Deep learning-based fault diagnosis systems for prognostics and health management of mechanical systems is an active research topic. Notably, the absence and class imbalance of fault data (insufficient fault data compared to normal data) have been shown to cause many challenges in developing fault diagnosis systems for the manufacturing fields. Therefore, this paper presents case studies using deep learning algorithms in the absence or class imbalance of fault data. Auto-encoder-based anomaly detection method, which can be used when fault data is absent, was applied to diagnose faults in a robotic spot welding process. The anomaly detection threshold was set based on the reconstruction error of trained normal data and the confidence level of the distribution of normal data. The anomaly detection performance of the auto-encoder was verified using non-trained normal data and three sets of fault data through the threshold. As a case study for insufficient fault data, synthetic data was generated based on cGAN and applied to diagnose fault of bearing. Using the imbalanced dataset to generate synthetic fault data and to reduce the imbalance ratio, it was confirmed that the accuracy of the synthetic data generation-based 2DCNN fault diagnosis model was improved.
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The purpose of this study was to analyze dynamic postural balance against tilting perturbation in the young and the elderly. Twenty-eight young subjects and 22 elderly subjects participated in this study. Subjects performed dynamic balance test on a force plate during tilting perturbations (tilt-up and tilt-down). As outcome measures, peak distance and velocity were calculated from center of pressure (COP). Two-way ANOVA were performed for the outcome measures with the independent factors of age and gender. COP peak distance of the elderly was significantly greater than that of the young (p < 0.05). Velocity of COP showed age difference (p < 0.001) and also interaction effects only in tilt-up perturbation (p < 0.05). Especially, age-related difference existed in only women (p < 0.001). The age-related changing of women in the dynamic balance may be related to the greater fall rate of elderly women.
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