The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.
ICCBR-2017, the 25th International Conference on Case-Based Reasoning, was held in Trondheim (Norway) in June 2017. The conference included 27 original contributions presented in oral sessions and in a poster session. In addition to three invited talks, the meeting also included workshops on CBR and Deep Learning, Computer Analogy, and Process-Oriented CBR, as well as a Doctoral Consortium, the Computer Cooking Contest, and the first CBR Video Competition.
Comparisons of different deep learning-based methods on fault diagnosis for geared system
