Methods of Feasible Directions. By G. Zoutendijk. Pp. 126. 30s. 1960. (D. van Nostrand Co. London)

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

Contactless fault diagnosis is one of the most important technique for fault identification of equipment. Based on the idea of contactless fault diagnosis, this paper presents a sound-based diagnosis method for railway point machines (RPMs). First, the sound signals are preprocessed using empirical mode decomposition (EMD). Entropy, time-domain and frequency-domain statistical parameters of the first 15 intrinsic mode functions (IMFs) are then extracted. Second, a two-stage feature selection strategy blending Filter method and Wrapper method is proposed, which can significantly reduce the dimension of features and select the optimal features. The superiority and effectiveness of the proposed feature selection strategy are verified by comparing with other feature selection methods. Third, a weighted majority voting (WMV)-based ensemble classifier optimized using particle swarm optimization (PSO) is developed and compared with single classifiers. And the ensemble patterns are discussed to select the most optimal ensemble pattern. The average diagnosis accuracies of 10 repeated trails of reverse-normal and normal-reverse switching processes reach 99% and 99.93%, respectively, which indicates the effectiveness and feasibility of the proposed method. 

A Sound-Based Fault Diagnosis Method for Railway Point Machines Based on Two-Stage Feature Selection Strategy and Ensemble Classifier

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

Faults happen inevitably in traction systems and thus place the security of the whole high-speed train at risk. In order to improve the safety and reliability of high-speed trains, this article deals with fault detection and diagnosis (FDD) problem for traction systems. Because of high sampling frequency of equipped sensors, FDD strategies in the supervision system of high-speed trains should be of enough high computation efficiency, which is a great bottleneck for artificial intelligence-based FDD methods. For reducing the computational load while maintaining the satisfactory diagnostic accuracy, an enhanced FDD architecture using the modified principal component analysis and broad learning system is developed in this article. Based on the proposed data-driven design whose core is to extract fault information, fast and accurate FDD can be achieved without requirements for mathematical models or control mechanism of high-speed trains. The effectiveness and feasibility of the proposed online design are illustrated on the traction control platform of high-speed trains.

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

Incipient fault detection and diagnosis (FDD) is an important measure to improve the efficient, safe and stable operation of high-speed trains This paper proposes a data-driven FDD method, namely deep slow feature analysis and belief rule base method (DSFA-BRB), for the running gears of high-speed trains The method uses two kinds of statistics to perform fault detection on the multi-dimensional data of the running gears In addition, the characteristics of more accurate data are extracted, which greatly reduces the complexity of constructing a diagnostic and quantitative model Further, by constructing a BRB model combining expert knowledge and data, it is possible to avoid misjudgment caused by data incompleteness Compared with the traditional methods, the DSFA-BRB algorithm has better performance in reducing fault alarm probability Finally, the validity of the algorithm is verified by the actual running gears system

Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains

Fault diagnosis is a key way to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a fault diagnosis method based on belief rule base with mixed reliability (BRB-mr). Different from the traditional BRB, this method considers two kinds of interference factors that affect the observation data in engineering practice, including the performance of sensors and the influence of external environment, and we quantify them as static reliability and dynamic reliability of attributes in BRB. In order to integrate two kinds of reliability factors into the reasoning of BRB, a discount method is developed based on Dempster-Shafer theory (D-S theory), which is helpful for more accurate diagnosis. In this paper, the effectiveness and practicability of the method are verified by a single fault of the running gear, and the supplementary numerical data verified its feasibility in multiple fault mode diagnosis. Then this method is compared with traditional methods. The result shows BRB-mr model has stronger diagnostic ability to identify faults and it has a certain engineering application value to be extended to other complex system fault diagnosis.

A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

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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Chao Cheng

Papers

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

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

Data-Driven Incipient Fault Detection and Diagnosis for the Running Gear in High-Speed Trains

A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

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