Deep learning-based rail wagon bearing oil shedding fault detection method

Abstract

The invention provides a rail wagon bearing oil shedding fault detection method based on deep learning, and the method comprises the following steps: obtaining a bearing region image, and constructing a training set; building a bearing oil shedding fault detection network; the training set is preprocessed and sent to the fault detection network in batches for training, and a final fault detection model is obtained by adjusting parameters; and acquiring a to-be-detected image, processing the to-be-detected image, inputting the to-be-detected image into the fault detection model, and calculating an abnormal score to obtain a bearing oil shedding fault detection result. The automatic detection method is high in accuracy and real-time performance, and solves the problem of false detection and missing detection caused by visual fatigue due to the fact that faults can only be judged by identifying images through naked eyes of dynamic vehicle inspectors at the present stage. On the basis of the deep convolutional neural network, a structure of an auto-encoder and a generative adversarial network is adopted, and the combination of the auto-encoder and the generative adversarial network can reconstruct a specifically distributed picture more naturally, which has stronger feature extraction capability for fault detection.

Description

technical field [0001] The invention relates to the technical field of image recognition and fault detection of railway freight cars, in particular to a deep learning-based detection method for oil throwing faults of railway freight car bearings. Background technique [0002] With the development of the national economy and the continuous improvement of the demand for railway transportation, the scale of railway freight continues to expand, and the failure of railway freight cars directly affects the safety of train operation. As an important part of the side frame of railway freight cars, rolling bearings are prone to failures such as oil throwing and loss of gear keys, which will affect the running of the train in the slightest and cause traffic accidents in the worst case. [0003] Usually, the fault detection of railway wagons is accomplished through the combination of dynamic vehicle inspectors and high-speed camera capture systems. Dynamic vehicle inspectors check the...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: The purpose of the present invention is to solve the problem that the existing railway freight car bearing oil throwing fault identification can only be completed by human eyes, and proposes a railway freight car bearing oil throwing fault detection method based on deep learning, which saves manpower and material resources , which is convenient for railway dynamic inspectors to quickly troubleshoot

Images