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.

轴承故障信号是一种微弱的信号，因此容易被背景噪声淹没。因此，签名提取面临着巨大的挑战。因此，有效的特征提取方法在轴承故障提取中起着至关重要的作用。本文提出了一种基于替代解析能量算子和稀疏贝叶斯阶跃滤波（SBSF）的轴承故障检测新方法。SBSF技术可以从原始信号中消除很多背景噪声，并增强与轴承故障相关的特性。此外，它还具有很高的计算效率。此后，一种改进的分析能量算子，即对称高阶分析能量算子（SHO-AEO），它是一种优于传统解调技术的增强型解调技术，应用于从滤波后的信号中检测轴承故障特征。提议的能量度量是使用原始信号，其希尔伯特变换及其高阶导数形成的。与传统的能源运营商不同，它包括分析信号的实部和虚部的信息。作为一种解调技术，它还可以从滤波后的信号中提取幅度和频率调制。此外，与以前的能源运营商相比，它具有更好的抗噪能力。因此，所提出的结合SHO-AEO和SBSF的故障检测方法不仅具有较高的计算效率，而且还提供了更好的噪声处理潜力。通过模拟和真实测试，