Constructing Condition Monitoring Model of Harmonic Drive

Featured Application Detecting faults in a machine in the early stage reduces loss due to damage. This paper proposes a method to detect machinery anomalies through operation sounds that combines the wavelet transform and state-of-the-art neural network architecture, and can be used in an intelligent factory. The harmonic drive is an essential industrial component. In industry, the efficient and accurate determination of machine faults has always been a significant problem to be solved. Therefore, this research proposes an anomaly detection model which can detect whether the harmonic drive has a gear-failure problem through the sound recorded by a microphone. The factory manager can thus detect the fault at an early stage and reduce the damage loss caused by the fault in the machine. In this research, multi-layer discrete wavelet transform was used to de-noise the sound samples, the Log Mel spectrogram was used for feature extraction, and finally, these data were entered into the EfficientNetV2 network. To assess the model performance, this research used the DCASE 2022 dataset for model evaluation, and the area under the characteristic acceptance curve (AUC) was estimated to be 5% higher than the DCASE 2022 baseline model. The model achieved 0.93 AUC for harmonic drive anomaly detection.