A Turnout Fault Diagnosis Method Based on Qualitative Trend Analysis

Abstract

The invention discloses a turnout fault diagnosis method based on qualitative trend analysis. According to the method, first, Gaussian filtering and interval half trend extraction are performed on typical current running signals in the five states of turnout normality, unlocking difficulty, step conversion resistance fault, sawtooth conversion resistance fault and locking difficulty fault, fault trend rules are summarized, and a fault diagnosis knowledge base is established; second, Gaussian filtering and interval half trend extraction are performed on a to-be-diagnosed current running signal, obtained fault trends are matched with the fault trend rules in the fault diagnosis knowledge base, and corresponding matching degrees are obtained; if all the matching degrees are smaller than 0.5, fault reasons are determined through manual analysis; if not all the matching degrees are smaller than 0.5, the fault trend with the maximum matching degree is the turnout state; and finally a fault diagnosis result is output. The method is low in cost, simple in structure and convenient to operate, and the diagnosis result has high credibility.

Description

technical field [0001] The invention belongs to the field of state detection of railway turnouts, in particular to a method for diagnosis of turnout faults based on qualitative trend analysis. Background technique [0002] Turnouts are installed between two or more strands of track to connect different tracks. Restricted by its own complex mechanical structure and actuators, the mechanical strength of each part of the turnout is generally lower than that of the equipment on the line, so the mechanical structure is prone to fatigue changes, resulting in excessive clearance between the point rail and the basic rail of the turnout, resulting in Trains have been jammed or even derailed. Turnouts are generally installed outdoors, and their working environment is greatly affected by weather factors. For example, strong winds can cause debris between the point rail and the basic rail of the turnout to be blocked, and rainy and snowy weather will cause abnormal resistance to the sl...

AI Technical Summary

Problems solved by technology

However, the complex and harsh operating environment of the turnout site makes the preset thresholds in the computer monitoring equipment lose their reference value after a period of time. In addition, these thresholds are generally set by maintenance experts, and the ambiguity of expert experience also makes them Summarized threshold rules fail to work in the long run and generalize effectively

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