A PSO-trained Adaptive Neuro-fuzzy Inference System for Fault Classification

نویسندگان

  • Haris M. Khalid
  • Syed Z. Rizvi
  • Lahouari Cheded
  • Rajamani Doraiswami
  • Ammar Khoukhi
چکیده

When a fault occurs during an industrial inspection, workmen have to manually find the location and type of the fault in order to remove it. It is often difficult to accurately find the location and type of fault. Hence, development of an offline intelligent fault diagnosis system for process control industry is of great importance since successful detection of fault is a precursor to fault isolation using corrective actions. This paper presents a novel hybrid Particle Swarm Optimization (PSO) and Subtractive Clustering (SC) based Neuro-Fuzzy Inference System (ANFIS) designed for fault detection. The proposed model uses the PSO algorithm to find optimal parameters for (SC) based ANFIS training. The developed PSO-SC-ANFIS scheme provides critical information about the presence or absence of a fault. The proposed scheme is evaluated on a laboratory scale benchmark two-tank process. Leakage fault is detected and results are presented at the end of the paper showing successful diagnosis of most incipient faults when subjected to a fresh set of data.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Voting Algorithm Based on Adaptive Neuro Fuzzy Inference System for Fault Tolerant Systems

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

متن کامل

Voting Algorithm Based on Adaptive Neuro Fuzzy Inference System for Fault Tolerant Systems

some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weight...

متن کامل

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM OPTIMIZATION USING PSO FOR PREDICTING SEDIMENT TRANSPORT IN SEWERS

The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Ada...

متن کامل

Prediction of Weld Strength in Resistance Spot Welded Samples by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Resistance Spot Welding (RSW) is one of the effective manufacturing processes used widely for joining sheet metals. Prediction of weld strength of welded samples has great importance in manufacturing and different methods are used by researchers to find the fracture force. In this article, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for prediction of joint strength in welded s...

متن کامل

Prediction of Weld Strength in Resistance Spot Welded Samples by Adaptive Neuro-Fuzzy Inference System (ANFIS)

Resistance Spot Welding (RSW) is one of the effective manufacturing processes used widely for joining sheet metals. Prediction of weld strength of welded samples has great importance in manufacturing and different methods are used by researchers to find the fracture force. In this article, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized for prediction of joint strength in welded s...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010