# Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
## 1 Introduction  
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. Industry 4.0 includes many technologies and related paradigms, such as the industrial internet of things, cloud-based design, digital technologies, and innovative applications of artificial intelligence (AI) [1]. It has been recognized that AI technologies are impressive and bring many benefits to the enterprise. The transportation sector, especially the railway sector, has adopted Industry 4.0 to a large extent to improve service quality, reduce cost, and increase resource utilization [2]. For railway transportation, the routine inspection and maintenance of train components and rails are crucial, because neglecting any link is likely to cause major harm. With the development of computer vision techniques, it has become possible for railway systems to use visual inspection technology to replace manual inspection. [PERSON] et al. [3] provided a full review of visual inspection applications based on image processing in the railway industry for rail surface, track component wear, rail component identification, train body components, etc. Wheelsets are key to ensuring the safe operation of trains because the failure of an axle, a wheel, or one of its bearings will inevitably lead to accidental derailment [4; 5; 6]. The primary source of damage to railway facilities and vehicles is wheel defects. Possible causes of damage include shaft misalignment, contaminated areas, excessive load, overheating, lack of lubrication, electrical damage, wheel damage, and manufacturing defects, etc. [6]. To develop wayside measurement systems for wheel defect recognition, [PERSON] et al. [7] utilized the optoelectronic measuring technique to develop a non-contact measurement system capable of measuring the geometric parameters of wheelsets online. [PERSON] et al. [8] proposed two methods to automatically detect wheel defects based on the wheel vertical force, measured by a permanently installed sensor system on the railway network. One method is based on novel wavelet features to classify time series data by support vector machine (SVM); the other method trains the convolutional neural networks (CNN) for different defect types to predict if a wheel has a defect during regular operation. [PERSON] et al. [9] established a 3D numerical dynamic model of a vehicle-track coupling system and analyzed the sensitivity and reliability of different sensors and setups for wheel flat detection. In their method, the wheel flat was identified by the envelope spectrum approach with spectral kurtosis analysis by considering the evaluated shear and accelerations in 19 positions as inputs. [PERSON] et al. [10] used a railway wheel flat measurement method based on a parallelogram mechanism to detect wheel flats dynamically and quantitatively. In addition, they established a three-dimensional simulation model based on the rigid-flexible coupled multibody dynamics theory to improve the speed threshold of the measuring mechanism vibration under wheel impact. [PERSON] et al. [11] proposed a new, long-term monitoring method for wheel flats based on multi-sensor arrays. In their method, the dynamic strain responses of rails are captured by sensor arrays mounted on the rail web to ensure that all the wheels are assessed during the train passage. The above research is mainly focused on detecting possible defects in train wheels due to long-term use or gradual deterioration. The wheelset is a critical component of the traveling mechanism and must provide precise geometric and mechanical characteristics to minimize dynamic action and avoid derailment [12]. Therefore, whether the wheel and axle can be closely integrated is an essential key to safety inspection in the manufacturing procedure of wheelsets.