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• W2548655899 abstract "Bearing faults are the most common causes of failure also in the case of electrical machines used in automotive applications. Advanced fault diagnosis methods are required to detect these faults in their incipient phase, even in the harsh, vibration-full environment of vehicles. In the paper the effectiveness of a complex bearing faults detection approach is analyzed. It uses the minimum entropy deconvolution (MED) technique to enhance the ability of an autoregressive (AR) model based filtering technique, designed to detect single point defects in rolling bearings. The detection capabilities of the method were studied by means of processing the measured vibration signals for different bearing conditions of an electrical machine. The study included the analysis of the fault detection effectiveness also in the case when additional random noise was added to the measured signals." @default.
• W2548655899 created "2016-11-11" @default.
• W2548655899 creator A5005889719 @default.
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• W2548655899 date "2016-09-01" @default.
• W2548655899 modified "2023-09-26" @default.
• W2548655899 title "Bearing fault detection of electrical machines used in automotive applications" @default.
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• W2548655899 doi "https://doi.org/10.1109/icelmach.2016.7732825" @default.
• W2548655899 hasPublicationYear "2016" @default.
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