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Train bearing fault diagnosis method based on multi-directional reconstruction

A fault diagnosis, multi-directional technology, used in mechanical bearing testing, motor vehicles, thermometer applications, etc., can solve problems such as missing reports of abnormal conditions

Active Publication Date: 2019-01-04
NORTHEASTERN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, within the threshold range, the abnormal change of the correlation between the bearing temperatures cannot be detected by this method, resulting in the underreporting of abnormal conditions.

Method used

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  • Train bearing fault diagnosis method based on multi-directional reconstruction
  • Train bearing fault diagnosis method based on multi-directional reconstruction
  • Train bearing fault diagnosis method based on multi-directional reconstruction

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Embodiment Construction

[0065] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0066] The method of this embodiment is as follows:

[0067] The embodiment of the invention is for fault bearing location during train operation. The 36 temperature sensors on the train correspond to 36 important positions, and the 36 temperature sensors are used to monitor the working status of the train bearings. It contains 4 bearings, each bearing has 7 temperature sensors (small gearbox motor side, small gearbox wheel side, large gearbox motor side, large gearbox wheel side, motor stator, motor drive end, motor non-drive end ). Also included are 8 axle boxes, each with a temperature sensor, for a total of 36 sensors. In the collected train bearing temperature dat...

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Abstract

The invention provides a train bearing fault diagnosis method based on multi-directional reconstruction, which relates to the technical field of data-driven train fault diagnosis. The train bearing fault diagnosis method adopts a dynamic internal model principle component analysis method to perform modeling on bearing temperature data in the normal running process of a train, so as to monitor bearings in the train running process, diagnoses a fault sample by adopting a contribution method based multi-directional reconstruction when a fault is detected, and positions the faulty bearing. The train bearing fault diagnosis method provided by the invention has a more reasonable explanation for the bearing fault state of the train, effectively monitors the bearing state during the train runningprocess, can solve the under-reporting problem of the traditional detection method based on a threshold rule, and detects the bearing fault state of the train timely.

Description

technical field [0001] The invention relates to the technical field of data-driven train fault diagnosis, in particular to a train bearing fault diagnosis method based on multi-directional reconstruction. Background technique [0002] The running speed of the train can reach more than 300km / h. Once a fault occurs while running under high traffic density, the consequences will be disastrous. Among them, axle box faults, gearbox faults, motor stator faults, motor drive end faults, motor non-drive end faults, etc. are frequently occurring bearing faults. Bearings in a healthy state are the basic guarantee for train operation and personnel safety. Therefore, in order to ensure train operation and personnel safety, the monitoring and diagnosis of train bearing faults is very important. [0003] At present, trains usually use a rule-based method for bearing fault monitoring and diagnosis, such as the French TGV high-speed rail (Train Grande Vitesse) and Germany's ICE Intercity...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M17/08G01M13/04G01K13/00G06F17/16G06K9/62
CPCG06F17/16G01K13/00G01M13/04G01M17/08G01K2205/00G06F18/2135
Inventor 刘强方彤秦泗钊
Owner NORTHEASTERN UNIV
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