Multi-sensor gearbox fault diagnosis using generalized minimum entropy deconvolution and main frequency center extraction

Abstract

Accurate diagnosis of gearbox faults is vital to maintain the stability and dependability of rotating machinery. In practical engineering scenarios, the installation of multiple sensors is often necessary to monitor numerous variables and gather extensive information. To effectively utilize the fault information from multiple sensors and improve fault diagnosis performance, this paper presents a gearbox fault diagnosis method using generalized minimum entropy deconvolution (GMED) and main frequency center extraction (MFCE). Firstly, the introduced GMED enhances the fault signals from multiple sensors through the maximization of standardized moments, offering advantages such as stability, wide applicability, and minimal parameter tuning requirements. Subsequently, the proposed MFCE is employed to extract features. MFCE selects main spectral lines and derives a novel feature metric named main frequency center. This not only further reduces the influence of interfering components, but also requires the extraction of only 2 n features (where n is the number of sensors) to achieve a high fault recognition rate, while remaining compatible with multiple classical classifiers. Finally, the effectiveness and robustness of the proposed method are demonstrated through experimental results on two gearbox fault datasets.