Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Pawar, Prashant M.; Ganguli, Ranjan

doi:10.1016/s0045-7825(03)00237-8

Cited by 98 publications

(45 citation statements)

References 39 publications

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“…The primary advantage of genetic fuzzy over traditional fuzzy system is that when the number of parameters of input and output function of a system increases, GF system provides an easy solution for the model as compared to the fuzzy network which gets plagiarized when complexity increases. This work was validated by Pawar [13] in his work for damage detection of beams and blades of a helicopter rotor. They considered a non-uniform beam and BO-105 hinge less rotor blade of a helicopter for testing purposes.…”

Section: A Genetic-fuzzy Analysismentioning

confidence: 82%

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Thatoi¹

2013

IJET

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Abstract-Detection of cracks using vibration analysis employs different algorithms out of which artificial intelligence (AI) techniques is drawing a lot of attention of the engineers and scientists. This paper presents a comprehensive review of various AI techniques for determining the location, size and depth of cracks in different structures. The techniques mainly consist of fuzzy interference technique, neural network (NN), genetic algorithm (GA), hybrid models and other optimization algorithms.

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Section: A Genetic-fuzzy Analysismentioning

confidence: 82%

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Thatoi¹

2013

IJET

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“…Let N T be the no. of samples to be tested and out of that if the system correctly classifies N C times then success rate of the system for the fuzzy rule as a percentage is given by Eq.4 [16]. By observation of Fig.…”

Section: Resultsmentioning

confidence: 99%

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Mukane¹,

Shaikh²

2013

IJCA

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Surface metrology with image processing is a challenging task having wide application in the industry. Analysis of surface finish can be done using image processing. The proposed system aims at identifying three classes of surface finish images, viz. Casting, Milling and shaping. The system proposes an effective combination of features for analysis of the engineering surfaces. The prototype system developed for classification of surface finish images using Discrete Wavelet Transform (DWT) and Fuzzy Logic classifier. The feature extracted using DWT are Standard Deviation and Mean. The system out performs the earlier methods and gives 92.78% of average success rate for only 72 number of features. The system is also analysed by different wavelet filters for maximum success rate and minimum success rate comparison. General TermsSurface finish identification of metallurgical images, Texture Analysis and Fuzzy logic.

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“…Without preknown model knowledge and complicated designing progress under mathematic theory, the simple model-free controller maybe gain more extensive use. Unfortunately, as other controllers with GA optimization [11][12][13][14][15][16], it lacks instrict mathematic theory support especially convergence stability proof.…”

Section: Experimental Testmentioning

confidence: 99%

“…To achieve better performance and improved robustness, neural networks [8], adaptive learning [9,10], and the genetic algorithm (GA) [11,12] are being used in designing such controllers. A lot of studies proposed that merged techniques provide a more accurate and robust solution than that derived from any single technique.…”

Section: Introductionmentioning

confidence: 99%

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

Mai

Huang

Liao

et al. 2012

Journal of Control Science and Engineering

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A simple model-free controller is presented for solving the nonlinear dynamic control problems. As an example of the problem, a planetary gear-type inverted pendulum (PIP) is discussed. To control the inherently unstable system which requires real-time control responses, the design of a smart and simple controller is made necessary. The model-free controller proposed includes a swing-up controller part and a stabilization controller part; neither controller has any information about the PIP. Since the input/output scaling parameters of the fuzzy controller are highly sensitive, we use genetic algorithm (GA) to obtain the optimal control parameters. The experimental results show the effectiveness and robustness of the present controller.

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Genetic fuzzy system for damage detection in beams and helicopter rotor blades

Cited by 98 publications

References 39 publications

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Identification of Metallurgical Surface Finish Images-In Manufacturing Process using Fuzzy Classifier

Simple Model-Free Controller for the Stabilization of Planetary Inverted Pendulum

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