Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Self-Organizing Maps
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Detection of stator winding fault in induction motor using fuzzy logic
Applied Soft Computing
Bayesian Wavelet-Based Methods for the Detection of Multiple Changes of the Long Memory Parameter
IEEE Transactions on Signal Processing
Fuzzy multivariable Gaussian evolving approach for fault detection and diagnosis
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Expert Systems with Applications: An International Journal
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Hi-index | 0.00 |
In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a two-step formulation. The first step consists of a fuzzy clustering to transform the initial data, with arbitrary distribution, into a new one that can be approximated by a beta distribution. The fuzzy cluster centers are determined by using a Kohonen neural network. The second step consists in using the Metropolis-Hastings algorithm for performing the change point detection in the transformed time series generated by the first step with that known distribution. The incipient faults are detected as long as they characterize change points in such transformed time series. The main contribution of the proposed approach is the enhanced resilience of the new failure detection procedure against false alarms, combined with a good sensitivity that allows the detection of rather small fault signals. Simulation and practical results are presented to illustrate the proposed methodology.