A multidimensional hybrid intelligent method for gear fault diagnosis

Abstract

Identifying gear damage categories, especially for early faults and combined faults, is a challenging task in gear fault diagnosis. This paper proposes a new multidimensional hybrid intelligent diagnosis method to identify different categories and levels of gear damage automatically. In this method, Hilbert transform, wavelet packet transform (WPT) and empirical mode decomposition (EMD) are performed on gear vibration signals to extract additional fault characteristic information. Then, multidimensional feature sets including time-domain, frequency-domain and time–frequency-domain features are generated to reveal gear health conditions. Multiple classifiers based on several classification algorithms and input features are combined with genetic algorithm (GA). Because of the use of multidimensional features and the combination of multiple classifiers, more accurate diagnosis results are expected with the proposed method. Experiments with different gear damage categories and damage levels were conducted, and the vibration signals were captured under different loads and motor speeds. The proposed method is applied to the collected signals to identify the gear damage categories and damage levels. The diagnosis results show it can reliably recognize single damage modes, combined damage modes, and damage levels.