Sound-vibration spectrogram fusion method for diagnosis of RV reducers in industrial robots

Abstract

Due to continuous high dynamic load of industrial robots, rotate vector (RV) reducers may be more prone to wear and pitting and there will be abnormal vibration and sound caused by incipient faults. However, many experienced experts can identify the health state of machines by the sensitivity of their hearing to some special sounds. To simulate human hearing and visualize the sound heard by experts when RV reducer is running, Mel-filter bank is designed and Mel-spectrograms are introduced to show the sound energy distribution of RV reducers under various conditions. A spliced feature is constructed to describe the texture and boundary of the energy distribution. Moreover, a sound-vibration fusion approach is proposed to fuse the spliced features of sound and vibration for accurately identifying of RV reducer health states. Single-joint RV reducers experiments are performed to obtain sound and vibration data sets under different health states. The identification results of 50 runs show that the average accuracy of the proposed sound-vibration spectrogram fusion method is 93.83 %, greatly preferable to traditional methods. It is demonstrated that the proposed method is useful for robot condition monitoring and fault identification in industrial field.