Enhancement of bearing fault detection using an alternative analytic energy operator and sparse Bayesian step-filtering

The bearing fault signal is a kind of weak signal, so it is easy to be submerged by background noise. As such, signature extraction is facing a great challenge; hence, an effective signature extraction method plays an essential role in bearing fault extraction. In this paper, a new method for bearing fault detection based on an alternative analytic energy operator and sparse Bayesian step-filtering (SBSF) was applied. The SBSF technique can remove much background noise from the raw signal and enhance the characteristics related to the bearing fault. Besides, it has also a high calculation efficiency. Afterward, an improved analytic energy operator, the symmetric high-order analytic energy operator (SHO-AEO), which is an enhanced demodulation technique that outperforms the conventional demodulation technique, was applied to detect bearing fault signatures from filtered signals. The proposed energy measure is formed using the original signal, its Hilbert transform, and its high-order derivatives. Unlike traditional energy operators, it includes the information of the real and imaginary parts of the analytic signal. As a demodulation technique, it is also tailored to extract both the amplitude and frequency modulations from the filtered signal. Furthermore, compared with the previous energy operators, it provides better anti-noise capability. Hence, the proposed fault detection method of combining the SHO-AEO and SBSF not only has high computational efficiency but also provides much better noise handling potential. Through simulated and real tests, this proposed method is demonstrated to be robust against various noise levels and to detect the bearing fault signature.