Finding Dominant Parameters For Fault Diagnosis Of a Single Bearing System Using Back Propagation Neural Network

Transcription

1 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 40 Finding Dominant Parameters For Fault Diagnosis Of a Single Bearing System Using Back Propagation Neural Network 1 A. S. J. Karib, 2 L. A. Wulandhari, 3 A. Wibowo, 4 H. Haron Department of Computer Science Faculty of Computing Universiti Teknologi Malaysia Skudai, Johor Bahru, Malaysia 1 asyakir5@live.utm.my, 2 awlili2@live.utm.my, 3 antoni@utm.my, 4 habib@utm.my Abstract-- Bearing is a component that affects the performance of a rotating machine in order to be used properly. The purpose of using ball bearing is to reduce rotational friction and support radial and axial loads. Diagnosis of bearing vibration data is an important process to identify either the bearing is normal or defect. In this paper, we consider a single bearing system that consists of one normal or defect Fan End (FE) bearing in which an accelerometer is attached to both normal and defect FE bearing to capture the vibration data. These vibration data are grouped into several samples data. Five parameters are extracted from each sample to be used as the input in neural network model and class of target output is either normal or defect. Those five parameters are standard deviation, skewness, kurtosis, absolute mean and root mean square. Our objectives are to find the dominant parameters and the best model for fault diagnosis of single bearing system. It is important to find the dominant parameters since the selection of dominant parameter can reduce the pre-processing data phase. According to our observation that the dominant parameter is either of standard deviation, absolute mean or root mean square and the best BPNN model is the model with one input neuron and one neuron in hidden layer. Index Term -- Back Propagation Neural Network; Fault Diagnosis; Bearing System; Dominant Parameters. I. INTRODUCTION Ball bearing is an important component in supporting a rotating machine. A bearing must be in good conditions to ensure the machine could work properly [1]. In other words, a bearing in good condition could reduce the percentage of rotator machine failure and the maintenance cost of a machine [1]. Unfortunately, ball bearing is one of the components that are easily broken or damaged compared to the other components such as shaft and rotor [2]. Besides, a damaged bearing may also affect the rotating machine operational performance and there is possibility the machine could not functions at all. Therefore, an early diagnosis should be done to determine the bearing conditions to avoid this situation happened. In this paper, diagnosis is obtained from the experiments and tests to determine the condition of a bearing either normal or defect. Bearing diagnosis have been done by other previous researchers in various field especially in science and technology to find the other initiative or new idea to improve or solve the problems related in their cases. There are fault diagnosis using hybrid of BPNN-Genetic Algorithms [3], fuzzy neural network [4], radial basis function (RBF) [5] and genetic-based neural networks [6]. However, all these studies and researches are almost the same but the difference is those studies and researches are respectively using differential methods to diagnose the bearing system. In this paper, we present a framework based on statistical data extraction and Back Propagation Neural Network (BPNN) to find the dominant parameters and the best model for fault diagnosis of a single bearing system. We consider a bearing system that consists of one normal and defect Fan End (FE) bearing. An accelerometer is attached to both FE bearings to capture the vibration data. It is means that there are two classes of vibration data produced which are normal bearing data and defect bearing data. These data are then divided into several samples data. Five parameters are extracted from each sample to be used as the inputs of BPNN model and only class used as the target output which is either normal or defect. Six BPNN models are developed to determine the dominant parameters. Then we select the best model of BPNN to diagnose the bearing system with the dominant parameters. It is important to determine the dominant parameters to minimize the pre-processing data so that the diagnosis of bearing system is easily executed by using only those dominant parameters. The structure of the bearing system and the example of ball bearing are shown in Figure 1 and 2.

2 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 41 FE Accelerometer FE Bearing where N = 500, k = 1, 2,, N, x ik = the i th data on the k th sample, x k = mean value of the k th sample. Fig. 1. Bearing and accelerometer system structure Fig. 3. Vibration data of normal bearing Fig. 4. Vibration data of defect bearing Vibration Data Fig. 2. The example of ball bearing II. DATA PREPARATION As mentioned in Section 1, there are two classes of vibration data which are normal and defect data. These data are collected from the accelerometer readings attached on the FE bearings. Figure 3 and 4 are the original vibration data of normal and defect bearing. It is difficult to determine a single bearing system whether normal or not using original vibration data. In our experiment, we use of vibration data in total, collected from both normal and defect bearing which means each type of bearing consists of data. Then, the data are divided into 240 samples for normal and another 240 samples for defect. This means, each sample consists of 500 vibration data. Then for our purposes, five parameters are extracted from each sample and used as the input of each BPNN model [7]. These five parameters, which are standard deviation, skewness, kurtosis, absolute mean and root mean square, are frequently used in fault diagnosis because of their effectiveness and practical [5]. The frameworks of finding dominant parameters and model selection are simplified in Figure 5.The followings are the formulae for every parameters. Divide the vibration data into N samples Feature parameters extraction for each sample - p 1 (standard deviation) - p 2 (skewness) - p 3 (kurtosis) - p 4 (absolute mean) - p 5 (root mean square) Model development and feature parameters selection using BPNN. Dominant Parameters and best model Fig. 5. The framework of finding dominant parameters and model selection

3 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 42 Table I shows a few examples of those five parameters. For target output, they are represented in binary forms which are 1 (normal) and 0 (defect). III. FINDING DOMINANT PARAMETERS USING BPNN Neural Network is an information processing paradigm inspired by the way of brain processed the information in human and animal nerve system. The first artificial neuron model in the world was produced by Warren McCulloch and Walter Pits in the year of 1943 [8]. Unfortunately, the technology at that time being has limits their searching. The approach used by Neural Network is the same as human, which is through learning process by showing examples. Back Propagation Neural Network is a method to get a lot of weights in the network. This method is a supervised learning method that always been used to solve the problems in neural networks. Basic algorithm of this method is to minimize the error or network error using the error function description. A. BPNN Model Development and Classification Six BPNN models are built to determine the dominant parameters and diagnose the bearing system. These models are differ in the amount of input layer size and hidden layer size. Choosing the amount of hidden neurons and layers are important to determine the architecture of whole Neural Network models [10]. Eventhough the hidden layer is not interacts with outside directly, but this layer will much affect the ouput result later. Thus, the number of neurons contain in the hidden layer should be emphasized and the selection are properly made. The number of ouput neuron are constant which is one neuron for every each model. The different between each model are the number of input neurons and hidden neurons. The size of input layer should be suitable to avoid underfitting and the number of neurons in a Neural Network model should not be too much to avoid overfitting [11]. Table 2 shows the six models of BPNN that been used in our experiment. As we can see, those models are differ in input layer size, which is input layer size of five neurons and one neuron. Then,the models with five input layer size are also differ in hidden layer size. There are three diferent hidden layer size been used which are one,three and ten neurons in the hidden layer. The same thing goes to the models with one input neuron. Sample Standard Deviation (p 1 ) T ABLE I PARAMETERS VALUE FOR FIVE DATA SAMPLES Skewness (p 2 ) Kurtosis (p 3 ) Absolute Mean (p 4 ) Root Mean Square (p 5 ) Target (t) where T ABLE II BPPN MODELS Model M H M = input layer size, H = number of hidden neurons. For the model with one input neuron, every parameter will be tested one by one so that dominant parameters could be determined. All six models built have one hidden layer. The target output class are constant for every models, which is only one class, either normal or defect. These models are then been tested with those dominant parameters that will be determined to find the best model of BPNN based on lowest CPU operational time. There are five parameters are used as the input of BPNN models. Among these five parameters, there are several of them are dominant. This paper is about how to find those dominant parameters and execute the diagnosis of fault bearing the system. The five parameters are trained, validated

4 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 43 and tested using those six BPNN models. The results of the experiments and tests will be in classification accuracy. The calculation of classificaion accuracy is based on the following equation [3]: Classification = x 100 (6) where total true output class is the number of algorithm output which fill in the correct class and total output is the number of entire algorithm output. B. Results and Analysis The first model that been tested is the model with five input neurons. Based on the results obtained, we could summarized that there are dominant parameters among those five parameters. This parameter could affect the result by giving the highest percentage of classififcation which is almost 100%. Every model are tested ten times and the results shown are the average. All the experiments and tests are executed using Matlab (R2010b) platform. Table 3 shows the result obtained with one, three and ten hidden neurons for five input neurons model. Based on the results obtained above, we could see that the percentage of classification are very high which means there are several dominant parameters among those five parameters. These dominant parameters could affect the result by giving the highest percentage of classification which is 100%. To find those dominant parameters, we need to test those five parameters one by one. The parameters that give classification accuracy percentage exactly and almost 100% are the dominant parameters. The experiment results with one input neuron p 1 (standard deviation), p 2 (skewness), p 3 (kurtosis), p 4 (absolute mean) and p 5 (root mean square), respectively, are shown in Table 4, 5, 6, 7 and 8. Based on the results obtained, we could conclude that p 1 (standard deviation), p 4 (absolute mean) and p 5 (root mean square) are the dominant parameters. This is because all these three parameters are giving the exactly and almost 100% of classification accuracy. artificial intelligence techniques other than BPNN to find the dominant parameters. ACKNOWLEDGEMENT The authors thank to Universiti Teknologi Malaysia (UTM) and Ministry of High Education (MOHE) for Fundamental Research Grant Science (FRGS) Vote No. R.J F084 and the Research Management Center (RMC) for supporting this research project. The first author sincerely thank to UTM for awarding Zamalah scholarship. REFERENCES [1] A. Harnoy, Bearing Design in Machinery : Engineering Tribology and Lubrication. Marcel Dekker, Inc, 2003, pp [2] P. V. J. Rodriguez, A. Arkkio, Detection of Stator Winding Fault in Introduction Motor Using Fuzzy Logic. Applied Soft Computing 8, 2008, pp [3] L. A. Wulandhari, A. Wibowo, M. I. Desa, Hybrid Neural Network- Genetic Algorithm for Fault Diagnosis of Bearing System, Proceeding of Industrial Engineering and Service Science, 2011, pp. 1-6 [4] H. Wang, P. Chen, Fault Diagnosis for A Rolling Bearing Used in A Reciprocating Machine by Adaptive Filtering Technique and Fuzzy Neural Network, WSEAS TRANSACTION on SYSTEMS Issue, Vol. 7, 2009, pp [5] Y. Lei, Z. He, Y. Zi, Application of An Intelligent Classification Method to Mechanical Fault Diagnosis, Expert Systems with Applications 36, 2009, pp [6] Y. C. Huang, C. M. Huang, H. C. Sun, L. S. Liao, Fault Diagnosis Using Hybrid Artificial Intelligent Methods, 5 th IEEE Conference on Industrial Electronics and Applications, 2010, pp [7] W. Li, T. Shi, G. Liao, S. Yang, Feature Extraction and Classification of Gear Faults Using Principal Component Analysis, Journal of Quality in Maintenance Engineering Vol. 9 No. 2, 2003, pp [8] K. Patan, Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Process, Springer, 2008, pp [9] M. H. Hassoun, Fundamentals of Artificial Neural Networks, Asoke K. Ghosh, PHI Learning Private Limited, 2008, pp [10] L. L. Pullum, B. J. Taylor, M. A. Darrah, Guidance for the Verification and of Nueral Networks, John- Wiley & Sons, Inc, 2007, pp [11] B. Liu, H. Pan, Fault Diagnosis of Bearing Using Wavelet Packet Transform and PSO-DV Based Neural Network, Natural Computation 6, 2010, pp IV. CONCLUSION This paper presented the diagnosis of bearing vibration data using Back Propagation Neural Networks. The result shows that the dominant parameter is either of standard deviation (p 1 ), absolute mean (p 4 ) and root mean square (p 5 ). Finding the dominant parameters is important for reducing data prepreparation time. We can just use one of those three dominant parameters to execute the fault diagnosis of bearing to identify either the bearing is normal or not. Meanwhile, the best BPNN model selected is BPNN model with one input neuron and one neuron in hidden layer because of the simplest structure and the ability to complete the network training in most less CPU operational time. This research can be improved by applying two or more bearing systems as an example for the future works. Besides, another researchers may also use different

5 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 44 T ABLE III RESULT FOR FIVE INPUT NEURONS MODEL Number of Hidden Neuron T ABLE IV RESULT FOR ONE INPUT NEURON MODEL (P1 - STANDARD DEVIATION) Number of Hidden Neuron T ABLE V RESULT FOR ONE INPUT NEURON MODEL (P2 - SKEWNESS) Number of Hidden Neuron

6 International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:13 No:01 45 T ABLE VI RESULT FOR ONE INPUT NEURON MODEL (P3 - KURTOSIS) Number of hidden neuron T ABLE VII RESULT FOR ONE INPUT NEURON MODEL (P4 - ABSOLUTE MEAN) Number of hidden neuron T ABLE VIII RESULT FOR ONE INPUT NEURON MODEL (P5 - ROOT MEAN SQUARE) Number of hidden neuron