# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 1 Introduction  
The Train Communication Network (TCN) is crucial for the normal operation of trains. Control information and monitoring information of trains are transmitted through the train communication network, so the TCN is a necessary condition to ensure the normal operation of trains [1; 2; 3]. The Multifunction Vehicle Bus (MVB) is the vehicle bus of the TCN, which is used for connecting programmable stations and simple sensors/actors [4; 5; 6]. Due to the large amount of on-board equipment, the diverse operating environment and the complex network topology, it is difficult for maintenance personnel to perform timely repairs of MVB network faults, which reduces the efficiency of train operation and maintenance, leading to an increase in operation and maintenance costs. The communication data of the MVB exist in two main forms during transmission: physical-layer data and link-layer data. MVB devices generate digital link-layer data, and analog voltage waveform signals (i.e., physical-layer data) are generated through digital-to-analog conversion circuits [7; 8]. Since the MVB waveforms are analog quantities generated by the conversion of digital data, the information carried by the physical-layer voltage waveform is much richer than the information carried by the link-layer data: the information in the physical-layer voltage waveform not only includes communication data, but also contains the physical features of the waveform itself [9]. These features can characterize different MVB fault modes and whether the device status is healthy or not, and this is exactly the information needed by an online diagnosis system [10; 11]. [PERSON] et al. [12] used stacked denoising autoencoders to automatically extract waveform features of the MVB physical layer and used these features to train deep neural networks to achieve MVB network fault-mode classification. [PERSON] et al. [13] proposed a method for diagnosing and locating MVB network faults based on a weighted support vector machine. Theyextracted waveform features from the physical layer to characterize different MVB faults and used the WSVM sample weight distribution method of multi-hop edge approaching to deal with the different influences of samples located at different positions in the feature space on a support vector machine (SVM) hyperplane. [PERSON] et al. [14] proposed an MVB fault diagnosis method based on physical waveform features and ensemble pruning. They classified different MVB faults with a random-forest classifier and proposed an ensemble pruning method based on diversity indexes and a k-mean algorithm to reduce the number of decision trees and improve the ensemble performance. [PERSON] et al. [15] proposed an MVB fault diagnostic method based on a support vector machine. By extracting the waveform features, fault diagnosis is transformed into a pattern-recognition problem, and an SVM-based classifier was trained for network fault classification and recognition. [PERSON] et al. [16] proposed a diagnostic method for MVB terminal faults based on multi-kernel learning support vector machines (MKLSVMs), which combined physical wave features and MKLSVMs to complete terminating fault diagnosis. The above studies mainly extracted data features from the physical layer or the data-link layer of MVBs for fault diagnosis, and their methods perform well. The results show that waveform features are related to MVB faults and are crucial for fault diagnosis. However, these studies focused more on the theoretical research of MVB fault mechanisms and diagnosis rather than practical applications. Therefore, these studies need data of high quality. Taking the latest study based on MKLSVMs as an example, this study employs a sampling rate of 100 MHz to collect waveform data, which are then analyzed to achieve waveform feature extraction from the data. However, online MVB fault diagnosis requires short diagnostic cycles and real-time data analysis. The data generated by high-speed sampling will increase computational pressure and raise costs.