Gearbox bearing fault detection method and system

Abstract

The invention discloses a gearbox bearing fault detection method and system, and the method comprises the steps: processing missing data and noise data in temperature feature information data, pressure feature information data and unbalance loading feature information data through employing feature preprocessing technologies, such as missing value filling, feature redundancy removal and outlier correction; establishing a first fault classification model algorithm Y1 by using an extreme value gradient lifting algorithm, and after the temperature, pressure and unbalance loading feature information of the bearing is processed, classifying the faults of the bearing; establishing a second fault classification model algorithm Y2 by adopting a closely connected convolutional network structure capable of processing a one-dimensional frequency spectrum sequence to carry out fault diagnosis classification on the vibration characteristic information of the rolling bearing; and establishing a fault diagnosis model fusing multi-sensor features, performing decision-making layer result fusion on two single-source fault diagnosis models including a first fault classification model algorithm Y1 and a second fault classification model algorithm Y2 through a D-S evidence theory, and then identifying the fault type of the gear box bearing.

Description

technical field [0001] The invention relates to the field of fault detection, in particular to a method and system for fault detection of a gearbox bearing. Background technique [0002] Rolling bearings are a key component supporting mechanical rotation. Once a failure occurs, it will directly affect the smooth operation of the entire system, and may even cause huge economic losses and safety accidents. With the rapid development of high-speed trains, a good foundation has been laid for the construction of high-speed railways. At the same time, new requirements are put forward for the vehicle operation and maintenance department, which requires real-time monitoring of high-speed train component failures. From the perspective of safety, on the one hand, a more comprehensive, reasonable and targeted maintenance plan can be formulated for the maintenance vehicles to improve the current problem of over-repair of high-speed train maintenance. On the other hand, more high-tech t...

AI Technical Summary

Problems solved by technology

However, the above maintenance methods cannot realize real-time and effective online detection of gearbox bearings, and cannot effectively identify bearing faults in a timely manner. There is a lag in maintenance, which seriously affects the safety of train operation.

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