Intelligent bearing fault detection by enhanced energy operator

In this paper, we propose an intelligent bearing fault detection method based on a calculus enhanced energy operator (CEEO). The main purpose is to extract the bearing fault signatures in the presence of strong noise and multiple vibration interferences without prior information of the resonance excited by the bearing fault. This new energy operator exploits both the interference handling capability of a differentiation step and the noise suppression nature of the integration process. It also shares the simplicity, computational efficiency, and the ability to reveal signal impulsiveness of the energy operator. All these elements, i.e., differentiation, integration and energy operator, are implemented by a simple formula in a single step. Another advantage of the CEEO method is that, unlike the popular high frequency resonance methods, it does not require a bandpass filtering step and hence eliminates the burden to acquire the resonance information. As such, it is suited to on-line bearing fault detection in a noisy environment with multiple vibration interferences. Our simulation studies have shown that the CEEO method outperforms the conventional energy operator and the enveloping methods in handling both noise and interferences. Its performance has also been examined using our experimental data and the data from the literature.