Recently, data-driven machine health monitoring has become more popular due to the wide-spread deployment of lowcost sensors and deep learning algorithms’ achievements. The detection of failures of machines can be determined based on failure classification results using deep learning architectures. On this tendency, we constructed a plastic gear failure detection structure using a convolutional neural network. In this study, raw vibration data was converted to frequencydomain data. Amplitudes of frequencies in the monitored frequency band were transferred into images, which then were labeled as crack or non-crack by a high-speed camera. Although deep learning architectures have great potential to automatically learn from complex features of input data, the high-amplitude frequencies reflecting the main vibration causes such as gear meshing frequency and its harmonics or shaft frequency affect the accuracy of learning. Besides, the low-amplitude frequencies in a low-frequency band, which are sensitive to gear failures, show efficiency in early failure signs of the plastic gear. Thus, this paper proposed an image visualization and labeling method by focusing on lowamplitude frequency features in the low-frequency band and lessening high-amplitude frequency features. The results show that the proposed system learning from new visualized images can detect plastic gear’s early failure situation before the initial crack happened.
Nowadays, deep learning (DL) has become a rapidly growing and provides useful tools for processing and analyzing big machinery data. Many research projects achieved success in failure classification from machinery data using convolutional neural networks (CNNs), one of the most extensive study aspects of DL. On this trend, we constructed a crack detection system of POM (Polyoxymethylene) gears using a deep convolutional neural network (DCNN). In our work, vibration data collected from plastic gears was visualized and labelled as crack data or non-crack images. A DCNN based on pre-trained VGG16, which firstly pre-learned from ImageNet’s data and then re-learned from the labelled images, is utilized to classify crack or non-crack situations of plastic gears. In this case of study, the image quality distortions of the dataset such as blur, noise or contrast are stable and do not affect the performance of the DCNN. However, the image size, which keep a vital role to reach high performance of the detection system, has been unknown. Hence, this paper reveals an optimized size of images created from vibration data for high-accuracy of learning.
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