Limited Information Based Non-Additive Fault Detection and Diagnosis for CARIMA Model via Fuzzy Logic

2008 ◽  
Author(s):  
Zhuohuan Hu ◽  
Zhengzhi Han ◽  
Zuohua Tian
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Limited Information ◽  
Detection And Diagnosis

scholarly journals Fault detection and diagnosis of photovoltaic system using fuzzy logic control

2019 ◽  
Vol 107 ◽  
pp. 02001 ◽  
Author(s):  
Sayed A. Zaki ◽  
Honglu Zhu ◽  
Jianxi Yao
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Fuzzy Logic Control ◽  
Fault Detection And Diagnosis ◽  
Rapid Expansion ◽  
Photovoltaic System ◽  
Maintenance Cost ◽  
Pv Array ◽  
Logic Control ◽  
Detection And Diagnosis

Among several renewable energy resources, Solar has great potential to solve the world’s energy problems. With the rapid expansion and installation of PV system worldwide, fault detection and diagnosis has become the most significant issue in order to raise the system efficiency and reduce the maintenance cost as well as repair time. This paper presented a method for monitoring, identifying, and detecting different faults in PV array. This method is built based on comparing the measured electrical parameters with its theoretical parameters in case of normal and faulty conditions of PV array. For this purpose, three ratios of open circuit voltage, current, and voltage are obtained with their associated limits in order to detect eight different faults. Moreover, the fuzzy logic control FLC method is performed for studying the failure configuration and categorizing correctly the different faults occurred. The outcomes obtained by performing the different faults representing permanent and temporary faults demonstrated that the FLC was equipped to precisely identify the faults upon their occurring. Different simulated and experimental tests are conducted to demonstrate the performance of the proposed method.

scholarly journals Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

2014 ◽  
Vol 24 (3) ◽  
pp. 271-287 ◽  
Author(s):  
Pandiyan Manikandan ◽  
Mani Geetha
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
A Priori ◽  
Fault Detection And Diagnosis ◽  
Operator Experience ◽  
Reasoning System ◽  
Tank System ◽  
Detection And Diagnosis ◽  
Fuzzy Logic Theory

Abstract The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

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