A.D.Dongare, Amit D.Kachare

Transcription

1 Predictive Tool: An Artificial Neural Network Abstract:-This paper presents an investigation of pressure distribution on steel shafts of hydrodynamic journal bearing at variable speed, load, torque & temperature by theoretical, experimental & simulation approach. In the theoretical work, the theoretical calculations of pressure distribution for different sets of speed and load are carrying out by using MATLAB programming. The experimental system is employed on journal bearing test rig at different working speeds, loads, torque and temperature with different surface roughness of bearing for getting pressure distribution. The collected theoretical & experimental data such as pressure distribution is employed as training & testing data for an artificial neural network in order to carry out the simulation. The predictive work consists of the simulations, which helps to make a comparison either with theoretical or with experimental results of pressure distribution as per the will. During ANN implementation, different ANN s are build, because this strategy will allowed for better adjustment of ANN for each specific problem. Back propagation algorithm is used to update the weight of network during training. ANN prediction helps to predict the cases that are not available in the training set. Finally the neural network has been predicted the pressure distribution which is in close agreement with experimental pressure distribution by test rig. Keywords: Hydrodynamic Journal Bearing, Pressure Distribution, Artificial Neural Network, Simulation. I. INTRODUCTION Hydrodynamic Journal Bearing is that bearing in which, the load supporting high pressure fluid film is created due to the shape and relative motion between the two surfaces. The moving surfaces pulls the lubricant into a wedge shaped zone, at a velocity sufficiently high to create the high pressure film necessary to separate the two surfaces against the load i.e. it leads to Hydrodynamic Lubrication. Working of hydrodynamic journal bearing is governed by many parameters such as oil film thickness, pressure distribution, speed, load, viscosity, rheology, temperature etc. The primary reason for measuring pressure distribution is predict the performance of the component. A bearing surface, for instance, requires a level of surface texture that allows lubricant to be retained in small pockets as well as allowing the bearing to roll with minimum friction. If the surface is too rough, wear can quickly develop; however, if the surface is too smooth, inadequate lubrication & seizure might occur. It is impossible to predict the behavior of hydrodynamic journal bearing used in practical applications. Also it is not possible to predict the behavior of hydrodynamic journal bearing in journal bearing test rig set up for different operating conditions which are beyond the working range of set up. Technique of Artificial Neural Network can be used to predict the behavior of hydrodynamic journal bearing at extreme operating conditions which can avoid sudden failure of A.D.Dongare, Amit D.Kachare 209 hydrodynamic journal bearing. Technique of Artificial Neural Network needs theoretical and experimental data of above mentioned parameters for training and testing the neural network by feeding it teaching patterns and letting it change its weights according to some learning rule. II. LITERATURE REVIEW C. Sinanoglu, A.O. Kurban and S. Yildirim investigated the pressure variations on the steel shafts on the journal bearing system with low temperature and variable speed. This mainly consists of two parts, experimental and simulation. In the experimental work, journal bearing system is tested with different shaft speeds and temperature conditions. The temperature of the system s working conditions was under minus. The collected experimental data such as pressure variations are employed as training and testing data for an artificial neural network. The neural network is a feed forward three layered network. Quick propagation algorithm is used to update the weight of the network during the training. Finally neural network predictor has superior performance for modeling journal bearing systems with load disturbance. [1] C. Sinanoglu, A.O. Kurban and S. Yildirim investigated that journal bearings form great majority of hydrodynamic bearings used in the industry. Although considerable research has been carried out, both analytical and experimental, to study the behaviour of journal bearings, an accurate prediction of their performance under operating conditions is difficult to obtain (Koc and Kurban, 1996). In journal bearings, the load supporting hydrodynamic pressure is very important. In order to create narrowing oil wedge mechanism, several mechanisms have been put forward to explain the formation of load supporting pressure area (Kurban and Sinanog lu, 2000). [2] Many researchers have demonstrated that the hydrodynamic load carrying capability should be achieved by some mechanisms such as wedge mechanism (Chen et al., 2002). They described in typical studies on the thermal effects in journal bearings. They also investigated the geometry, system pressure, velocity of the journal bearing and the oil film. In their results, roughness and thermal effects become equally important when the load and speed were increased. The hydrodynamic lubrication theory of rough surfaces on shafts has been studied with considerable interest in recent years. This is mainly because, all bearing surfaces are rough to some extent and generally the roughness asperity height is of the same order as the mean separation between the lubricated contacts. Under such conditions, surface roughness of the bearings considerably affects its performance. Most of the investigators in this area have confined their work to solid bearings. However, the study of surface roughness has a

2 greater importance in the study of porous bearings as the surface roughness is inherent to the process used in their manufacture. Gururajan and Prakash (2002) developed a stochastic model to study the surface roughness effects in porous bearings. Their investigation results were compared with the approximate solutions, and the range of various influencing parameters, for which the approximate analysis was satisfactory from a practical point of view. Gururajan and Prakash (1999) investigated the effect of surface roughness in an infinitely long porous journal bearing operating under steady conditions. Further, Gururajan and Prakash (2000) also studied the effect of surface roughness on short porous journal bearing. These studies have predicted a considerable influence of surface roughness on the performance of porous bearings. Another interesting observation made by these authors is that the magnitude of the influence of surface roughness depends on the roughness type and the type of the bearing geometry. All the investigations mentioned above were confined to the study of surface roughness on porous bearings with Newtonian fluids as lubricant. Some researchers studied the effect of surface roughness in hydrodynamic lubrication of a porous journal bearing with couple stress fluid as lubricant (Nadunivamani et al., 2002). It was assumed that the roughness asperity heights were small compared to the film thickness. It was observed that the effect of surface roughness on the bearing characteristics was more pronounced for couple stress fluids as compared to the Newtonian fluids. The presence of polar additives in the lubricant caused enhancement of the load-carrying capacity and reduction in the coefficient of friction as well as in the attitude angle. As a result, the performance of the bearing system was improved. [3] III. SPECIFICATIONS OF HYDRODYNAMIC JOURNAL BEARING Diameter of journal (dj) : mm Following graphs shows the theoretical values of pressure distribution at various speeds. Graph 1 Plot of Pressure Distribution at a Speed of 800rpm & Load of 150N Length of bearing (lb) Radial clearance (c) : 40 mm : mm c/rj : lb/dj : 1 Journal speeds : 800,900 rpm Load : 150 N IV. THEORETICAL PRESSURE DISTRIBUTION Theoretically pressure distribution is determined by using MATLAB programs. Equation Used, P = (1) Graph 2 Plot of Pressure Distribution at a Speed Of 900rpm & Load Of 150N V. EXPERIMENTAL PRESSURE DISTRIBUTION A. Experimental Set Up The Journal Bearing Test Rig TR-60 is used to demonstrate the pressure distribution in a lubricant under load condition. It also measures frictional torque. 210

3 connected to the controller, which displays the values of the angular position of pressure sensor with reference to the load line and the corresponding pressure values. Normal load, rotational speed can be varied to suit the test conditions. Frictional torque value can also be displayed on the controller. Data obtained are transmitted to PC through data acquisition cable. [4] Following graphs shows the experimental values of pressure distribution at various speeds. Fig. 1 Jurnal Bearing Test Rig 60 This is a sturdy versatile machine, which facilitates study of pressure at corresponding angular position of the pressure sensor with the load line. The JBTR equipment consists of a vertically mounted shaft and driven by a variable speed motor. A metallic bellow connects brass bearing at bottom and top is fixed to frictional torque load cell. Bearing made of brass material encloses the shaft at the lower end and is immersed in an oil sump. An rpm sensor disc is mounted on the driven pulley to measure the revolution of the shaft per minute. A stepper motor moves the bearing in the direction of the rotation of the shaft unto 180 o in steps of 9 o. Graph 3 Plot of Pressure Distribution at a Speed of 800 Rpm and Load of 150 N A pressure sensor is fixed on the bearing, which measures the film pressure distributed in the oil film. Radial load is applied by dead weights through a lever mechanism. The assembly of the shaft and the bearing is immersed in oil (i. e. SAE20W40) so as to provide continuous lubrication at all times. The equipment is Graph 4 Plot of Pressure Distribution at a Speed of 900 Rpm and Load of 150 N VI. ARTIFICIAL NEURAL NETWORK An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems such as brain, process information. ANN consists of multiple layers of simple processing elements called neurons. These are computer based neural networks since these are tend to mimic the natural neural networks. An artificial neural network executes in cycles, where whole network of neurons is stimulus at any time, resulting in parts of network seeming to separate out into sub networks. Artificial neural networks are usually designed to perform a specific task due to constraints imposed by power of computer on which the network is running. Modern neural networks are a long way behind their biological counter parts. [5] VII. BIOLOGICAL INSPIRATION Human brain is made up of a network of neurons that are coupled with receptors and effectors. Receptors are called dendrites and effectors are called axons.[6]each axon splits into thousands of separate connections, which interface with other neurons or other tissues such as the muscle, at a point called synapse as depicted in Fig.2. Cell body or Soma is that part of 211

4 neuron where processing of inputs takes place. Neuron operates by propagating an electrochemical signal from dendrites to axons, if the input signal is above certain threshold. Neuron s ability to change response depending on input signal is above certain threshold. Neuron s ability to change response depending on inputs enables it to take part in the learning activity seen throughout the brain.[7] IX. RESULTS Following Figures are the result of simulation between experimental pressure distribution & ANN prediction for pressure distribution. Fig. 2 Biological Neuron VIII. FEED FORWARD NETWORKS This is a subclass of acrylic networks in which a connection is allowed from a node in layer i only to nodes in layer i+1 as shown in Fig.3.These networks are succinctly described by a sequence of numbers indicating the number of nodes in each layer. For instance, the network shown in Fig.3 is a feed forward network; it contains three nodes in the input layer (layer 0), two nodes in the first hidden layer (layer 1), three nodes in the second hidden layer (layer 2), and two nodes in the output layer (layer 3). [8] These networks, generally with no more than four such layers, are among the most common neural nets in use, so much so that some users identify the phrase neural networks to mean only feed forward networks. Conceptually, nodes in successively higher layers abstract successively higher level features from preceding layers. In the literature on neural networks, the term feed forward has been used sometimes to refer to layered or acrylic networks. [9] Fig.4 ANN Results For Speed Of 800rpm & Load Of 150N Above Fig.4 shows the experimental & simulation results of pressure distribution for speed of 800 rpm & load of 150N. This Fig. 4 is the result of multiple inputs to neural network. In this Fig.4 the non linear map with circular points indicate the experimental pressure distribution while the non linear map without points indicate the prediction of ANN. So,it is observed that the Artificial Neural Network predictor follows the experimental results in close aggreement. Fig. 3 Feed Forward Networks Fig. 5 ANN Results For Speed Of 900rpm & Load Of 150N The Fig. 5 shows the representation of experimental & Artificial Neural Network approaches. In this Fig.5 the non linear map with circular points indicate the experimental pressure distribution while the non linear map without points indicate the prediction of ANN. The 212

5 results in the Artificial Neural Network s & test data targets (experimental) are in good agreement. Therefore it is observed that the ANN exactly follows the desired results. X. CONCLUSION In this research work, ANN was designed to analyze the pressure distribution on journal bearing system. The values of pressure distribution were measured for different working parameters (i.e. speeds & loads) from the experimental set up. Then ANN s would be employed to predict the pressure distribution of the journal bearing system. From the experimental & simulation results, neural network exactly followed the desired results. Therefore, ANN s will be used to predict & model such systems in practical applications. Due to simulation with experimental values of pressure distribution, ANN finally predicts the behavior of hydrodynamic journal bearing at extreme operating conditions which can avoid sudden failure of hydrodynamic journal bearing. ACKNOWLEDGEMENT The authors would like to acknowledge & thanks to Prof. R.R. Kharde, Asso. Prof. & Head, Dept. of Mechanical Engineering, P.R.E.C. Loni Dr. Y.R. Kharde, Principal, Shree. Saibaba Institute of Engineering Research & Allied Sciences, Rahata, Prof. S.B. Belkar, Asso. Prof., P.R.E.C. Loni & Prof. R.R. Navthar, Asstt. Prof., P.D.V.V.P. COE Ahmednagar for their immense help in this work. NOMENCLATURE c: Radial clearance, mm e: Eccentricity, mm ε :Eccentricity ratio D : Diameter of bearing, mm d j :Diameter of journal, mm lb :Length of journal bearing, mm N j : Rotational speed of journal, rpm P: Bearing pressure, kpa p :Pressure on fluid film, kpa W: Load, N µ: Dynamic viscosity, N-s/sq. m S: Summerfield Number REFERENCES [1] C. Sinanoglu, A.O. Kurban and S. Yildirim, Analysis of pressure variations on journal bearing system using artificial neural networks, Industrial Lubrication and Tribology, (2004), Volume 56, No. 2, Page no [2] Fazil Canbulut, Cem Sinanoglu and Sahin Yildirim, Neural network analysis of leakage oil quantity in the design of partially hydrostatic slipper bearings, Industrial Lubrication and Tribology, (2004), Volume 56, No. 4, Page no [3] M. Ananda Rao & J. Srinivas, Dynamics of rotors supported on fluid film bearings using neural networks, Centre for System Dynamics & Condition Monitoring & Diagnostic Studies, Page no [4] Instruction Manual, Ducom Instruments Pvt. Ltd., Bangalore, Page no & [5] R.R.Navthar & Dr. N.V. Halegowda, Applications of Artificial Neural Network in Pressure Distribution Analysis of Hydrodynamic Journal Bearings, ASME Digital Library, ebooks, International Conference on Computer Technology & Development (2011), (Third ICCTD 2011), Page no [6] R.R.Navthar & Dr. N.V. Halegowda, Analysis of oil film thickness in Hydrodynamic Journal Bearing using Artificial Neural Networks, Ciit International Journal of Artificial Intelligent System & Machine Learning (Nov. 2011), Volume 3, No.12, Page no [7] Dr. R. R. Srikanth. Application of ANN in Condition Monitoring: A Case Study, (Conference proceeding Condition Monitoring of Mechanical System ) (2009), Gitam University, Vishakhapattanam, Page no [8] Kishan Mehrotra, Chilkuri K. Mohan, Sanjay Ranka, Elements of Artificial Neural Networks, Penram International Publishing (India), (1997), Volume 1, Page no [9] R.R.Navthar & Dr. N.V. Halegowda, Pressure Distribution Analysis of Hydrodynamic Journal Bearings using Artificial Neural Network, ASME Digital Library, e-books, International Conference on Computer & Automation Engineering, (Fourth ICCAE 2012) Page no [10] A.O. Kurban, Analysis of shafts surface pressures using neural network, Industrial Lubrication and Tribology, (2004), Volume 56, No. 4, Page no [11] Ghorbanian, M. Ahmadi, R. Soltani, Design predictive tool & optimization of journal bearing using neural network model & multi objective genetic algorithm, Scientia Iranica B (2011), Volume 18, No.5 Page no [12] Neural networks: A requirement for intelligent systems. [13] Carlos Gershenson, Artificial Neural Network for Beginners. [14] Chen, M.D., Chang, K.M., Lin, J.W. and Li, W.L., Lubrication of journal bearings influence of stress jump condition at the porous-media/fluid film interface, Tribology International, (2002), Volume 35, Page no [15] Gururajan, K. and Prakash, J., Roughness effects in a narrow porous journal bearing with arbitrary porous wall thickness, International Journal of Mechanical `Sciences, (2002), Volume 44, Page no [16] Turaga, R., Sekhar, A.S. and Majumdar, B.C., The effects of roughness parameter on the performance of hydrodynamic journal bearings with rough surfaces, Tribology International, (1999), Volume 32, Page no [17] Yildirim, S. and Uzmay, Neural network applications to vehicle s vibration analysis, I., Mechanism and Machine Theory, (2003), Volume 38, Page no [18] Qingchang, T., Wei, L. and Bo, L., Investigations on a permanent magnetic-hydrodynamic hybrid journal 213

6 bearing, Tribology International, (2002), Volume 35, Page no AUTHOR S PROFILE Prof.A.D. Dongare M.E. (Design Engg), Ph.D.(App) P.R.E.C. Loni Rahata Ahmednagar. Area of Research: Design and Tribology. Professional Membership: IE (I). A.D. Kachare B.E. (Mechanical), P.G. Student. P.R.E.C. Loni Rahata Ahmednagar. Area of Research: Design. Professional Membership: IE (I). 214