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Research Article

Study on Fault Diagnosis of Turbine Using an Improved Cosine Similarity Measure for Vague Sets

L.L. Shi and J. Ye

Aiming at complexity and uncertainty of relation between vibration and fault types of turbine, a new method of fault diagnosis of turbine was proposed based on an improved vague cosine similarity measure. Compared with the previous cosine similarity measures for vague sets, the improved cosine similarity measure has more information to deal with vagueness and uncertainty problems by considering truth-membership functions, false-membership functions and hesitancy degree of vague sets and then it can overcome the undefined problem when the degree of membership and degree of non-membership are zero, respectively. Then, the cosine similarity measure was applied to fault diagnosis of turbine. For this fault diagnosis, the matter-element models of the turbine fault were built according to diagnostics derived from specialists’ knowledge of practical experience and then through the vague cosine similarity measure between a fault-testing sample and fault knowledge samples, the vibration fault is determined according to the maximum cosine similarity measure value. The fault-diagnosis example of the turbine shows that the proposed method is simple and effective.

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  How to cite this article:

L.L. Shi and J. Ye, 2013. Study on Fault Diagnosis of Turbine Using an Improved Cosine Similarity Measure for Vague Sets. Journal of Applied Sciences, 13: 1781-1786.

DOI: 10.3923/jas.2013.1781.1786


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