Minimum sample size determination of vibration signals in machine learning approach to fault diagnosis using power analysis

Authors:
V. Indira;R. Vasanthakumari;V. Sugumaran
Affiliations:
Department of Mathematics, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India;Department of Mathematics, Villianur College for Women, Villianur, Puducherry, India;Department of Mechatronics Engineering, SRM University, Kattankulathur, Kancheepuram Dist., India
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 1
Cited 1

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Expert Systems with Applications: An International Journal

A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis

Applied Soft Computing

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Abstract

The machine learning approach to fault diagnosis consists of a chain of activities such as data acquisition, feature extraction, feature selection and classification. Each one is equally important in fault diagnosis. As machine learning is a soft science, there is a lot of scope for finding mathematical reasoning which otherwise researchers do it arbitrarily or heuristically. Minimum number of samples required to separate faulty conditions, with statistical stability is one such important factor. This paper provides a method for determination of minimum sample size using power analysis. A typical bearing fault diagnosis problem is taken as a case for illustration and the results are compared with that of entropy-based algorithm (J48) for determining minimum sample size. The results will serve as a guideline for researchers working in fault diagnosis area to choose appropriate sample size.