@inProceedings{
 title = {A simple and efficient method for fault diagnosis using time series data mining},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {Broken rotor bar faults,Fault detection and diagnosis,Fuzzy c-means clustering,Induction motors,Time series data mining},
 volume = {1},
 id = {2ad0c07f-f8b1-3c3d-8083-01d02eea7539},
 created = {2017-04-04T11:31:00.096Z},
 file_attached = {false},
 profile_id = {87d5a801-8629-3fa3-a3f9-81001241fe90},
 last_modified = {2017-04-04T11:31:00.096Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Early detection and diagnosis of incipient faults is desirable for online condition evaluation and improved operational efficiency of induction motors. A classification technique based on time series data mining is developed to detect broken rotor bar faults in induction motors. The proposed algorithm uses only stator phase currents as input without the need for any other signals. The stator phase currents are transformed to park's vector components and a new feature vector is constituted by using these components. The phase space of constituted feature vector is constructed according to determined time delay and embedding dimension for each motor conditions. Each motor condition is separated to two clusters by using fuzzy c-means clustering algorithm. The center points of these clusters are saved for test phase. A Gaussian membership function is used for that a point is the degree of belonging to a cluster. The current signals of a three phase induction motor are derived an actual experimental setup. A healthy induction motor and one, two and three broken rotor bar faults are classified under four different operation speed. Experimental results show the strength of the proposed method. © 2007 IEEE.},
 bibtype = {inProceedings},
 author = {Aydin, I. and Karaköse, M. and Akin, E.},
 booktitle = {Proceedings of IEEE International Electric Machines and Drives Conference, IEMDC 2007}
}

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A classification technique based on time series data mining is developed to detect broken rotor bar faults in induction motors. The proposed algorithm uses only stator phase currents as input without the need for any other signals. The stator phase currents are transformed to park's vector components and a new feature vector is constituted by using these components. The phase space of constituted feature vector is constructed according to determined time delay and embedding dimension for each motor conditions. Each motor condition is separated to two clusters by using fuzzy c-means clustering algorithm. The center points of these clusters are saved for test phase. A Gaussian membership function is used for that a point is the degree of belonging to a cluster. The current signals of a three phase induction motor are derived an actual experimental setup. A healthy induction motor and one, two and three broken rotor bar faults are classified under four different operation speed. 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