Work

Constructing Condition Monitoring Model of Harmonic Drive

Year: 2022

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app12199415

Authors: Jong-Yih Kuo, Chao-Yang Hsu, Ping-Feng Wang, Hui-Chi Lin, Zhen-Gang Nie

Institutions: National Taipei University of Technology, Beijing Institute of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,408

Funder: National Taipei University of Technology