Neuro-Fuzzy Logic Based Robust Detection and Isolation of Economizer and Air Pre-Heater Faults in Steam Boiler
Authors:   Electronics and Instrumentation Engineering, Easwari Engineering College, Chennai, India, P* Navaseelan , S Nagarajan ,   Electronics and Instrumentation Engineering, Easwari Engineering College, Chennai, India, B Chinthamani ,   Electronics and Instrumentation Engineering, Easwari Engineering College, Chennai, India, B Nagalakshmi ,   Electronics and Instrumentation Engineering, Easwari Engineering College, Chennai, India, B K Chitra ,   Electronics and Instrumentation Engineering, Easwari Engineering College, Chennai
Published: December 2019

The technique of Fault Detection and Isolation (FDI) of Economizer and Air-preheater of Boiler using Neuro-fuzzy system is presented in this paper. FDI using model based approach and intelligent methods are the current trend applied in space industries, process industries and power plants. Intelligent methods like Fuzzy, Neural network and Neuro-fuzzy methods are simpler for modeling and faster for detection and isolation of faults. Here the water wall type steam boiler which is used for producing steam in fertilizer industry is studied. The proposed scheme is detecting and isolating the faults and failures happens in the economizer and air preheater of boiler. The common faults are corrosion, erosion, cracking of boiler tubes at welding points, tube rupturing, scale formation in the tubes, external ash deposits etc. The inherent non-linearity of boiler makes Neuro-fuzzy logic method suitable for FDI for all possible faults. The detection of faults is carried out by computing residuals, which are the differences between real process output and estimated output by neuro-fuzzy logic model. These estimated outputs were obtained from the neuro-fuzzy logic model which is trained using real time data by Adaptive Neuro-fuzzy Inference Systems (ANFIS). The real time data of economizer and air-preheater of boiler is collected and used for residual generation. The residuals will be formed for two outputs which are playing important role. If the residual exceeds threshold value indicates various faults in the boiler components and makes the proposed FDI scheme robust against process and measurement noises, process modeling error, disturbances and all uncertainties etc. The threshold band is calculated using model error model method. To isolate the faults, the residuals are normalized and its magnitudes are compared with present fault severity limits. More the range of severity more will be the magnitude of faults in the boiler. FDI by neuro-fuzzy method is more advantages as it combines the advantage of artificial neural network and fuzzy logic methods. The neural networks are more adaptable and have more learning ability. Fuzzy systems are dealing with human reasoning and decision making. As a result the designed FDI scheme is more sensitive to faults and less sensitive to uncertainties and disturbances etc. makes the scheme robust. The required data and fault knowledge for the research work is collected from BHEL make 55 tons per hour capacity, water tube type boiler available in Madras fertilizer Limited (MFL), Chennai.