# Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
## 1 Introduction
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]. The force-displacement curve, also known as the press-fit curve, is an important indicator of the quality of wheelset press-fitting. 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]. In general, the press-fit quality can meet the standard by using NC wheelset assembly machines, but this is far from enough to achieve the goal of zero-defect production in railway equipment manufacturing. For safety reasons, operators need to recheck press-fit curves manually and individually, instead of judging by press-fit records, as in our practical case. However, there are many uncertainties in manual inspection because the specific measurement criteria of curve judgment are not suitable for quantitative representation, such as the judgement rules of \"flat part\" or \"evenly rise\" [15]. Consequently, there are often ambiguous results in press-fit curve judgment, which lead to different interpretations of the results between inspectors. Figure 1 shows some of the press-fit curves used in this study. It can be seen that there is little difference between the qualified and unqualified images of press-fit curves. In addition, human misinterpretation errors are more likely to happen, due to the long working hours required. Therefore, an intelligent automatic system that can assist the inspector in judging the press-fit curves is urgently needed to improve the reliability and efficiency of wheelset press-fit inspection. Compared with traditional machine learning technologies, deep learning has recently become a trendy research topic in AI [16; 17; 18]. This solves the central problems in representation learning by expressing more straightforward representations to enable computers to build complex patterns out of simpler concepts [19]. However, AI technologies still have limitations. Data are key to successful machine-learning algorithms. Insufficient data will lead to failed classification results. However, in the practical cases of the manufacturing industry, it is not easy to collect enough representative abnormal data because normally operating factories are unlikely to experience abnormal conditions frequently. For this reason, the number of abnormal samples that can be collected in this experiment is obviously much lower than for normal samples. The number of unqualified images of the press-fit curve only accounts for 2.3% of all images (109/4754). A description of the number of collected images can be found in Section 4. At present, many scholars have successfully applied the Siamese network methods in various application fields to suppress the impact of class imbalance on classification performance [20; 21; 22]. Therefore, this study applies a deep Siamese network to establish the prediction model of press-fit quality, to solve the severe imbalance between positive and negative samples. 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. The remainder of this paper is organized as follows. The review of deep Siamese neural network is described in Section 2. Section 3 presents the system architecture of the intelligent railway wheelset inspection system and the deep learning technique used. Section 4 describes experimental results and analysis, and the conclusions are presented in Section 5.