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Fault detection of ball bearings by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in ball bearings involve significant nonlinear dynamical properties. The quality of chaotic vibration of ball bearings is studied by the reconstructed phase space. The phase space demonstrates different chaotic vibration of ball bearing for different healthy/faulty conditions. But, to easily use of this procedure in the ball bearing fault detection, the chaotic behavior of vibration signal is quantified by a set of new features. The new set of features based on chaotic behavior, including the largest Lyapunov exponent, approximate entropy and correlation dimension are extracted to acquire more fault characteristic information. The effectiveness of the new features based on chaotic vibrations in the ball bearing fault detection is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.

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Today, fast and accurate fault detection is one of the major concerns in the industry. Although many advanced algorithms have been implemented in the past decade for this purpose, they were very complicated or did not provide the desired results. Hence, in this paper, we have proposed an emerging method for deep groove ball bearing fault diagnosis and classification. In the first step, the vibration test signals, related to the normal and faulty bearings have been used for both of the drive-end and fan-end bearings of an electrical motor. After that, we have employed the one dimensional Meyer wavelet transform for signal processing in the frequency domain. Hence, the unique coefficients for each kind of fault were extracted and directed to the adaptive neuro-fuzzy system for fault classification. The intelligent adaptive neuro-fuzzy system was adopted to enhance the fault classification performance due to its flexibility and ability in dealing with uncertainty and robustness to noise. This system classifies the input data to the faults in the race or the balls of each of the fan-end and the drive-end bearings with specific fault diameters. In the final part of this study, the new experimental signals were processed in order to verify the results of the proposed method. The results reveal that this method has more accuracy and better classification performance in comparison with other methods, proposed in the literature.

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In this research, an intelligent method is introduced for remaining useful life prediction of an internal combustion engine timing belt based on its vibrational signals. For this goal, an accelerated durability test for timing belt was designed and performed based on high temperature and high pre tension. Then, the durability test was began and vibration signals of timing belt were captures using a vibrational displacement meter laser device. Three feature functions, namely, Energy, Standard deviation and kurtosis were extracted from vibration signals of timing belt in healthy and faulty conditions and timing belt failure threshold was determined. The Artificial Neural Network (ANN) was used for prediction and monitoring vibrational behavior of timing belt. Finally, the ANN method based on Energy, Standard deviation and kurtosis features of vibration signals was predicted timing belt remaining useful life with accuracy of 98%, 98% and 97%, respectively. The correlation factor (R2) of vibration time series prediction by ANN and based on Energy, Standard deviation and kurtosis features of vibration signals were determined as 0.87, 0.91 and 87, respectively. Also, Root Mean Square Error (RMSE) of ANN based on Energy, Standard deviation and kurtosis features of vibration signals were calculated as 3.6%, 5.4% and 5.6%, respectively.

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The main objective is to improve Hilbert-Huang transform using the advantages of non-linear entropy-based features in the time and frequency domain to reduce noise effects. In addition, applying appropriate entropy-based features lead to restrict information redundancy and overcome the need for dimension reduction, in the fault detection of a rotating system. To modify the Hilbert-Huang method, the effect of added noise on various types of nonlinear entropy-based features is investigated for each intrinsic mode functions (IMFs) which extracted by ensemble empirical mode decomposition algorithm. Considering the approximate entropy (ApEn) sensitivity to noise, an evaluation index is presented for selecting the proper amplitude of the added noise based on the approximate entropy and mutual information coefficient of the different IMFs. Subsequently, taking into account the high capability of permutation entropy (PeEn) and marginal Hilbert spectrum entropy (MHE) in the signal characteristic, a threshold is determined for fault detection based on their values associated to the main IMF which has the highest value of mutual information coefficient. As a result, the permutation entropy values and marginal Hilbert spectrum entropy of the main IMF can be used for detection of any deviation from normal operation of the rotor bearings system, regardless of the fault type. Consequently, to determine the type of defect, the higher-order spectra have been used.The bi-spectrum of envelope is calculated. This bi-spectrum is employed to identify the coupling between the rotating frequency and fault-characteristic frequencies, for misalignment and unbalanced fault diagnosis of a rotating machinery vibration simulation system