Incipient Fault Detection for Air Brake System of High-Speed Trains

Sang, Jianxue; Guo, Tong; Zhang, Junfeng; Zhou, Donghua; Chen, Maoyin; Tai, Xiuhua

doi:10.1109/tcst.2020.3027673

Cited by 10 publications

(3 citation statements)

References 30 publications

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“…In modern TCSs, the increasing autonomy of the train's on-board system indicates a higher requirement to the capability in the full lifecycle, and there is a growing demand for system health monitoring and early assessment. It can be seen in the literature that great effort has been made in the fault detection and system maintenance of the on-board train control equipment, including fault detection and diagnosis [3][4][5], fault prediction [6], reliability assessment [7], health monitoring [8], and decision-making for optimized maintenance planning [9][10][11]. A preventive maintenance strategy for the on-board equipment has been considered in practical railway operation, which means the system maintenance schedule is planned according to the average or expected life time statistics or prediction.…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Liu

Cai

et al. 2023

Machines

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On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance.

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Section: Introductionmentioning

confidence: 99%

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Liu

Cai

et al. 2023

Machines

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show abstract

“…After obtaining the approximate stationary property, the multimodal data is mapped to a tight domain, and the fault signal ratio (FSR) is used to reflect the sensitivity of the proposed detection statistic to the fault. Finally, experiments were carried out on the braking test platform to verify the effectiveness of the proposed strategy [10]. Huang et al [11] proposed an improved fully integrated empirical mode decomposition based on adaptive noise (ICEEMDAN) and one-dimensional convolutional neural network (1-D CNN) fault diagnosis method, which can simultaneously identify the fault state in high-speed train bogies and the location of fault components.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

Wang

Song

et al. 2023

Applied Sciences

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A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the time series pressure data into model elements in the model space, ensuring simplicity and stability. Various fitting functions, including Fourier basis, Gaussian basis, polynomial basis, sine basis, and others, are employed to accurately fit the pressure curve of the air brake pipe. The fault diagnosis process involves two steps: classifying the fault based on an optimal approximation equation and diagnosing it by analyzing the topological relationship of the model elements in the model space. The proposed method achieves an average fault diagnosis accuracy of 89.8%, with high accuracy rates for different fault states: 98% for normal state, 88% for blockage state, 84% for leakage state, and 96% for compressor fault state. Compared to the hidden Markov model method, the proposed method improves the average diagnostic accuracy by 2% for known working conditions and 4.87% for all working conditions, demonstrating its effectiveness and reliability. The fault diagnosis of the air brake tube in high-speed trains is of great significance, which aims to realize accurate fault diagnosis and prediction through sensor data monitoring and signal processing technology, so as to ensure the safe operation of high-speed trains. These studies provide an important theoretical and practical basis for the improvement and application of fault diagnosis methods.

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“…Although incipient sensor faults occur commonly both in linear and nonlinear systems and have practical significance, they have received less attention in the past and only from 2015 research focusing incipient sensor faults are seen [15,30,33]. Initially, sensor incipient fault detection was popular in aircraft fault detection and maintenance [9,29] but with increasing demand of effective fault diagnosis in industries, sensor fault detection has become popular in rotating machinery [8,34,35], train air brake system [26,31], industrial cyber-physical systems [6], nuclear power plants [10], simulated models of continuous stirred tank reactor and tennessee eastman process [27,32] and traction systems [12].…”

Section: Introductionmentioning

confidence: 99%

Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network

Mali

Laskar

2020

SN Appl. Sci.

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Risk of sudden collapse of any industrial plant increases if small magnitude incipient faults are not detected at an early stage. The paper proposes an optimized Monte Carlo deep dropout neural network (MC-DDNN) to identify incipient faults of sensors installed in wastewater treatment plants using the historical dataset of the plant. Such faults usually remain invisible or are misinterpreted as noise signals due to their small magnitude but can be overcome by the proposed method. MC-DDNN easily identifies the incipient faults of sensors installed in a simulated wastewater treatment benchmark model as well as sensors installed in a real industrial plant. The tabulated results show the estimated probability of incipient fault in terms of percentage probability as detected by the MC-DDNN. The dissolved oxygen (DO) sensor incipient faults in benchmark simulation model (BSM2) are detected with probability ranging from 4.9% to 23.4% and DO, pH and mixed liquor suspended solids (MLSS) sensors of effluent treatment plant (ETP) are detected with probability ranging from 0.07% to 11.43%. This estimated probability of faults indicates the small magnitude of the faults and hence proves that the method is capable of identifying faults at an early stage to issue warnings for early maintenance of the plant.

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Incipient Fault Detection for Air Brake System of High-Speed Trains

Cited by 10 publications

References 30 publications

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

Incipient fault detection of sensors used in wastewater treatment plants based on deep dropout neural network

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