# A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms
###### Abstract  
Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train's internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults. urban rail transit; turn-back fault; rule generation; classification algorithm; topic analysis Article  
A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Frameworkstudies have been undertaken on AEC and PEC [2; 4; 3]. In this study, the characteristics of AEC and PEC failures were obtained to contribute to the related research. Research into the three different kinds of turn-back fault has been undertaken to help the system make an accurate and timely diagnosis. From a data-driven perspective, this research uses an overall framework for understanding train failure during reentry. Specifically, the research on urban rail transit can (1) mine different reentry rules; (2) combine rules and algorithms to improve the quality and accuracy of algorithms; and (3) help testers analyze, understand, and determine the faults. Based on the urban rail transit system, this study analyzed tabular and text data. We searched and cleaned the data in train work logs and daily work reports of field testers. As implied by the \"no free lunch theorem\" [5], there is no universal optimal algorithm. This research combined real application scenarios and domain knowledge to conduct a comparative test of classification algorithms. We established a data set containing three types of turn-back failures. This data set is large, and the proportional distribution of fault categories was kept to be consistent with the real scene. The data set containing all three reentry scene failures is valuable for the field of urban rail transit failures. Understanding these failures can improve the efficiency of the urban rail transit system and ensure the safety of passengers. The frequent itemset generation (FIG) algorithm can be used to mine the rules under different failure scenarios. Classification algorithms, such as random forest (RF) [6], gradient boosting decision tree (GBDT) [7], AdaBoost [8], classification regression tree (CART) [9], logistic regression (LR) [10], support vector machine (SVM) [11], and naive Bayes [12], are often used in the research of classification problems in industrial scenarios. In this study, we used the frequent itemset generation algorithm based on Spark to mine feature combinations that frequently appeared in the work log and performed feature crossover based on the frequent item sets.