Feature recognition of small amplitude hunting signals based on the MPE-LTSA in high-speed trains

Jing, Ning; Cui, Wanli; Chong, Chuanjie; Ouyang, Huajiang; Chen, Chunjun; Zhang, Bing

doi:10.1016/j.measurement.2018.08.035

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…Nonlinear factors [ 38 , 39 ] have been proven to affect the bifurcation evolution of small amplitude hunting and, according to [ 23 ], all the values of the Lyapunov exponent of the lateral acceleration are greater than 1, which means that the lateral acceleration signals from the bogie frame have non-stationary characteristics.…”

Section: Experiments Results and Analysismentioning

confidence: 99%

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Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains

Jing

Fang

Wei

et al. 2020

Sensors

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Joint Approximate Diagonalization of Eigen-matrices (JADE) cannot deal with non-stationary data. Therefore, in this paper, a method called Non-stationary Kernel JADE (NKJADE) is proposed, which can extract non-stationary features and fuse multi-sensor features precisely and rapidly. In this method, the non-stationarity of the data is considered and the data from multi-sensor are used to fuse the features efficiently. The method is compared with EEMD-SVD-LTSA and EEMD-JADE using the bearing fault data of CWRU, and the validity of the method is verified. Considering that the vibration signals of high-speed trains are typically non-stationary, it is necessary to utilize a rapid feature fusion method to identify the evolutionary trends of hunting motions quickly before the phenomenon is fully manifested. In this paper, the proposed method is applied to identify the evolutionary trend of hunting motions quickly and accurately. Results verify that the accuracy of this method is much higher than that of the EEMD-JADE and EEMD-SVD-LTSA methods. This method can also be used to fuse multi-sensor features of non-stationary data rapidly.

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Section: Experiments Results and Analysismentioning

confidence: 99%

“…Our aim is to identify the small divergence state before hunting occurs. Thus, in this paper, the monitoring of small amplitude hunting enables us to rapidly distinguish between these four basic states online [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains

Jing

Fang

Wei

et al. 2020

Sensors

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“…Based on the monitoring data collected in recent years during the actual running of high-speed trains and the scientific test data specifically for hunting motion, Ning J conducted a preliminary study related to the monitoring of hunting and SAH motion using machine learning methods (Ning et al, 2019(Ning et al, , 2020Ye and Ning, 2019). Ma et al (2019) proposed a deep learning-based body vibration prediction method and validated it using track data from high-speed rail lines.…”

Section: Related Workmentioning

confidence: 99%

A small-amplitude hunting motion recognition method based on transfer learning

Wang

Jing

Zhao

et al. 2022

Journal of Vibration and Control

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The hunting motion of the vehicle increases the wear of the wheel tracks and affects the lateral stability of the vehicle system and safety, so the hunting motion of the vehicle needs to be recognized. However, the existing recognition methods ignore the small-amplitude hunting motion that precedes the onset of the running motion. In addition, due to the scarcity of real hunting data of high-speed trains, there is a problem of underfitting when training with traditional deep learning methods. In this paper, first, the dynamics model of a high-speed train is established, and the normal, small-amplitude hunting and hunting motions of high-speed trains in the process are simulated. Second, this paper proposes a transfer learning-based method for high-speed train hunting motion recognition. The method uses easily collected normal data samples in the training process, does not use real data samples of small-amplitude hunting and hunting motions, and completes the high-speed train running motions recognition task by transferring from simulation data (source domain) to real data (target domain). Finally, the validation is carried out using the real data, which proves that the method-related approach has some engineering application value in the intelligent monitoring of high-speed trains.

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“…Numerical experiments and line tests showed the capability of the method to separate the case of a bogie with new wheel profiles from a condition with worm profiles. Based on Multiscale Permutation Entropy and Local Tangent Space Alignment, Ning et al [16] proposed a feature extraction method to distinguish the different states of complex signals and to identify the bifurcation evolution of small amplitude hunting signals. Empirical mode decomposition (EMD) is a common approach to decompose and extract local characteristic signals, especially for the analysis and processing of nonlinear nonstationary signals [17].…”

Section: Introductionmentioning

confidence: 99%

“…In the real-time monitoring system, not only the identification accuracy but also the computational efficiency and robustness of an algorithm are really important. However, the calculation accuracy and efficiency of identification algorithm are often conflicting requirements, especially for signal decomposition methods [16][17][18][19][20][21]. Furthermore, the robustness of the algorithm is often not concerned enough and only the bogie acceleration is taken into account in the above methods.…”

Section: Introductionmentioning

confidence: 99%

A signal analysis based hunting instability detection methodology for high-speed railway vehicles

Sun

Meli

Cai

et al. 2020

Vehicle System Dynamics

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Hunting stability is a long-standing research topic and has been deeply investigated due to its great influence on railway vehicle dynamic performances. Most of the existing hunting monitoring methods detect only the large amplitude hunting instability (LAHI). However, the small amplitude hunting instability (SAHI) is still hard to be detected accurately and efficiently. To face this challenging problem, this paper describes a signal analysis based hunting instability detection methodology. The proposed method is based on crosscorrelation techniques and is able to detect both SAHI and LAHI in a simple, efficient and effective way. Eight cross-correlation indicators (CCIs) are exploited to detect anomalous SAHI and LAHI conditions. A fully detailed dynamic model of one typical high-speed railway vehicle is developed to test the methodology and to compare the CCIs under different vehicle operating conditions. The most effective CCI and its critical values are determined on the basis of the statistics and comparisons of the simulation results. Furthermore, the robustness of the proposed method to distinguish hunting instability and periodic excitations coming from track irregularities has been verified. Finally, the proposed instability detection methodology has been validated by detecting the SAHI successfully on field test data coming from specific experimental campaigns.

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Feature recognition of small amplitude hunting signals based on the MPE-LTSA in high-speed trains

Cited by 21 publications

References 13 publications

Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains

Rapid Multi-Sensor Feature Fusion Based on Non-Stationary Kernel JADE for the Small-Amplitude Hunting Monitoring of High-Speed Trains

A small-amplitude hunting motion recognition method based on transfer learning

A signal analysis based hunting instability detection methodology for high-speed railway vehicles

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