditional maintenance programs are designed to implement the idea of routine services of all machinery and quick responses to unexpected failures. As the opposite, a predictive maintenance program provides specified maintenance duties only when they are actually needed, minimizing to the maximum unexpected failures. In [7] predictive maintenance is defined as a technique that indicates the actual operating conditions of machines based on data analysis that inform their wear or degradation processes. This technique can be adapted for a non-invasive model indicating the current operating condition of the process based on mechanical, electrical and electromagnetic gathering information. The treatment of these problems in the three phase induction motor has a set of analysis well explored that is related to problems with bearings that compose the machine. This failure specifically, according to [8], can reach more than 40% of problems that occur in electric motors in general. Several methods are applied to the fault detection such as mechanical vibration analysis, stator current frequency spectrum, axial flow and others that are showed by [9]. Basically, any kind of change in the bearings of machines cause excessive vibration, and this vibration can cause, among others, harmonic and changes. The simplest way to detect refers to spectral analysis of the signals obtained from the specific sensor readings coupled to the machine frame at certain points and composed by a combination of acquisitions [10-13]. This procedure is highly reliable considering that each bearing failure has a specific frequency as per treated the aforementioned studies. However, this method has a considered application cost as it requires specific equipment, installation, time of application and qualified manpower in reading and interpretation of the results. Also, valid procedures which evaluate the consequences that these oscillations produce are able to be incorporated into other types of signals removed from the machine. As per [12], which deals with the analysis of electrical current to the signals in the frequency domain or [14], where the procedure is the analysis of the components positive, negative and zero sequence. Intelligent systems have been applied to solving various problems of control and drive machine [15-19]. These proposals do not require specific equipment but only

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Abstract -- The induction motor has been widely used in various industrial applications. Thus, several studies have presented strategies for the diagnosis and prediction of failures in these motor. One strategy used recently is based on intelligent systems, in particular, artificial neural networks. The purpose of this paper is to present an alternative tool to traditional methods for detection of bearing failures using on a perceptron network with signal analysis in time domain. Experimental results are presented to validate the proposal. Index Terms -- Artificial Neural Networks, Failure prediction, Three phase induction motors.

C

I.

INTRODUCTION

urrent technology context allows us to state that the Three Phase Induction Motor (TIM) is the primary means of transformation of electrical into mechanical energy. Allied to its factors of favoritism already consolidated such as robustness and low cost, it may become great part of industrial applications and draw attention to the production of components and accessories allowing a variety of employment and operation. As any electrical machine, it requires proper maintenance, once failures compromise entire production causing large losses at industrial process. In [1] it was estimated that the maintenance costs may be listed 15% to 40% of total production. Based on this approach, some studies present maintenance as an important point to be considered and invested in order to minimize process and improve industrial processes performance. Specific techniques present detection models of a particular type of fault by using specialist system [2-5].

Φ This work was supported by Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná (Process Nr. 06/ 56093-3), Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq (Process Nr. (Process Nr 474290/2008-5, Nr 473576/2011-2 and Nr 552269/2011-5) and Capes-DS. W. S. Gongora is with the Department of Electrical Technician, IFPR Institute, Assis Chateaubriand, PR 85935-000 Brazil ([email protected]). H. V. D. Silva is with the Department of Electrical Engineering, UNOPAR University, Londrina, PR 86041-120 Brazil ([email protected]). A. Goedtel is with the Department of Electrical Engineering, UTFPR University, Cornélio Procópio, PR 86300-000 Brazil (e-mail: [email protected]). W. F. Godoy is with the Department of Electrical Engineering, UTFPR University, Cornélio Procópio, PR 86300-000 Brazil (e-mail: [email protected]). S. A. O. da Silva is with the Department of Electrical Engineering, UTFPR-CP University, Cornélio Procópio, PR 86300-000 Brazil (e-mail: [email protected]).

978-1-4799-0025-1/13/$31.00 ©2013 IEEE 566

requires the data directly from the sensoors. These systems are capable of classifying determine the prresence or absence of faults. The intelligent systems applied to the diagnosis of machines are based on Artificial Neural Networks (ANN), Fuzzy Logic (FL), Hybrid Systems (HS), among others [20fa in electrical 23]. In the strategies for predicting faults machines [22] present a comparison of four structures of neural networks able to detect faults inn the motor with mathematical analysis and manipulation of stator currents. Among other procedures with their specificities, [24] provides an algorithm able to classify how w the two types of motor failures and the severity of the fauult itself, based on the readings of the spectrum of the sttator currents and machine vibration. The purpose of this paper is to pressent a strategy to predict bearing failures in induction motors based on Artificial Neural Networks analysis with signal at time domain, only monitoring the electrical cuurrent in the stator power of an induction motor. This article is organized as follows: Seection 2 presents a description of the major faults in electric motors. Section 3 presents aspects of artificial neural networrks in an overview. In Section 4 the methodology proposedd in this article is presented with experimental results. Finaally, in Section 5, the conclusions of the study are presented. II.

Of the failures reported inn the literature [24-25], it is estimated that the bearings are responsible r for approximately 40% of the stops undesired of the electric motors, as can be followed in Figure 2. t predict motor failures are Some methods described to based on non-invasive strategiees. This is due to the fact that the mechanical failures can be diagnosed by changes in the signals of the electric current of these motors as reported m housing vibration [28[26-27], or by analyzing the motor 11].

Fig. 2. Possibility of occurrence of failuures in induction motors

As an example, Figure 3 (a) shows the normalized currents measured in a motorr of 7.5 CV applied to the heating process of a sugar canne grinding with mechanical problems (bearings). This probblem has been detected by the conventional method of analyysis of mechanical vibration. Even the machine showing such disturbances, still in operation, there was collectionss of data and can be observed in detail that distortion exists beetween the motor currents. After corrective maintainabbility, new measures of both mechanical vibration and cuurrent were taken and no vibrations were noticed. Thus, as showed in Figure 3 (b), it o of the machine by can be inferred the proper operation restoring the standard current signal, s as it was not observed distortions. Figure 3 (c) presentts the motor under analysis in the industrial process.

ASPECTS RELATED TO FAILURES IN ELECTRIC MOTORS

The induction motors, surely the most widely used in m This various productive sectors may present malfunctions. failure can be divided into two major grroups: i) electrical faults and ii) mechanical failures. Figuree 1 shows a block diagram of the main fault types in which electrical e faults are highlighted problems relating to statoor winding, rotor winding, which are present in some models m of motors; broken bars in the rotor, broken rotor rings; r connections among others. Moreover, the mechanicaal failures may be derived from problems of bearings, eccentricity, e wear coupling misalignment among others as repported by [8].

Fig. 3. (a) Current before maintenancce (b) Current after maintenance (c) Motor under analysis.

Fig. 1. Failures classification

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b is the threshold associatedd with the neuron; vj(k) is the weighted responsse (summing junction) of the j-th neuron with respect to the t instant k; ϕj(.) is the activation functioon of the j-th neuron; yj(k) is the output signal of the j-th neuron with respect to the instant k.

Other methods of signal analysis of the motor supply current are based on spectral frequency sppectrum, which are characteristic of each mechanical failure [11-29]. However, problems related with electric powerr quality, which harmonic content is a result of feeding non-linear n loads on the same power supply, can influence thee analysis of these data and be interpreted as mechanicall failures, as per reported in [23]. Thus, in the productive sectors it is obbserved the use of mechanical vibrations analysis or a com mbination of two analyzes: mechanical vibrations and thee spectrum of the supply signal current of these motors.

The adjust of the network weights (wj), associated with the j-th output neuron, is donne by computing of the error signal linked to the k-th iteeration or k-th input vector (training example). This error siignal is provided by:

e j ( k ) = d j ( k ) − y j (k ) (3) where dj(k) is the desired respoonse to the j-th output neuron. Adding all squared errors produuced by the output neurons of the network with respect to k-thh iteration, we have:

III. MODELS OF NEURAL ARTIFICIALL NETWORKS Identification using artificial neural neetwork has shown promise for the solution of a series of problems p involving power systems [30]. More specifically, thhe use of ANN has provided alternative schemes to handling problems p related to electrical machines [22-24]. In this stuudy, ANNs were applied to bearing fault identification in TIIM. For such purpose, a multilayer percepptron network was used, which was trained with a backproppagation algorithm [30]. This training algorithm has two baasic steps: the first one, called propagation, applies values too the ANN inputs and verifies the response signal in its outpuut layer. This value is then compared with the desired signal for that output. The second step occurs in thee reverse way, i.e., from the output to the input layer. The erroor produced by the network is used in the adjustment process of its internal parameters (weights and bias) [30]. The basic element of a neural network is the artificial neuron (Fig. 4), which is also knownn as the node or processing element.

E (k ) =

1 2

p

∑ e 2j (k ) j =1

(4)

where p is the number of outputt neurons. For an optimum weight connfiguration, E(k) is minimized with respect to the synaptic weight wji. Therefore, the weights associated with the ouutput layer of the network are updated using the following relaationship:

w ji (k + 1) ← w ji (k ) − η

∂E (k ) ∂w ji (k )

(5)

where wji is the weight conneecting the j-th neuron of the output layer to i-th neuron of the t previous layer, and η is a constant that determines the t learning rate of the backpropagation algorithm. b to the hidden layers The adjustment of weights belonging of the network is carried outt in an analogous way. The necessary steps for adjusting thhe weights associated with the hidden neurons can be found inn [30]. IV. FAILURE IDENTIFICATION N USING NEURAL NETWORKS The proposed work consistss in the use of stator current signals of an induction motor presented in time domain to an ANN capable to identify the exxistence or absence of bearing failures. Differently from the traditiional methods of mechanical vibration analysis, which require the installation of special sensors at various points of thee machine in order to acquire the vibration signals, the methood proposed in this work uses the data sampled by a digital oscilloscope of four isolated channels TPS 2014 Tecktronixx model with current ferrules A622 100 Amp AC/DC. This unit has a storage cappacity which uses a memory card where the signals are recoorded as a datasheet of 2500 points. The sampling rate is variabble according to the selector sec/div which is adjusted ass a function of the signal displayed on the screen.

Fig. 4. Representation of the artificiaal neuron.

The artificial neuron illustrated in Fig. 4 can be modeled mathematically as follows: n

v j (k ) =

∑ X i .wi + b

(1)

i =1

y j (k ) = ϕ j (v j (k ))

(2)

where: n is the number of input signals of the neuron; n Xi is the i-th input signal of the neuron; wi is the weight associated with the i-thh input signal;

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TABLE II ARTIFICIAL NEURAL NETWORK PARAMETERS

TABL LE III MOTORS USED FOR DATA D ACQUISITION

Type Network 1 Netwoork 2 Network 3 Architecture Perceptron Percepptron MLP Training S S S Nº of layers 3 3 2 Neurons in the 1º 2 2 4 layer Neurons in the 2º 2 layer Training BP BP+L LM BP Algorithm Network function HT HT T LF 1º layer HT Network function 2º layer Output network Linear Lineear Linear function (S) Supervised; (LM) Levenberg Maquaardt; (LF) Logistic Function (HT) Hyperbolic Tangent

Power

Application

12,5 HP 7,5 CV 1 CV

Mill Heating system Laboratory

Bearing (front) 6308 6307 6204

Bearing (back) 6307 6306 6203

CV are used in the first stage Motors of 12,5 HP and 7,5C of sugar cane milling processess and the 1CV motor, is used in the laboratory. The laborratory motor usage aims to integrate signals increasing thhe universe of samples with different motors of different powers. Data related with laboratory motor was collected with no bearing failure, being h distortion. As for the considering a signal with low harmonic motors used in the milling process, data was collected during normal operation at bearing fauult condition. After proper maintenance, byy replacing defective bearings and also cleaning the machine, a further vibration evaluation were done to confirm bearing status, and a new set of data were acquired, assuming that these signals were obtained under normal operating conditioons.

Based on the collected data and withh a proper import routine as per described in Figure 6, these data are handled and evaluated in MATLAB. The signals are separated by a half cyccle and normalized by its peak value to hold then in the time domain disregarding machine scale.

A.

Input Data Treatment The current signals of eachh phase of the various failed motors, were individually coollected by the oscilloscope during normal steady state operation. o Both motors were assessed before maintenance and a again, after replacing the bearings, it was necessary a proocessing data routine and split training and validation sets of thhe networks. The amplitude value of eacch sample point of the three phase stator current of the motors under analysis are presented as the network inputt. This method was proposed by [32] where it is considered as a the input signal a sinusoidal waveform in continuous time. In this application, each half cycle is divided into a numbeer of samples required to be submitted to the network, thuus making the linear signal discretization, as showed in Figgure 7.

Fig. 6. Data processing routine

In a first step, by using traditional methods of mechanical vibration analysis bearings faults were identified in the motors mentioned in Table 3. Thus, signnals of voltage and current were collected at industrial proceess, during normal operation. It should be emphasized that these t machines are connected directly in the line and with no inverter-fed. i

Fig. 7. Input data organization

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TABLE IV DIVISION OF COLLLECTED DATA

The 2500 current signal points obbtained from the oscilloscope with its setting in 10ms/divv, totaling 12 half cycles of wave sampled, each with 208 poiints. In order to simplify the structure of the proposed network, each half cycle is sampled in 50 points that is capable of recreating the sinusoidal signal. o for each half Due to the current delay and analysis occur cycle, it is considered the absolute valuees of the sampled signals only getting positive half cycless of the signal as showed in Figure 7. Another aspect related to the data treeatment is the fact that signals representing the currents of a three-phase machine. Thus, it is necessary to mountt a column vector with points collected each phase of the collected system so as to disregard the delay between the phasees. Each sample is mounted with the 50 points of the m one another, thus subsequent semi cycle of each phase from creating an array of 150 entry points to thee network. When dealing to the use of real signs off electrical currents from machines of different power and different d functional states, it is necessary to perform the norm malization of these data. p the data This normalization also performed by processing algorithm, consider the peak value of the waveform of each sampled signal. Figure 8 shows the normaalized experimental curves. It is worth mentioning here that the commented standardization satisfies this applicationn and performed work. Differentiated values thereof may be b applied to other processes and respond as expected.

Classes Training samples Validation samples B.

(%) 66 33

Samples 26 14

Robustness Test - Noise Signal S In order to carry out the nettwork performance evaluation in front of an interference onn the presented signs, it was inserted into the sampled signnal a noise background. This noise composed of a random vaalue, was considered the worst possible case where its frequenncy is equals to the frequency discretization of the treated signal. Thus the entire value presented to the network inpuut was altered, which can be noticed in Figure 9 (a). Thus, it was developed a rooutine that generates a noise. This routine establishes random values between -1 and 1. These values are attenuated whhen multiplied by a maximum factor of distortion, thus allowing easy modification and verification of the maximum interference value which the network remain immune. Considering that the networkk input is normalized between 0 and 1, when added noise, it suuffers changes proportional to the maximum distortion facctor by which noise was submitted. This can be observedd in Figure 9 (b). The robustness test procedurre was not applied to training step. The insertion of noise was w accomplished only to the network validation data. Thus, obtaining o the maximum value of interference for which do not alter the network classification result comes closse to 3.70% of the normalized value of the input signal.

Fig. 8. Standardized inputs

n only with In order to work with the proposed networks information collected from real appplications without simulation results, sampling vectors were randomly divided into two classes, one for training and one for validation, and A can be observed, they were divided according to Table 4. As 26 samples are used for training the netw work, totaling 66% of the total, and about 33% for validdation, totaling 14 samples. The validation samples are comppared to the results of the proposed topologies.

Fig. 9. Noise input

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V.

other types of faults. This evaluation was not addressed in this paper due to the usage of signals collected in the field. The proposed methodology presents a direct result of the failure classification and does not use specific equipment such as vibration analyzers and others. Data processing is optimized and applicable to any level of model predictive control. For a possible hardware implementation, proposed networks have compact structures facilitating its application in low-cost processors.

EXPERIMENTAL RESULTS

The proposed networks were subjected to training with the same input signals having a learning rate of η= 0.5. As stopping criterion it was established the mean squared error (MSE), defined for each network as showed in Table 5. The Network 1 converged with 74 epochs, while Network 2 has reached the stopping criterion with 38 epochs. As for the Network 3 it was necessary 7453 epochs, however, after concluding the network training this high number of epochs will not impact on the network performance.

VII. [1]

TABLE V FINAL RESULTS

Type Training samples Validation samples AQE Learning Coefficient Epochs Positive false Negative false Classification error Accuracy percentage

Net 1 26 14 1 10-1 0,5 74 5 0 5/14 60,71%

Net 2 26 14 1 10-8 0,5 38 0 0 0/14 100%

Net 3 26 14 1 10-1 0,5 7453 0 0 0/14 100%

[2] [3] [4] [5]

After analyzing the proposed models, it is possible to compare the methods showed in Table 5, whose network architectures are described in Table 2. For Network 1 it was not obtained satisfactory results, with the best performance of 60% for data not presented the network, thus determining a low generalization capacity and infeasibility of its implementation. In the Network 2, which uses Levenberg-Maquardt algorithm, it was achieved 100% of accuracy for the validation data. When compared with Network 1 is also observed a reduction in training epochs and mean squared error. Network 3 shows a larger structure and also a larger number of neurons applied to the processing. This network showed a percentage of 100% accuracy with results as efficient as the Network 2.

[6] [7] [8] [9] [10]

[11] [12]

VI. CONCLUSIONS The purpose of this work is achieved with the test of two different topologies able to classify the existence of bearing failure, by analyzing only the stator current of the ThreePhase Induction Motor in the time domain. Is worth mentioning that the application of such prediction routine can be used in a variety of potencies and also in various operating regimes. Proposed model estimates the severity of the bearing fault since the network was trained to classify such state, similar to the existing process. This is due to the fact that the signals collected, as well as the traditional methods, identifies the existence of bearing faults in accordance with a range of acceptable vibration not determining the degradation of the piece. Other possibility is to consider this same ANN to identify

[13]

[14]

[15]

[16] [17]

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VIII.

Silva, H. V. D. Graduated in Computer Eng. Universidade do Norte do Paraná (2009), Specialization in Automation and Process Control Industrial Technology from the Universidade Tecnológica Federal do Paraná - Campus Cornélio Procópio (2011). He is currently a professor at the Universidade do Norte do Paraná - UNOPAR and masters student in Electrical Engineering from the Universidade Tecnológica Federal do Paraná - Campus Cornélio Procópio Goedtel, A. was born in Arroio do Meio, Brazil, in 1972. He received the B.S. degree in electrical engineering from the Federal University of Rio Grande do Sul, Porto Alegre, Brazil, in 1997, the M.Sc. degree in industrial engineering from the São Paulo State University (UNESP), São Paulo, Brazil, in 2003, and the Ph.D. degree in electrical engineering from the University of São Paulo (USP), São Paulo, in 2007. Currently, he is an Assistant Professor with the Federal Technological University of Paraná, Cornélio Procópio, Brazil. His research interests are within the fields of electrical machinery, intelligent systems, and power electronics. Godoy, W. F. was born in Cornélio Procópio, Brazil, in 1977. He received the B.S. degree in electrical engineering from the University Norte do Paraná, Londrina, Brazil, in 2003, the M.Sc. degree in eletrical engineering from the University of Londrina, Londrina, Brazil, in 2010. Currently, he is an Assistant Professor with the Federal Technological University of Paraná, Cornélio Procópio, Brazil. His research interests are within the fields of electrical machinery, intelligent systems, and electrical maintenance. Silva, S. A. O. was born in Joaquim Távora, Brazil, in 1964. He received the B. S. and M. S. degrees in electrical engineering from Federal University of Santa Catarina, Florianopolis, Brazil, in 1987 and 1989, respectively. He received the Ph.D. degree in electrical engineering from Federal University of Minas Gerais, Belo Horizonte, Brazil, in 2001. Since 1993, he has been with the Electrical Engineering Department in the Federal Technological University of Parana, where he is currently a Professor of Electrical Engineering. His present research involves UPS systems, Photovoltaic systems, Active power filters, Control systems and Power quality.

.BIOGRAPHIES

Gongora, W. S. was born in Cascavel, Brazil in 1984. He received the B.S degree in control and automation engineering from the Faculdade Assis Gurgacz (2007) and the M.Sc. degree at electrical engineering from Universidade Tecnológica Federal do Paraná (2013). He is a professor at the Federal Institute of Paraná (IFPR), campus Assis Chateaubriand. Working mainly on the themes: artificial neural networks, fault prediction, three-phase induction motor.

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