Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms

On-line monitoring of manufacturing process is extremely important in modern manufacturing for plant safety, maximization of the production and consistency of the product quality (Song et al., 2003). The development of diagnostic systems for the industrial applications has started in early 1970s. The recent developments in the microelectronics have increased their intelligence and let them found many industrial applications in last two decades (Mendonca et al., 2009; Shi & Sepehri, 2004). The intelligent data analysis techniques are one of the most important components of the fault diagnosis methods (Uppal et al, 2002; Uppal & Patton, 2002). In this study, the faults of a pneumatic system will be monitored by using the artificial neural networks (ANN). When the speed control and magnitude of the applied force is not critical, pneumatic systems are the first choice. They are cheap, easy to maintain, safe, clean, and components are commercially available. They have even been used for precise control of industrial systems (Nazir & Shaoping, 2009; Ning & Bone, 2005). Unfortunately, their nonlinear properties and some limitations at their damping, stiffness and bandwidth characteristics avoid their widespread applications (Belforte et al., 2004; Tsai & Huang, 2008, Bone & Ning, 2007; Taghizadeh et al., 2009; Takosoglu et al., 2009). The interest for the development of diagnostic methods for pneumatic and hydraulic systems has increased in the last decade (Nakutis & Kaskonas, 2008). Researchers concentrated on the detection of the faults of the components. The condition of the pneumatic and hydraulic cylinders (Wang et al., 2004), and digitally controlled valves (Karpenko et al., 2003) were the main focus of the studies. Some of the other considered faults were leakage of the seals (Nakutis & Kaskonas, 2005, 2007; Yang, 2006; Sepasi & Sassani, 2010), friction increase (Wang et al., 2004; Nogami et al., 1995) and other

[1]  Z. Nakutis,et al.  Pneumatic Cylinder Diagnostics using Classification Methods , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[2]  Stephen Grossberg,et al.  Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..

[3]  Changzheng Chen,et al.  A method for intelligent fault diagnosis of rotating machinery , 2004, Digit. Signal Process..

[4]  Giuliana Mattiazzo,et al.  A method for increasing the dynamic performance of pneumatic servosystems with digital valves , 2004 .

[5]  Joseph McGhee,et al.  Neural networks applied for the identification and fault diagnosis of process valves and actuators , 1997 .

[6]  Farrokh Sassani,et al.  On-line fault diagnosis of hydraulic systems using Unscented Kalman Filter , 2010 .

[7]  Tao Han,et al.  ART–KOHONEN neural network for fault diagnosis of rotating machinery , 2004 .

[8]  Ibrahim N. Tansel,et al.  Fault diagnosis of pneumatic systems with artificial neural network algorithms , 2009, Expert Syst. Appl..

[9]  Chu Na,et al.  Pattern recognition based on weighted and supervised ART2 , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[10]  Shu Ning,et al.  Experimental Comparison of Position Tracking Control Algorithms for Pneumatic Cylinder Actuators , 2007, IEEE/ASME Transactions on Mechatronics.

[11]  An-Chyau Huang,et al.  Multiple-surface sliding controller design for pneumatic servo systems , 2008 .

[12]  Nariman Sepehri,et al.  Diagnosis of process valve actuator faults using a multilayer neural network , 2003 .

[13]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[14]  Lipo Wang,et al.  Speech word recognition with backpropagation and fuzzy-artmap neural networks , 1995, IEA/AIE '95.

[15]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Ali Ghaffari,et al.  Improving dynamic performances of PWM-driven servo-pneumatic systems via a novel pneumatic circuit. , 2009, ISA transactions.

[17]  Gibaek Lee,et al.  Process Monitoring of an Electro-Pneumatic Valve Actuator Using Kernel Principal Component Analysis , 2004 .

[18]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[19]  Ibrahim N. Tansel,et al.  Selection of optimum cutting condition of cobalt-based superalloy with GONNS , 2010 .

[20]  Shu Ning,et al.  Development of a nonlinear dynamic model for a servo pneumatic positioning system , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[21]  S. Grossberg,et al.  ART 2: self-organization of stable category recognition codes for analog input patterns. , 1987, Applied optics.

[22]  Yoshihide Yokoi,et al.  Failure diagnosis system on pneumatic control valves by neural network , 1993, IEEE International Conference on Neural Networks.

[23]  Ibrahim N. Tansel,et al.  Determining Initial Design Parameters by Using Genetically Optimized Neural Network Systems , 2009 .

[24]  Kyung Youn Kim,et al.  Model‐based fault detection and isolation method using ART2 neural network , 2003, Int. J. Intell. Syst..

[25]  Patrick S. K. Chua,et al.  A study of hydraulic seal integrity , 2007 .

[26]  Wenxian Yang,et al.  Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming , 2006 .

[27]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[28]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[29]  Stephen Grossberg,et al.  ART 2-A: An adaptive resonance algorithm for rapid category learning and recognition , 1991, Neural Networks.

[30]  Ronald J. Patton,et al.  Fault diagnosis of an electro-pneumatic valve actuator using neural networks with fuzzy capabilities , 2002, ESANN.

[31]  Kenji Suzuki,et al.  Artificial Neural Networks - Industrial and Control Engineering Applications , 2011 .

[32]  N. Sepehri,et al.  Adaptive fuzzy-neural-based multiple models for fault diagnosis of a pneumatic actuator , 2004, Proceedings of the 2004 American Control Conference.

[33]  João Miguel da Costa Sousa,et al.  An architecture for fault detection and isolation based on fuzzy methods , 2009, Expert Syst. Appl..

[34]  Imin Kao,et al.  Analytical fault detection and diagnosis (FDD) for pneumatic systems in robotics and manufacturing automation , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[36]  Arun K. Samantaray,et al.  Fault Detection and Isolation of Smart Actuators Using Bond Graphs and External Models , 2005 .

[37]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[38]  D. Devaraj,et al.  Artificial neural network approach for fault detection in rotary system , 2008, Appl. Soft Comput..

[39]  J. Wang,et al.  Identification of pneumatic cylinder friction parameters using genetic algorithms , 2004, IEEE/ASME Transactions on Mechatronics.

[40]  Wang Shaoping,et al.  Optimization Based on Convergence Velocity and Reliability for Hydraulic Servo System , 2009 .

[41]  Jakub Emanuel Takosoglu,et al.  Rapid prototyping of fuzzy controller pneumatic servo-system , 2009 .

[42]  Vasile Palade,et al.  NEURO-FUZZY BASED FAULT DIAGNOSIS APPLIED TO AN ELECTRO-PNEUMATIC VALVE , 2002 .