Fuzzy Logic–Based Sensor and Control Surface Fault Detection and Reconfiguration

2016 ◽  
pp. 715-736 ◽  
Author(s):  
Ambalal V. Patel ◽  
Shobha R. Savanur
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Control Surface ◽  
And Control

scholarly journals Fuzzy Logic System for Intermixed Biogas and Photovoltaics Measurement and Control

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Liston Matindife ◽  
Zenghui Wang ◽  
Yanxia Sun
Keyword(s):  
Fuzzy Logic ◽  
Fault Detection ◽  
Embedded System ◽  
Controller Design ◽  
Field Studies ◽  
System Optimization ◽  
Generation Process ◽  
Control Methods ◽  
And Control ◽  
Measurement And Control

This study develops a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods on small- to medium-scale systems fall short of comprehensive system optimization and fault diagnosis, hence the need to revisit these control methods. The control strategy developed in this study is intelligent as it is wholly based on fuzzy logic algorithms. Fuzzy logic controllers due to their superior nonlinear problem solving capabilities to classical controllers considerably simplify controller design. The mathematical models that define classical controllers are difficult or impossible to realize in biogas and photovoltaic generation process. A microcontroller centered fuzzy logic measurement and control embedded system is designed and developed on the existing hybrid biogas and photovoltaic installations. The designed system is able to accurately predict digester stability, quantify biogas output, and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control are also successfully implemented. The system is able to optimize the operation and performance of biogas and photovoltaic energy generation.

scholarly journals Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ming-Hung Shu ◽  
Dinh-Chien Dang ◽  
Thanh-Lam Nguyen ◽  
Bi-Min Hsu ◽  
Ngoc-Son Phan
Keyword(s):  
Surface Roughness ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Control Surface ◽  
Process Conditions ◽  
Fuzzy Data ◽  
Size Condition ◽  
Resolution Identity ◽  
And Control ◽  
Control Limits

For sequentially monitoring and controlling average and variability of an online manufacturing process, x¯ and s control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional x¯ and s charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments.

Artificial Neural Networks vs. Fuzzy Logic: Simple Tools to Predict and Control Complex Processes—Application to Plasma Spray Processes

2008 ◽  
Vol 17 (3) ◽  
pp. 365-376 ◽  
Author(s):  
Abdoul-Fatah Kanta ◽  
Ghislain Montavon ◽  
Michel Vardelle ◽  
Marie-Pierre Planche ◽  
Christopher C. Berndt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Plasma Spray ◽  
Complex Processes ◽  
Artificial Neural ◽  
And Control ◽  
Control Complex
Sign in / Sign up

Export Citation Format

Share Document