Hongtian Chen

Bio: Hongtian Chen is an academic researcher from University of Alberta. The author has contributed to research in topics: Computer science & Fault detection and isolation. The author has an hindex of 13, co-authored 41 publications receiving 616 citations. Previous affiliations of Hongtian Chen include Nanjing University of Aeronautics and Astronautics.

A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

Abstract: High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

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

Abstract: 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.

Deep PCA Based Real-Time Incipient Fault Detection and Diagnosis Methodology for Electrical Drive in High-Speed Trains

Abstract: Incipient fault detection and diagnosis (FDD) is a key technology for enhancing safety and reliability of high-speed trains. This paper develops a real-time incipient FDD method named deep principal component analysis (DPCA) for electrical drive in high-speed trains. This method can effectively detect incipient faults in electrical drive before they develop into faults or failures. This scheme adopting multivariate statistics is composed of multiple data processing layers to extract more accurate signal features of electrical drive, which exhibits several salient advantages: 1) It can establish precise data models containing both systematic and noise information of electrical drive, which are helpful for incipient fault detection; 2) the incipient faults are described by multicharacteristics which can improve the fault diagnosis ability; 3) it can be easily implemented even if the system models and parameters of electrical drive are unknown. The effectiveness and feasibility of the proposed FDD scheme are authenticated via a mathematical analysis and validated via two experiments. Results of two experiments show that the missing alarm rate and detection delay by using the proposed DPCA-based FDD method are less than 10% and 0.68 s, respectively. In comparison with the standard PCA-based FDD method, the proposed DPCA-based FDD method can show its superiorities by the detailed performance comparisons.

Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains

Abstract: Incipient faults in electrical drives can corrupt overall performance of high-speed trains; however, they are difficult to discover because of their slight fault symptoms. By sufficiently exploiting the distribution information of incipient faults, this paper presents the reason why incipient faults cannot be detected by the existing fault detection and diagnosis (FDD) methods. Under principal component analysis (PCA) framework, we propose a new data-driven FDD method, which is named probability-relevant PCA (PRPCA), for electrical drives in high-speed trains. The salient strengths of the PRPCA-based FDD method are: 1) it can greatly improve the fault detectability; it is suitable for non-Gaussian electrical drives; 2) based on the improved fault detectability, it can achieve accurate fault diagnosis via support vector machine; and 3) it can be easily applied to electrical drives even if neither physical models or parameters nor expert knowledge of drive systems is given; and it is of highly computational efficiency that can meet requirements on the real-time FDD. A set of experiments on a dSPACE platform-based traction system of the CRH2A-type high-speed train are carried out to demonstrate the effectiveness of the proposed method.

An improved incipient fault detection method based on Kullback-Leibler divergence.

Abstract: This paper presents an improved incipient fault detection method based on Kullback-Leibler (KL) divergence under multivariate statistical analysis frame. Different from the traditional multivariate fault detection methods, this methodology can detect slight anomalous behaviors by comparing the online probability density function (PDF) online with the reference PDF obtained from large scale off-line data set. In the principal and residual subspaces obtained via PCA, a symmetric evaluation function is defined for both single variate and multivariate cases. The uniform form of probability distribution and fault detection thresholds associated with all eigenvalues are given. In addition, the robust performance is analyzed with respect to a wide range of Signal to Noise Ratio (SNR). Case studies are conducted with three types of incipient faults on a numerical example; combining with two nonlinear projections, the proposed scheme is successfully used for incipient fault detection in non-Gaussian electrical drive system. The results can demonstrate the superiority of the proposed method than several other methods.

A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

Abstract: High-speed trains have become one of the most important and advanced branches of intelligent transportation, of which the reliability and safety are still not mature enough for keeping up with other aspects. The first objective of this paper is to present a comprehensive review on the fault detection and diagnosis (FDD) techniques for high-speed trains. The second purpose of this work is, motivated by the pros and cons of the FDD methods for high-speed trains, to provide researchers and practitioners with informative guidance. Then, the application of FDD for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years. Finally, the challenges and promising issues are speculated for the future investigation.

Data-Driven Monitoring and Safety Control of Industrial Cyber-Physical Systems: Basics and Beyond

Abstract: Industrial cyber-physical systems (ICPSs) are the backbones of Industry 4.0 and as such, have become a core transdisciplinary area of research, both in industry and academia. New challenges brought about by the growing scale and complexity of systems, insufficient information exchange, and the exploitation of knowledge available have started threatening the overall system safety and stability. This work is motivated by these challenges and the strategic and practical demands of developing ICPSs for safety-critical systems such as the intelligent factory and the smart grid. It investigates the current status of research in ICPS monitoring and control, and reviews the recent advances in monitoring, fault diagnosis, and control approaches based on data-driven realization, which can take full advantage of the abundant data available from past observations and those collected online in real time. The practical requirements in the typical ICPS applications are summarized as the major issues to be addressed for the monitoring and the safety control tasks. The key challenges and the research directions are proposed as references to the future work.

Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

Abstract: Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.

Recent Advances in Key-Performance-Indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT

Abstract: Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems’ behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms’ robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.