Performance Degradation Prognosis Based on Relative Characteristic and Long Short-Term Memory Network for Components of Brake Systems of in-Service Trains

Ding, Jingxian; Zuo, Jianyong

doi:10.3390/app122211725

Cited by 2 publications

(6 citation statements)

References 31 publications

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“…The microcomputer-controlled electropneumatic brake system (Figure 1(a)) is widely used in trains, and it is mainly composed of driver controller, compressed air supply unit (CASU), electronic brake control unit (EBCU), pneumatic brake control unit (PBCU), and basic brake unit (BBU). 5 Driver controller generates brake command signals and transmits them to EBCU for brake force calculation and distribution. CASU supplies compressed air to PBCU and other air-consuming equipment.…”

Section: Brake System Of High-speed Trainmentioning

confidence: 99%

Section: Black Box Model Of Air Brake Systemmentioning

confidence: 99%

“…Fault prognosis, detection and diagnosis are effective ways to improve system active safety ability and reduce accident risk. 4,5 Researches on fault prognosis, detection and diagnosis of brake system have been reported in recent years, 6,7 and the relevant methods mainly contain three categories: model-based, data-driven, and signal processing-based. Ding and Zuo 5 proposed a performance degradation prognosis model based on relative characteristic and long short-term memory network for components of brake systems.…”

Section: Introductionmentioning

confidence: 99%

“…4,5 Researches on fault prognosis, detection and diagnosis of brake system have been reported in recent years, 6,7 and the relevant methods mainly contain three categories: model-based, data-driven, and signal processing-based. Ding and Zuo 5 proposed a performance degradation prognosis model based on relative characteristic and long short-term memory network for components of brake systems. Niu and Zhao 8 proposed a fault detection and isolation method for locomotive brake system based on bond graph model.…”

Section: Introductionmentioning

confidence: 99%

“…Fault prognosis, detection and diagnosis are effective ways to improve system active safety ability and reduce accident risk. 4,5…”

Section: Introductionmentioning

confidence: 99%

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System-level performance prognosis based on data augmentation for air brake systems of in-service trains

Ding,

Zuo

2023

Advances in Mechanical Engineering

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In order to prognose the performance for air brake systems of in-service trains, a data augmentation method based on correlation analysis and improved multivariate support vector regression was proposed in this paper. By using black box theory and correlation analysis, brake cylinder pressure was extracted as the performance identification signal of air brake system, and the input and output signals were used as the auxiliary signals to construct the black box model of air brake system. Meanwhile, in order to make full use of the obtained data information, a data augmentation prognosis model based on improved multivariate support vector regression was established. Moreover, the optimal parameters of prognosis model were selected by means of particle swarm optimization algorithm, and RMSE and MAE were used as evaluation indicators. Finally, case studies on test-rig performance experiment data of air brake system were conducted. The prognosis model was trained and verified by using the service 1-7N and emergency, service full brake-relief and stage brake-relief experiment data, respectively, and the modeling and calibration error at pressure-holding stage were both in the order of ±5 kPa, which demonstrated the proposed method to be quite accurate and effective.

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Section: Brake System Of High-speed Trainmentioning

confidence: 99%

Section: Black Box Model Of Air Brake Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Fault prognosis, detection and diagnosis are effective ways to improve system active safety ability and reduce accident risk. 4,5…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

System-level performance prognosis based on data augmentation for air brake systems of in-service trains

Ding,

Zuo

2023

Advances in Mechanical Engineering

Self Cite

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Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization

Wang

et al. 2023

Actuators

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As the core component of the automobile braking system, brake pads have a complex structure and high failure rate. Their accurate and effective state monitoring can help to evaluate the safety performance of brake pads and avoid accidents caused by brake failure. The wear process of automobile brake pads is a gradual, nonlinear, and non-stationary time-varying system, and it is difficult to extract its features. Therefore, this paper proposes a CNN-LSTM brake pad wear state monitoring method. This method uses a Convolutional Neural Network (CNN) to complete the deep mining of brake pad wear characteristics and realize data dimensionality reduction, and a Long Short-Term Memory (LSTM) network to capture the time dependence of the brake pad wear sequence, so as to construct the nonlinear mapping relationship between brake pad wear characteristics and brake pad wear values. At the same time, the artificial Gorilla Troops Optimization (GTO) algorithm is used to perform multi-objective optimization of the network structure parameters in the CNN-LSTM model, and its powerful global search ability improves the monitoring effect of the brake pad wear status. The results show that the CNN-LSTM model based on GTO multi-objective optimization can effectively monitor the wear state of brake pads, and its coefficient of determination R2 value is 0.9944, the root mean square error RMSE value is 0.0023, and the mean absolute error MAE value is 0.0017. Compared with the BP model, CNN model, LSTM model, and CNN-LSTM model, the value of the coefficient of determination R2 is the closest to 1, which is increased by 8.29%, 5.52%, 4.47%, 3.30%, respectively, which can more effectively realize the monitoring and intelligent early warning of the brake pad wear state.

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Performance Degradation Prognosis Based on Relative Characteristic and Long Short-Term Memory Network for Components of Brake Systems of in-Service Trains

Cited by 2 publications

References 31 publications

System-level performance prognosis based on data augmentation for air brake systems of in-service trains

System-level performance prognosis based on data augmentation for air brake systems of in-service trains

Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization

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