Fusion Sparse Coding Algorithm for Impulse Feature Extraction in Machinery Weak Fault Detection

Impulse components in vibration signals are important indicators of machinery health states. Sparse coding (SC) is regarded as an efficient impulse feature extraction method, but it cannot extract the weak impulse features in vibration signals with heavy background noises. In this paper, a fusion sparse coding (FSC) method is proposed to extract impulse components effectively. Firstly, several sparse coding algorithms are executed in parallel independently as participating algorithms. Then, fusion scheme of different sparse coding algorithms is presented to improve the accuracy of sparse signal reconstruction. Lastly, the proposed method is used to process aircraft engine rotor vibration signals compared with other feature extraction approaches. Experiment result shows FSC method can extract impulse features accurately from heavy noisy vibration signal, and it provides great significance for machinery weak fault detection and diagnosis.