Hybrid Approach Based on ANFIS Models for Intelligent Fault Diagnosis in Industrial Actuator
نویسندگان
چکیده
This paper introduces the application of the hybrid approach Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification and diagnosis in industrial actuator. The ANFIS can be viewed either as a fuzzy inference system, a neural network or fuzzy neural network (FNN). This paper integrates the learning capabilities of neural network to the robustness of fuzzy systems in the sense that fuzzy logic concepts are embedded in the network structure. It also provides a natural framework for combining both numerical information in the form of input/output pairs and linguistic information in the form of if-then rules in a uniform fashion. The proposed algorithm is achieved by the intelligent scheme ANFIS. This intelligent system is used to model the valve actuator and classify the fault types. Computer simulation results are shown in this paper to demonstrate the effectiveness of this approach for modeling the actuator and for classification of faults for different fault conditions. KeywordsNeuro-Fuzzy System; Hybrid Learning; Fault Diagnosis
منابع مشابه
Intelligent Fault Diagnosis in Industrial Actuator based on Neuro-Fuzzy Approach
This paper introduces the application of the hybrid approach Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification and diagnosis in industrial actuator. The ANFIS can be viewed either as a fuzzy inference system, a neural network or fuzzy neural network (FNN). This paper integrates the learning capabilities of neural network to the robustness of fuzzy systems in the sense that ...
متن کاملEVELOPMENT OF ANFIS-PSO, SVR-PSO, AND ANN-PSO HYBRID INTELLIGENT MODELS FOR PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE
Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this ...
متن کاملPredicting Unconfined Compressive Strength of Intact Rock Using New Hybrid Intelligent Models
Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especi...
متن کاملA PSO-trained Adaptive Neuro-fuzzy Inference System for Fault Classification
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 iso...
متن کاملGenetically Tuned Dual-ANFIS Model for Steam Turbine Fault Diagnosis and Treatment
Fault diagnosis of steam turbine is essential to predict further development and to anticipate it by taking appropriate measures. Fault diagnosis of modern industrial power plants by human inspection is time-consuming and expensive as well as fault diagnostic system modelling based on conventional mathematical tools is not suitable for ill defined and uncertain system. Therefore, it is necessar...
متن کامل