Abstract

Subway fans play significant roles in subway system such as ventilation, smoke discharging and cooling. Nowadays, fast and effective condition monitoring of subway fan is highly important for the safety and comfort in subway system. However, as one of the most important monitoring tool, fan vibration signals are strongly interfered by frequent piston winds during train arrival-depart, such effect will cause many unnecessary false alarm and deteriorate the fault detection performance. To overcome this problem, this study proposes a condition monitoring method of subway fan under piston wind effect. Piston wind detection method and feature extraction method two key techniques for CMM-PWD method. The piston wind detection method is based on similarity estimation and a new similarity criterion is proposed in this paper. For feature extraction, cyclostationary analysis is exploited for the cyclic feature extraction. And the proposed approach is verified by the piston wind experiments and bolt looseness experiments of axial flow fan and jet fan, which are widely used in subway ventilation system. The results show that the piston wind is accurately detected by proposed method, and fan fault feature is effectively extracted. More importantly, this approach could have great potential to extend to deal with other background interference in fault diagnosis of rotating machinery.

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Abstract

Dementia of Alzheimer’s type (DAT), is a progressive neurodegenerative disorder, is the most common cause of dementia in the elderly population. Previous clinical and histological studies suggest that the neurodegenerative process, which affects the brain, may also affect the retina of DAT patients, especially the Retinal Nerve fiber Loss (RNFL) layer. Any disease-modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind brain disease. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is possible that a mixture will be required. Profiling of human body parameter using computers can be utilized for the early judgment of Dementia of Alzheimer’s type. There are many imaging techniques utilized in clinical practice for the identification of Alzheimer’s kind pathology. In this paper, extracting the RNFL layer of Retina Optical Coherence Tomography (OCT) Images for the early diagnosis of DAT has been proposed. For this purpose, we have proposed a method based on Discrete Wavelet Networks (DWNs) for extracting the RNFL layer of Retina OCT images for the classification of Alzheimer’s from normal. This method provides reliable and validated results for OCT images.