<b>DEEP LEARNING BASED BEARING FAULT DETECTION WITH MULTI-RATE SIGNAL PROCESSING FOR DATASET FROM VARIOUS SHAFT ROTATIONAL SPEEDS</b>

<p dir="ltr">This thesis research begins with the bearing fault frequency analysis, leading to the bearing characteristic frequency zones which provide significant information for signal feature extraction. Then, a fusion algorithm is proposed to extract a principal signal from x-, y-, and z- channel sensor recordings with signal enhancement in terms of signal-to-noise power ratio. To tackle the dataset from the different shaft rotational speeds, a multi-rate signal processing technique is proposed to address the challenge of bearing fault detection by regulating the dataset to the reference shaft speed. With the regulated dataset, the one-dimensional frequency spectral features, which become independent of the shaft speed, can be extracted for a one-dimensional deep learning network.</p>