Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning

Jung-Sing Jwo,Li Zhang,Cheng-Hsiung Lee,Ching-Sheng Lin,Sin-Ming Huang

doi:10.3390/app11178243

Jung-Sing Jwo, Li Zhang + Show 3 more

Open Access

https://doi.org/10.3390/app11178243

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Abstract

Railway wheelsets are the key to ensuring the safe operation of trains. To achieve zero-defect production, railway equipment manufacturers must strictly control every link in the wheelset production process. The press-fit curve output by the wheelset assembly machine is an essential indicator of the wheelset’s assembly quality. The operators will still need to manually and individually recheck press-fit curves in our practical case. However, there are many uncertainties in the manual inspection. For example, subjective judgment can easily cause inconsistent judgment results between different inspectors, or the probability of human misinterpretation can increase as the working hours increase. Therefore, this study proposes an intelligent railway wheelset inspection system based on deep learning, which improves the reliability and efficiency of manual inspection of wheelset assembly quality. To solve the severe imbalance in the number of collected images, this study establishes a predicted model of press-fit quality based on a deep Siamese network. Our experimental results show that the precision measurement is outstanding for the testing dataset contained 3863 qualified images and 28 unqualified images of press-fit curves. The proposed system will serve as a successful case of a paradigm shift from traditional manufacturing to digital manufacturing.

Highlights

In recent years, many leading industrial countries have invested in national plans to support the domestic manufacturing industry’s move towards smart manufacturing to achieve Industry 4.0’s vision
To address the class imbalance, a modulating factor 1 − p xi, xj γ was added to the cross-entropy loss function, as defined in Formula (1), with a tunable focusing parameter γ ≥ 0
The press-fit curve is an important indicator of the quality of the railway wheelset press-fitting

Summary

Introduction

Many leading industrial countries have invested in national plans to support the domestic manufacturing industry’s move towards smart manufacturing to achieve Industry 4.0’s vision. In railway wheelset manufacturing and maintenance, numerical control (NC) wheelset assembly machines are commonly used to press-fit wheels, and continuously monitor and record pressure changes through the generated force–time or force-displacement curves [13]. As insufficient or excessive press-fitting force will lead to safety risks, operators need to monitor and evaluate the assembly quality based on the characteristics of the press-fit curve change at any time [14]. Sci. 2021, 11, 8243 cessful application of the deep Siamese network in manufacturing and proposal of an intelligent railway wheelset inspection system, suitable for railway equipment manufacturers. The main contribution of this paper is a demonstration of the successful application of the deep Siamese network in manufacturing and proposal of an intelligent railway wheelset inspection system, suitable for railway equipment manufacturers.

Deep Siamese Neural Networks

Experimental Results and Analysis

Actual Results

Conclusions