Turnout abnormity early warning and fault diagnosis method

Abstract

The invention provides a turnout abnormity early warning and fault diagnosis method. The method comprises the following steps of (1) based on the historical data of a turnout to be tested, selecting astandard motion curve; (2) using a clustering algorithm to segment the motion curve to be tested; if correct segmentation can not be performed, turning to a step (3); otherwise, turning to a step (4); (3) comparing a power curve in the motion curve to be tested with the power curve of all fault samples, searching the fault sample which is most similar to the power curve, and affirming that the motion curve to be tested has a fault corresponding to the found fault sample; (4) for each segment of the motion curve to be tested, extracting a first kind of characteristics; and (5) comparing with the standard motion curve and determining whether the motion curve to be tested is abnormal or normal. Compared with an existing segmentation method, the method has universality; simultaneously, compared with the prior art, the selected standard curve is scientific and reliability is high; in addition, adaptive diagnosis is realized, a demand to a person is less and efficiency is high.

Description

technical field [0001] The invention belongs to the technical field of rail transit, and relates to an early warning and diagnosis method, in particular to an abnormal early warning and fault diagnosis method for a turnout. Background technique [0002] By the end of 2016, the total mileage of China's high-speed rail network had exceeded 20,000 kilometers, accounting for more than 60% of the total mileage of the world's high-speed rail. The increasing speed of high-speed rail also requires a more reliable rail system to ensure the safety and efficiency of transportation. As a key equipment of the high-speed rail system, the failure rate of turnouts accounts for the highest proportion of the entire signal failure, which affects the efficiency of high-speed rail transportation. At present, the switch machines used in speed-up turnouts are usually three-phase AC switch machines. Due to the loss of switch machine components, foreign matter jamming, weather effects, etc., the sw...

AI Technical Summary

Problems solved by technology

However, the actual fault diagnosis methods for turnouts are only limited to manual judgment or simple threshold judgment.

Manual judgment mainly depends on the experience of experts, and the boundaries are blurred; and because of the same type of switch machine, there are also differences in switching time and switching current due to the different types of switch rails installed (such as Figure 1a , Figure 1b , Figure 1c and Figure 1d shown), the threshold judgment is not universal, which brings inconvenience in practical application, and may even cause misjudgment

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