2011 IEEE International Electric Machines & Drives Conference (IEMDC)
Optimization of the Feedforward Neural Network for Rotor Cage Fault Diagnosis in Three-Phase Induction Motors Z. M. Taïbi, M. Hasni, and S. Hamdani
O. Rahmani, O. Touhami, and R. Ibtiouen
Electrical & Industrial Systems Laboratory-LSEI University of Sciences & Technology Houari Boumediene BP.32 El-Alia, Bab Ezzouar 16111 Algiers, Algeria E-mail:
[email protected]
Laboratoire de Recherche en Electrotechnique Ecole Nationale Polytechnique, BP 182 El Harrach 16200, Algiers, Algeria. E-mail:
[email protected],
their availability. Indeed, the appearance of a fault often leads to irreversible arrest of the induction machine causing therefore a cost of significant repairs to the company (case of the large power machines), without forgetting the caused production loss [1-3]. This paper discusses the fault diagnosis of induction machine using artificial neural networks (ANNs). The study is mainly focused on detecting rotor electric faults. In our experimentation, the machine is supplied by both the power system and an open-loop speed inverter (PMW). To avoid too much instrumentation to the monitored process (adding accelerometers …), the sensors used are those conventional, necessary to control the electrical machine, namely the current and voltage sensors. The diagnosis methodology adopted here is part of artificial intelligence methods (AIs) known as “methods without a priori knowledge or without models”; indeed, this non invasive methods are more preferable because they are based on easily accessible and inexpensive measurements to diagnose the machine conditions without disintegrating the machine structure [4]. Indeed, the Artificial Neural Networks (ANNs) belong to the pattern recognition methods which has faculty to learn the operating processes and to make decisions for data which they never met. A supervised ANNs has been used for fault classification and the proposed one is the multilayer feed forward neural networks because it is one of the most used architectures in the classification, [4-8]. The networks are trained and tested using data base formed from measurement data. Despite the fact that feed forward artificial neural networks (ANNs) have been a hot topic of research for many years there still are certain issues regarding the development of an ANN model, resulting in a lack of absolute guarantee that the model will perform well for the problem at hand. Increased attention is especially directed to propose a systematic way to establish an appropriate architecture in contrast to the current common practice that calls for a repetitive trial-and-error process, which is timeconsuming and produces uncertain results. For this, we adopted a methodology for determining the best architecture based on the use of a genetic algorithm (GA) and the development of novel criteria that quantify an ANN’s performance (both training and generalization) as well as its complexity.
Abstract— We present the results of our investigation in the use of the multi-layer feed forward back-propagation artificial neural networks (ANNs) for induction machine faults diagnosis. ANNs are used effectively to determine the classification of induction machine rotor faults tested at different loads. First the raw signals are collected and features are extracted from the collected signals allowing the development of a data base necessary for the training of the ANNs. However, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. The novelty in our works is that the genetic algorithms (GAs) can be used to select a smaller sub-set of ANN structure features that together form a genetically fit family for successful fault identification and classification tasks, at the same time, an appropriate simple structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined. The proposed methodology is experimentally tested on 4kW/1500rpm induction machines at 50Hz/380V ; the obtained results provide a high level of accuracy. Keywords-artificial neural networks (ANNs); algorithms (GAs); diagnosis; induction machine.
genetic
I. INTRODUCTION The electric machines diagnosis becomes strongly developed in the industrial world today because of the need of obtaining a safe line production, in particular for certain applications. The line productions must be equipped with reliable protective systems because any failure, even most trivial, can lead to inevitable damage. To avoid these problems the researchers worked since several decades to develop diagnosis methods. These are primarily aimed to prevent users of a possible risk that can occur in a particular point of the system. The proposed work focuses on fault diagnosis of threephase squirrel cage induction machines. The growth of this kind of electric machine, mainly due to its simplicity of construction, low cost manufacture, mechanical robustness or its low need of maintenance, is such as we now find it in all the industrial fields in addition to high-tech sectors such as aeronautics, nuclear chemistry or rail transports. These machines require special attention as for their operation and
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II. PROPOSED APPROACH Many works [6, 7, 9 and 10] have been elaborated for the development of intelligent systems. Researches were undertaken in the construction of the ANNs to solve various problems such as the pattern recognition, the forecast, the optimization, the associative memories or control. The genetic algorithms were developed to find optimal solutions in the applications of research and were used in several sectors as artificial intelligence methods optimization [11]. Several methods of GA [11, 12] were presented to solve major problems and to find solutions of a better quality, and they were applied successfully to solve many combinative problems.
Input Layer: Size depends on problem dimensionality. Hidden Layer: A design parameter; must decide on number of layers and size for each layer. Creates a non linear generalized decision boundary. Output Layer: Size depends on number of classification categories. Bias: Further generalizes the decision boundary. Net Activation: Weighted sum of the input values at respective hidden nodes. Activation function: Decides how to categorize the input to a node into a possible node output incorporating the most suitable nonlinearity. Network Learning: Training an untrained network. Several training methods are available. Stopping Criterion: Indicates when to stop the training process; e.g., when a threshold MSE is reached or maximum number of epochs used.
A. Artificial Neural Networks The artificial neural networks are highly connected network of elementary processors running in parallel. Each elementary processor computes a single output based on information it receives. Two main elements constitute an artificial neural network: the neuron model used to build the network and then the network architecture. Each artificial neuron is an elementary processor that receives a number of neural inputs upstream. At each of these inputs has an associated weight representing the strength of connections between neurons corresponding. This puts forward two specific characteristics of each neuron: a “potential” equal to the weights sum of the inputs and an “activation function” which gives the output of the neuron according to its "potential" [13],[14]. The Supervised training is used to determine the synaptic weights from labeled examples associated by an expert with network targets. The network parameters are therefore modified to minimize the error between the target output (provided by the expert) and the actual output of the network [15]. In a feedforward back propagation neural networks structure, the only appropriate connections are between the outputs of each layer and the input of the next layer [16]. A general architecture of feedforward artificial neural networks (FFANN) as well as the component definitions of the ANN are shown in Figure.1.
Figure 1.
The sampling process is performed repeatedly, until finding the best solutions for each subset and then the optimal solution is found. Among the evolutionary techniques, genetic algorithms (GAs) are the group of methods representing the wider application of evolutionary tools. They are based on the use of operators of selection, crossover and mutation. The replacement is usually done by the re-establishments of new individuals. Intuitively the Genetic Algorithm proceeds by producing successive generations of the best and best individuals by applying very simple functions. Research is only guided by the value “fitness” associated to each individual in the population. This value is employed to arrange the individuals according to their relative suitability to the problem in resolution. The main issue is the evaluation function (objective) which is charged to assign a value known as “fitness” for each individual, [18]. For that we left the ideas of ref [7] to adopt it with our work. The problem consists in finding the number of hidden layers and the hidden neurons that are the most critical elements of the feed-forward architecture. The methodology was easily implemented in MATLAB environment. The objective function is prepared by combining each of the four criteria described in ref. [7]. It is the function that returns a value which must be reduced by the GAs and it characterizes the fitness of each FFANN
B.
Genetic Algorithms The development of a feedforward neural networks model poses some problems. In order to understand the problem it is necessary to examine thoroughly the characteristics of a feedforward ANN. There are four elements in feed forward ANN architectures: •
The number of layers,
•
The number of neurons in each layer,
•
The activation functions of each layer,
•
The training algorithm.
A general architecture of feed-forward NN and the component definitions of the ANN [4,17].
ObjVal = FFAC × Solspc × (E generalization + E training ) FFAC = e
Because of the complexity of our problem, the genetic algorithm is used to find the best architecture defined by a set of criteria. The basic idea of the GAs is that the optimal solution will be found in solution space areas containing good solutions and these areas can be identified by robust sampling. Practically, this means that the solution space is divided into subsets that are evaluated to find the best solution.
with
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f (x)
⎧0.001x ⎪0.002x ⎪ f ( x) = ⎨ ⎪0.005x ⎪⎩0.01x
(1) (2)
(3)
Parameter input Signal preprocessing and build the data base
GAs and ANNs user interface
Create random population of hidden neurons number
Return best chromosom YES
No
Process criteria met
Population chromosome 6 Selection
1 2 Replacement Replace old population by the new one according to the fitness
Rank chromosomes for their fitness and regroup them in pair for the evolutionary process
Objective function
5
Decode from genotype to phenotype code
3
Create and train Anns then test them to evaluate their fitness
Covert genotypes to phenotypes
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G.A. operation
Crossover Mutation
Figure 2.
Flow diagram showing all the steps followed in the work.
TABLE I. MAIN PROGRAMMED GA EXECUTION STEP Read the data The user must specify the way of the file base from a working (.mat) containing the experimental data which file (.mat) are automatically read by the application
Solspc : Solution space consistency criterion E generalization : Generalization error.
Etraining : Training error
create the training, validation and of test set
The philosophy of the execution is very simple and is recapitulated in table 1.
Input the GA parameter
III. EXPERIMENTAL SETUP The experimental tests are carried out to extract the stator current signals, stator voltage signals for the four machines by very sensitive Hall Effect sensors connected to an acquisition card IOTEQ / DAQ series 1005 with a maximum sampling frequency of 200 kHz which is connected to a computer. The load used is a DC machine (separated excitation) connected to a resistive load. The signals have been collected under six loads included in the interval [0-80] %. The spectral analysis method applied on the stator current and the phase power uses Welch periodogram on N=100000 points with an overlap of 50% with the Blackman window whose length is N/2 or N (see Figure.4).
define the coding arrangement parameters Set the input and target layer of FFANN adapt the penalty criteria Execute the GA
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The user must write the line ranges that each set of data occupies in the file (.mat) Those include mainly the size of population, the number of generations, the size of offspring and the genetic operators (selection, crossing and change) those include the number of hidden layers and the number of bit for each hidden layer The user must introduce the neuron number for each layer The penalties include the architecture complexity and the result accuracy and the test error of each ANN
frequencies: 50Hz electrical system, 50Hz inverter, and six different loads for four machines studied; what gives (48) signals; thus there is in all (48) feature rows.
The features are extracted from the spectral representation of the current and the phase power and which have a physical significances on the rotor faults, as they are defined in Refs.[19, 20].
Figure 3. The diagnostics system diagram.
The feedforward Neural Network input layer is constituted by a set of 9 extracted features as following: - The slip g - The frequencies and amplitudes of the two components of frequencies Eq.4 in current spectrum [4] .
f = f s (1 ± 2 g )
(4)
-The frequencies and amplitudes of the two components of frequencies Eq.5 in power spectrum [21,22].
f = 2 f s (1 ± 2 g )
(5)
A. Machine slip computation To calculate the slip we adopted two methods according to the current source types mentioned.
Figure 4. Feature extracted from the current and phase power signals.
- Electrical power system source: it remains to determine the rotational frequency fr which has the expression: fr =
f s (1 − g ) p
B.
Standardization of data The data are standardized in the interval [- 1 1], this comes from the choice taken for the activation function of the FFANN layers, for that purpose we used the following function:
(6)
To solve this problem, we choose to seek the value of the characteristic frequency “fs + fr” , [23].
y = ( y max − y min ) ⋅ ( x − xmin ) / ( xmax − xmin ) + y min
- Inverter source: we must use in this case the component created by the rotor cage slots whose frequency have as relation, [21].
⎡ N (1 − g ) ⎤ + 1 ⎥ fs fr = ⎢ r p ⎣ ⎦
(8)
Ymax = 1 ; Xmin : the minimal value of vector X Ymin = -1 ;Xmax : the maximum value of vector X
(7)
C. Machine coding During the training phase of the FFNNs we allotted to each input vector a target as a code in 4 bits representing each machine. [1 -1 -1 -1]T for the healthy machine catches as reference for the diagnosis. [-1 1 -1 -1]T for the machine with one broken rotor bar. [-1 -1 1 -1]T for the machine with two adjacent broken rotor bars, [-1 -1 -1 1]T for the machine with an end-ring portion removed.
Knowing fs and the slip we could then calculate the theoretical values of the defect characteristic frequencies, then we have verified the agreement of these values to the spectrum components via a MATLAB program, and in end we arrive to the constitution of the database extracted from the corresponding signals. The experimental signals include two
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other similar work in the laboratory. We applied the selection criteria for each FFAC function, the best results is presented (see Table II. and Figure.6).
IV. RESULTS The predefined GA program is executed three times for each FFAC function, which gives us 12 architectures; the execution time is estimated at more than 12 hours. To choose the best of the 12 results we used two selection criterions which are cited in priority order:
TABLE II.
Rate of success: this parameter calculates how much correct output corresponding to the target wished is there according to the following algorithm: If the absolute value of the difference between the ith desired value and simulated one is higher than 15% thus the results is considered as erroneous. If not the result is right. The percentage of the right values represents the rate of success, with i=1, 2, 3, 4. Performance: it is the total error of the test vector which is calculated by the performance function of the feedforward Neural Network.
SIMULATION OF THE BEST RESULTS
Function
architect
Obj-val
perform
Accuracy
Execution f=0,001*x
9*5*3*4
0,018
4,05E-04
100%
Execution f=0,01*x
9*3*3*4
0,0248
8,47E-05
100%
Execution f=0,002*x
9*4*3*4
0,0364
6,33E-04
100%
Execution f=0,005*x
9*5*5*4
0,0315
1,46E-04
100%
V. CONCLUSIONS This approach is implemented in software and tested based on the experimental data. • The results show that the GA approach performs better than a human expert, and at the same time offers many advantages by comparison to similar approaches found in literature • The resultes proves that the proposed neural network approach is promising for the machine faults classification in the case where we improve the features extraction methods by introducing the index concept. • We can consider to use and apply the method on other rotor speed frequencies using the speed inverter. Figure 5. Overview of the experimental setup.
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VI. APPENDIX The name plate data of the squirrel cage induction machine are: Rated Power P= 4kW; Rated Voltage Vs =220/380 V (Δ/Y); Rated Current Is =15.2/8.8 A; Rated Speed Nn=1435 (rpm); Pole-pair number p=2; Number of phases m=3; Supply Frequency f=50Hz; cosΦ=0.83; Rotor inertia J=0.025 kg.m2.
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output representation of ANN
target results
first bit
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second bit
-2 2
VII. REFERENCES
0 -2
third bit
2 0 -2 fourth bit
2 0 -2
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Figure 6. Output representation of ANN for the best architecture found.
We have gathered the vectors corresponding to the experimentation done at 50Hz, which gives “48” vectors of data to which we have added “12” other test vectors made in
[5]
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Motor Reliability Working Group of IEEE. Report of large motor reliability survey of industrial and commercial installations. Part. I and II, IEEE Trans. On industry Applications, vol.IA-21, July/August 1985, pp.853-872. H.A. Bonnett. “Root cause AC Motor Failure analysis with a Focus on Shaft Failures”. IEEE Trans. On Industry Applications, vol.36, n°5, pp. 1435 - 1448 ,Sept/Oct. 2000. O. V. Thorsen, M. Dalva, “A survey of Fault in induction motors in offshore oil industry, petrochemical industry, gas terminal and oil refineries”, IEEE Trans. On Industry applications, vol. 31, n°5, pp. 1 - 9 1995. T. Aroui, Y. Koubaa, A. Toumi, "Application of Feed-forward Neural Network for Induction Machine Rotor Faults Diagnostics using Stator Current ", Journal of Electrical Systems (JES), Vol.3, n°4, pp. 213-226, 2007. B. Zhang, J. Yan , W. Zhang, “ Application of Multi-layer Feedforward Neural Network in Fault Diagnosis Based on FBP
[6]
[7]
[8]
[9] [10]
[11] [12]
[13]
[14]
[15] N. Palluat. « Methodologie de surveillance dynamique à l'aide des réseaux Neuro-flous temporels », Doctoral thesis, Automatics, Université de Franche-Comté, jan. 2006. [16] Abhisek Ukil, “Intelligent Systems and Signal Processing in Power Engineering” Springer-Verlag, Berlin Heidelberg, 2007. [17] Abhinav Saxena et Ashraf Saad , “Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems”, Internet soft computing ,Volume 7 (1) ,January 2007,pp : 441454 ,Elsevier Science Publishers B. V. Amsterdam, The Netherlands Sciencedirect ,2005. [18] S.N.Sivanandam et S.N.Deepa , “Introduction to Genetic Algorithms” Springer-Verlag Berlin, Heidelberg 2008. [19] Touhami, O. and Fadel, M. “Detection of broken rotor bars and stator faults in squirrel cage induction machine”. IEEE-IEMDC, vol.1, 3-5 May 2007, pp.821-825, Turkey [20] A.M. da Silva, R. J. Povinelli, and N.A.O. Damerdash. “ Induction Machine Broken Bar and Stator Short-Circuit Fault Diagnostics Based on Three-phase stator Current Envelopes. IEEE Trans. on Industrial Electronics, vol.55, pp.1310-1318, 2008. [21] G. Didier. « Modélisation et diagnostic de la machine asynchrone en présence de défaillances”, Doctoral thesis, Electrical engineering, Université Henri Poincaré, Nancy-I, Oct. 2004. [22] Dulce F. Pires, V. Fern مo Pires J.F. Martins, A.J. Pires, “Rotor cage fault diagnosis in three-phase induction motors based on a current and virtual flux approach”, Energy conversion and management , vol. 50, no4, pp. 1026-1032 Elsevier, Kidlington UK ,2009. [23] O. Ondel. "Diagnostic Par Reconnaissance Des Formes : Application A Un Ensemble Convertisseur Machine Asynchrone" , Doctoral thesis, Electrical engineering, Ecole Centrale De Lyon, Oct. 2006.
Algorithm”, Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 117 - 120 ,2008. SNPD '08. Ninth ACIS International Conference . Fiorenzo Filippetti, Giovanni Franceschini, and Carla Tassoni, “Neural Networks Aided On-Line Diagnostics of Induction Motor Rotor Faults “, IEEE Trans. On Ind. Appl., vol. 31 (4), pp. 892 - 899 july/august 1995. P.G. Benardos, G.-C. Vosniakos “Optimizing feed-forward ANN architecture” , science direct, Engineering application of artificial intelligence, Volume 20 (3), Pergamon Press, Inc. Tarrytown, NY, USA ,April 2007. Arabacı H., “The Detection of Broken Rotor Bars in Squirrel Cage Induction Motors Based on Neural Network Approach”, M.S., Selçuk University Graduate School of Natural and Applied Sciences, Konya,2005. 2005. Vapnik, V. “An overview of statistical learning theory”. IEEE Trans. On Neural Networks, vol.10, n°5, sept.1999, pp.988-999 Abiyev, R.H. and Kaynak, O. “Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants – A Novel structure and a comparative study”. IEEE Trans. on Industrial Electronics, vol. 55, no. 8, August 2008, pp. 3133-3140. Randy L., Haupt and Douglas H., Verner. “Genetic Algorithms in Electromagnetic”, Wiley-IEEE press, 2007. Gang Zhao et Wenjuan Luo, Huiping Nie, Chen Li.”A Genetic Algorithm Balancing Exploration and Exploitation for the Travelling Salesman Problem”, Fourth international Conference on Natural Computation , vol.1 ,2008, pp. 505 - 509 . Ali Zilouchian and Mohammad Jamshidi, “Intelligent control systems using soft computing methodologies”, CRC Press LLC, United States of America,2001. Hung T. Nguyen, Nadipuram R. Prasad, Carol L. Walker and Elbert A. Walker,”A First Course in Fuzzy and Neural Control” Chapman & Hall/CRC, 2003.
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