An Enhanced Multifeature Fusion Method for Rotating Component Fault Diagnosis in Different Working Conditions

Abstract

Mechanical rotating components such as bearing and gear are widely used in various industrial occasions. Fault diagnosis of rotating component can guarantee operational reliability and reduce maintenance cost of system. Although many fault diagnosis methods have been developed based on artificial intelligence methods, most of them ignore the existing signal processing knowledge in rotating element fault diagnosis domain. Meanwhile, these methods just assume that working conditions such as load or rotating speed keep constant, which may cause low accuracy once operating condition changes. To address these problems, an enhanced intelligent fault diagnosis method for rotating component is proposed based on multifeature and convolutional neural network (CNN). The vibration signals are first transformed into angular domain by resampling technique. Then, the angular domain signals are converted to obtain corresponding envelope and squared envelope spectrum features, which are fused into red–green–blue color image form to enhance sample features and enlarge differences among various health states. Finally, a CNN is constructed to accomplish fault recognition. Experimental results show that the average diagnosis accuracies of planetary gearbox and rolling element bearing datasets can reach 96.7% and 95.65%, which demonstrate that the multifeature approach is effective in fault diagnosis of rotating components in scenarios with different rotating speeds.