Traditional feature extraction methods are used to extract the features of signal to construct the fault feature matrix, which exists the complex structure, higher correlation, and redundancy. This will increase the complex fault classification and seriously affect the accuracy and efficiency of fault identification. In order to solve these problems, a new fault diagnosis (PABSFD) method based on the principal component analysis (PCA) and the broad learning system (BLS) is proposed for rotor system in this paper. In the proposed PABSFD method, the PCA with revealing the signal essence is used to reduce the dimension of the constructed feature matrix and decrease the linear feature correlation between data and eliminate the redundant attributes in order to obtain the low-dimensional feature matrix with retaining the essential features for the classification model. Then, the BLS with low time complexity and high classification accuracy is regarded as a classification model to realize the fault identification; it can efficiently accomplish the fault classification of rotor system. Finally, the actual vibration data of rotor system are selected to test and verify the effectiveness of the PABSFD method. The experimental results show that the PCA method can effectively eliminate the feature correlation and realize the dimension reduction of the feature matrix, the BLS can take on better adaptability, faster computation speed, and higher classification accuracy, and the PABSFD method can efficiently and accurately obtain the fault diagnosis results.

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Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System

Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of faults (FDD) in traction systems have become an active issue in the transportation area over the past two decades. Among these FDD methods, data-driven designs, that can be directly implemented without a logical or mathematical description of traction systems, have received special attention because of their overwhelming advantages. Based on the existing data-driven FDD methods for traction systems in high-speed trains, the first objective of this paper is to systematically review and categorize most of the mainstream methods. By analyzing the characteristic of observations from sensors equipped in traction systems, great challenges which may prevent successful FDD implementations on practical high-speed trains are then summarized in detail. Benefiting from theoretical developments of data-driven FDD strategies, instructive perspectives on this topic are further elaborately conceived by the integration of model-based FDD issues, system identification techniques, and new machine learning tools, which provide several promising solutions to FDD strategies for traction systems in high-speed trains. 

Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long-Short Term Memory, Deep Neural Network, Fuzzy Deep Neural Network, Adaptive Neuro Fuzzy Inference System and BRBES it is 18.66, 28.49, 17.05, 16.37 and 38.15 for combined cycle power plant respectively, which are significantly higher.

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A Deep Learning Inspired Belief Rule-Based Expert System

Long-time operation of traction systems under severe working environment easily induces various faults in high-speed trains, and thereby puts passengers in an unsafe position. Thanks to widely equipped sensors in high-speed trains and the technical development of advanced data analysis techniques, real-time fault detection (FD) using only online data is an inevitable trend to improve safety and reliability of high-speed trains. In this paper, an edge computing-aided FD framework for traction systems in high-speed trains is proposed; the superior advantages include: 1) its implementation can be easily carried out without system information such as accurate mathematical models of traction systems; 2) it has the well-deserved expansibility without being redesigned for the control structure of traction systems; 3) it nearly does not occupy resource of traction control units, and it does not overburden the information transmission systems. This highly intelligent FD framework using edge computing is firstly speculated in details, and its core theory is then applied to a dSPACE platform of high-speed trains for verification.

Edge Computing-Aided Framework of Fault Detection for Traction Control Systems in High-Speed Trains

The running gear is a vital component of a high-speed train to ensure operation safety. Accurately predicting the future health status of the running gear is significant to keep the reliability and safety of trains. It is difficult to predict the future health status based on the analytical model of the running gear system because of its complexity and coupling. Moreover, the fault data are a minor part of tremendous data in the running and monitoring process of a high-speed train, which obstructs accurately predicting the health status based on a data-driven method. To solve the above problems, this paper proposes a health status prediction method based on the belief rule base (BRB) for the running gear system. First, a failure mechanism is analyzed to confirm the fault characteristics, which can represent the health status of the running gear system. Second, in order to avoid the limitations of a single sensor acquisition, such as a lack of comprehensiveness and robustness, singular value decomposition is used to achieve multisensory information fusion. The fused features are used as the input to the health status prediction model. Data fusion is a way to improve the precision of the health status prediction in the model input. Then, this model based on the BRB is established using the fault data and expert knowledge. During the process of prediction, the subjectivity of experts makes the initial BRB imprecise, so a projection constrained covariance matrix adaptive evolution strategy algorithm is needed to optimize the initial parameters and improve the accuracy of the proposed model. Finally, a case study for the running gear system is carried out to verify the effectiveness and accuracy of the proposed model. The results show that the proposed model can help to accurately predict the health status of the running gear system.

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Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China; National Engineering Laboratory, CRRC Changchun Railway Vehicles Co., Ltd., Changchun 130062, China; Department of Automation, Tsinghua University, Beijing 100084, China; School of Mechatronic Engineering, Changchun University of Technology, Changchun 130012, China; Academy of Astronautics, Harbin Institute of Technology, Harbin 150001, China

Principal component analysis and belief-rule-base aided health monitoring method for running gears of high-speed train

The proper operation of running gears of a high-speed train is one of the key factors to ensure its safety and reliability. The diagnosis of the state of running gears of a high-speed train is one of the effective ways to improve its reliability. It is difficult to diagnose the running gears of a high-speed train accurately because of the characteristics of its complex-analytic structure, multiple types of monitoring feature data, and lack of effective failure mode data. Therefore, this paper proposes a fault diagnosis method for the running gears of a high-speed train based on a semi-quantitative information model. The relation between the effective data and expert knowledge is studied, and the state of the running gears of a high-speed train is rigorously analyzed. To reduce the data dimension and the diagnostic calculation time of the running gears of a high-speed train, the principal component analysis (PCA) is used to screen its key monitoring features. Then, based on the change of the feature quantity in the working process of the running gears of a high-speed train, the semi-quantitative information model of belief-rule-base (BRB) fault diagnosis is established. In the diagnosis process, the initial model parameters of BRB are determined by expert knowledge and they have certain subjectivity. To improve the accuracy of the model, the constrained covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the parameters of the initial BRB model to improve the validity and accuracy of the diagnosis. Finally, to verify the effectiveness of the proposed semi-quantitative information model, a set of real data of the running gears of a high-speed train is used as case studies.

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Mingliang Gao

Papers

Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

Principal component analysis and belief-rule-base aided health monitoring method for running gears of high-speed train

A Semi-Quantitative Information Based Fault Diagnosis Method for the Running Gears System of High-Speed Trains

A study of function-based wind profiles based on least squares method: A case in the suburbs of Hohhot