Traveling wave magnetic field fault detection method, device, equipment, storage medium and system

Abstract

The present application relates to a traveling wave magnetic field fault detection method, device, equipment, storage medium and system. The method obtains the magnetic gradient analysis signal and position information of the high-speed maglev track line, and divides the entire high-speed maglev track line into N sections according to the preset interval according to the position information; according to the magnetic field data in each section, the corresponding moment method is obtained. Multi-fractal spectrum, and calculate the characteristic quantity of the spectral line, input the variation of the characteristic quantity to the CFAR-constant false alarm rate detector, and determine the suspected abnormal data; for this data, use the wavelet modulus maximum value abnormal detection method to judge the singularity of the signal , and calculate the Lipschitz index of the singular point to determine whether there is an anomaly, and if so, output the specific anomaly location. This method has high detection accuracy and greatly improves the abnormal detection efficiency, making the daily maintenance and overhaul of the long stator core of the high-speed maglev track more convenient and effective, reducing economic losses and reducing safety hazards.

Description

technical field [0001] The present application relates to the technical field of fault diagnosis of large electric equipment, and in particular to a traveling wave magnetic field fault detection method, device, equipment, storage medium and system. Background technique [0002] Different from the wheel-rail track that simply carries vehicles, the track of the constant-conduction high-speed maglev is actually a long stator primary of a synchronous linear motor. It is an important component of the maglev traffic suspension, traction and transportation control system. The safety, stability and comfort of running on the train The long stator track is made of 360 pieces of silicon steel sheets with a thickness of 0.5mm. Chemical coating, the coating needs to have good insulation and high interlayer resistance, which can effectively suppress the generation of eddy current. However, the high-speed maglev track is laid in the long-distance open-air elevated form. Affected by vehicl...

AI Technical Summary

Problems solved by technology

However, the high-speed maglev track is laid in the long-distance open-air elevated form. Affected by vehicle operation and wind and sun, rail bumps and environmental corrosion may easily cause the epoxy resin layer on the surface of the stator core to crack and fall off, and the silicon steel sheet is directly exposed to the air. Further corrosion occurs, reducing the insulation performance between the silicon steel sheets, resulting in a short-circuit fault between the sheets of the iron core

This kind of fault is subtle and irregular in distribution. The initial impact is small and difficult to find. However, with the increase of eddy current loss, local overheating will aggravate the further corrosion of the stator core, resulting in an abnormal traveling wave magnetic field, which makes the maglev track unable to complete it. The electrical function of the vehicle affects the safety of the vehicle

At the same time, the grounding short circuit caused by the hanging of the winding and the damage of the winding insulation will have a great impact on the traveling wave magnetic field and cause safety hazards

[0003] The existing methods of manual visual inspection or carrying corresponding equipment for manual inspection are time-consuming, laborious, inefficient, and poor in safety. At the same time, they also affect the normal operation of the maglev transportation system, and it is difficult to carry out the maintenance and repair of the maglev track

Therefore, it is necessary to design a dynamic, fast and efficient detection method for the characteristics of maglev tracks, such as infinite length, elevated laying, few track abnormalities, uncertain distribution, and time-consuming manual inspection, so as to provide a basis for track maintenance.

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