NDLI: Fault diagnosis of rotating machinery based on a new hybrid clustering algorithm

Content Provider	SpringerLink
Author	Lei, Yaguo He, Zhengjia Zi, Yanyang Hu, Qiao
Copyright Year	2006
Abstract	A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature weights are learnt via FFNN based on the gradient descent technique, and sample weights are computed by using the distribution density function of a sample. Feature weighting and sample weighting highlight the importance of sensitive features and representative samples, and simultaneously weaken the interference of insensitive features and vague samples. The presented algorithm is described and applied to the incipient fault diagnosis of locomotive roller bearings. The diagnosis result demonstrates the superior effectiveness and practicability of the algorithm, and shows that it is a promising approach to the fault diagnosis of rotating machinery.
Starting Page	968
Ending Page	977
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	35
Issue Number	9-10
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-11-08
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Feature weighting Sample weighting Cluster validity index Hybrid clustering Fault diagnosis Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Industrial and Manufacturing Engineering Mechanical Engineering Computer Science Applications Software

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