Research on Urban Rail Train Passenger Door System Fault Diagnosis
Using PCA and Rough Set

Lin, Sheng; Jia, Limin; Qin, Yong; Yu, Baosheng; Wang, Yanhui

doi:10.2174/1874155x01408010340

Cited by 11 publications

(4 citation statements)

References 10 publications

(4 reference statements)

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“…The abnormality will not affect the train running in continuous mode. Under the formula, the abnormality becomes a dominant fault [45]. By monitoring such abnormal signs, it is possible to regularly check the running status of the backup system equipment, and find the fault of the backup system equipment in time.…”

Section: Results and Analysismentioning

confidence: 99%

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Li,

2023

IJCAI

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According to the increasing demand of interactive information of rail transit on-board signal equipment, a rail transit on-board monitoring and maintenance system based on data mining is proposed. The association rules for operation data acquisition are defined and based on these rules a correlation rules algorithm is proposed to obtain more reliable understanding and operation quality evaluation of train operation information. A new approach for the safety analysis is proposed for the analysis of failure mode of rail braking system. The proposed approach uses Bayes method reasoning to introduce reliability and probability analysis of braking system, hence guiding the maintenance strategy of the system. From a lot of logs, quickly find key issues, applied in the train test and repair field. The simulation experiment results show that after analyzing the simulation data and the curve, the system extraction results have certain error in the manual calculation results, and the error value is between 0.5 and 0.6, but the overall meets the actual work needs, and optimize the invalid data to reduce the error. The reliable operation and maintainability of the system are verified.Povzetek: Razvit je sistem za spremljanje železniškega prometa s podatkovnim rudarjenjem z uporabo Bayesove metode za zanesljivost.

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Section: Results and Analysismentioning

confidence: 99%

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Li,

2023

IJCAI

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“…Despite rigorous maintenance schedules, doors and brakes continue causing incidents which drive undesirable consequences, and in addition, the causes of failure cannot always be identified. Doors, in particular, have been a subject of research to find better ways to identify and prevent failure (Dinmohammadi et al, 2016;Van der Gucht et al, 2011;Shuai et al, 2014;Cheng et al, 2013). A case study conducted in UK reported that the cause of failure could not be identified for 42% of a total of 205 incidents with the passenger doors (Dinmohammadi et al, 2016).…”

Section: Performance Of Rail Passenger Doors and Brakes Systemsmentioning

confidence: 99%

Development of a Model to Gauge Second‐order Effects of Train Component Upgrades

Scott

Cao

Peculis

et al. 2017

INCOSE International Symp

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When upgrading rail infrastructure ROI‐based decisions partly rely on the demonstration of immediate improvements from those upgrades to the system. The rail industry is beginning to suffer in some areas from diminishing ROI from improving parts of its infrastructure. To inform decisions and help justify large expenditures, modelling needs to be done to detect the likely improvement to dependent systems as well as the primary system. Sydney Trains (ST) has undertaken a pilot study to demonstrate how modelling can be used to propagate performance changes made in one sub‐system into its dependent neighbors. The trial examines how to model the effect of improvements to the air dryer on the reliability and lifecycle of pneumatic systems of the Tangara train which utilize the air: the doors and brakes. The very detailed level model in this case helps provide evidence of improved reliability in these dependent systems can then be used to further justify the ROI and to assist in assessing the safety of the system on an ongoing basis.

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“…Presently, a variety of state recognition methods have been developed in the field of fault state recognition, mainly divided into methods that are based on qualitative empirical knowledge, methods based on analytical models and methods based on data-driven. Lin S. [6] used principal component analysis (PCA) and rough set-based fault identification method for train door fault identification. The comparison found that, although the PCA method can determine whether a fault has occurred, it cannot perform fault identification.…”

Section: Introductionmentioning

confidence: 99%

Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

Zhao

Zhou

2020

Information

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The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%.

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Research on Urban Rail Train Passenger Door System Fault Diagnosis Using PCA and Rough Set

Cited by 11 publications

References 10 publications

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Analysis Platform of Rail Transit Vehicle Signal System Based on Data Mining

Development of a Model to Gauge Second‐order Effects of Train Component Upgrades

Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper

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