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 & Kaškonas, 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 & Kaškonas, 2005, 2007; Yang, 2006; Sepasi & Sassani, 2010), friction increase (Wang et al., 2004; Nogami et al., 1995) and other