Abstract: Accurate prediction of Remaining Useful Life (RUL) of machines and machine components is very important for reliability evaluation. This paper proposes an Artificial Neural Network (ANN) as a method for accurate prediction of RUL of bearings based on vibration measurements during an accelerated life test. The input features to the neuro-predictor are: the vibration signal in the time domain, the dominant harmonics of the bearing vibration signal expressed in a set of selected coefficients of the discrete cosine transforms (DCT), and the main harmonics of the vibration signal as expressed by Fast Fourier Transform (FFT).The Euclidean distance which is a measure of time to failure based on RMS value is used as the figure of merit for the validation of the ANN. Henceforth; the RUL of the bearing can be predicted as the output of the neuro-predictor. The results prove that the suggested methodology can successfully be applied for prediction of bearing RUL.

• Publication Date: 28-May-2017
• DOI: 10.15224/978-1-63248-123-8-15
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Abstract: A method is presented for the optimization of design and manufacture of spiral bevel, hypoid, and worm gears. The aim of the optimization is to improve the operating characteristics of the gear pair, namely, to increase the service life of gears and to reduce the dynamic effects of gear mesh. Optimal modifications are introduced into the tooth surfaces to simultaneously reduce the maximum tooth contact pressure and transmission errors. These modifications are introduced by applying optimal machine tool settings and geometry of cutting tool in the manufacturing process. The method is demonstrated on spiral bevel, hypoid, and worm gears. The obtained results have shown that significant improvements in the operating characteristics of the gear pairs are achieved.

• Publication Date: 28-May-2017
• DOI: 10.15224/978-1-63248-123-8-16
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