Cepsogram: A Novel Fault Characteristics Extracting Strategy of Rolling Bearings Based on Cepstrum and Spectral Negentropy

Abstract

Bearings are crucial in electromechanical equipment, and the state significantly affects operational efficiency and industrial income. Therefore, efficient and accurate monitoring of bearing health and fault diagnosis are imperative for intelligent equipment operation and maintenance. The Kurtogram is widely used for bearing fault diagnosis as it adeptly extracts faulty information from the frequency domain. However, this approach imposes limitations on the center frequency and bandwidth, preventing the association of specific frequency groups with the spectrum. Furthermore, the Kurtogram exhibits poor robustness and cannot support its use with new equipment or under extreme working conditions. To address these shortcomings, this paper introduces a novel technique known as Cepsogram. First, a new cepstrum reconstruction spectral trend estimation method is designed to distinguish different modal information in the frequency domain. In this method, the number of reconstruction iterations is increased to improves the complexity of spectrum trends and expand the diversity of modular segmentation. Additionally, to capture the cyclic transient characteristics of the signal, spectral negentropy is employed to reduce interference and obtain more obvious fault characteristics compared to steepness. The use of engineering data is evaluated to substantiate the effectiveness and stability of this proposed method.

Keywords

• Cepsogram
• fault diagnosis
• spectral negentropy
• spectrum trend