Switches are one of the most important pieces of infrastructure in railway signal systems, and they significantly influence the efficiency and safety of train operation. Currently, the identification of switch failures mainly depends on the experience of railway
staff and the use of simple thresholding methods. However, these basic methods are highly inaccurate and frequently result in false
and missing alarms. This paper aims to develop a hybrid fault diagnosis (HFD) method for railway switches. The method is an
intelligent diagnosis method that uses massive current curves collected by microcomputer monitoring systems. We first divide the
switch operation current curves into three segments based on the three mechanical processes that occur during switch operation.
Then, a standard curve is selected from the fault-free curves, and common typical faults are ascertained through a microcomputer
monitoring system. Finally, derivative dynamic time warping and a quartile scheme are employed to identify fault curves. An
experiment based on current curves collected from the Guangzhou Railway Bureau in China demonstrates that the HFD method
is extremely accurate and has low false and missing alarm rates. HFD performs better than the studied support vector machine
(SVM) and dynamic time warping (DTW) methods, which are widely used for fault diagnosis.
Abstract-Accidental falls are crucial causes of death due to injury among the elderly. Many researches about fall detection applied complex algorithms and required heavy equipment. However, these approaches can hardly apply to the elderly's daily life. In this paper, we employ a six-axis gyroscope that integrated in a small smart bracelet. Users who wear the smart bracelet can get the information including acceleration for X, Y, and Z movement, and the rate of rotation in space. Then, we introduce three feature vector generation methods based on the information and feed these three vectors into support vector machine (SVM) algorithm for fall detection. From a dataset of 66 people, we show that the geometric parameters method is the best of the three with a high accuracy (100%), low false alarm rate (0%) and low missing alarm rate (0%) in a simulated home environment.
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