&lt;b&gt;Fault Detection for Railway Traction Motor Bearing through Leakage Current&lt;/b&gt;

Sakaidani, Yo; Kondo, Minoru; Takahashi, Ken

doi:10.2219/rtriqr.60.4_256

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Results of anomaly simulation tests confirmed that an anomaly could be detected in a high-frequency band regardless of the concentration of foreign bodies, and that an anomaly could be detected regardless of the vibration measurement point [18]. Another case study is the anomaly diagnosis of traction motor bearings, where it was confirmed in stationary tests after applying to the leakage current of traction motors instead of bearing vibration that anomalies in bearings could be detected under specific no-load conditions [19].…”

Section: Anomaly Detection Methodsmentioning

confidence: 94%

<b>Research and Development Trends Related to Vehicle Technology at RTRI</b>

ISHIGE

2023

QR of RTRI

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The aim of this new division is to respond efficiently and promptly to the increasing sophistication and diversification of needs, such as the application of digital technology in vehicle technology, response to decarbonization, and overseas expansion including international standards. To this end, this division is conducting research and development by integrating mechanical and electrical systems. This paper gives an overview of the status of recent activities related to vehicle technology at RTRI.

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Section: Anomaly Detection Methodsmentioning

confidence: 94%

<b>Research and Development Trends Related to Vehicle Technology at RTRI</b>

ISHIGE

2023

QR of RTRI

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“…To prevent this, the traction motor fault detection system tracks the frequency of the traction motor using a phase-locked loop. Voltage and current signals are obtained by light sensor, converted into square wave signals by zerocrossing voltage comparator, output square wave signals by phase-locked loop function module, and trigger converter to obtain digital signals [24][25].…”

Section: Phase Locked Loop Modulementioning

confidence: 99%

Designing an Intelligent Decision Support System for Traction Motor Fault Detection using Artificial Intelligence

Huang

Wang

Chen

et al. 2023

Preprint

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Inverters and inductive instruments have been used with electric motors in recent years. They must be analyzed together to diagnose the inverter and induction machine properly. Inverter defects may affect application performance, increasing cost and diminishing efficiency. AI has improved defect detection in electric cars and industrial equipment traction motors. Safety and performance need efficient detection. Real-time monitoring and AI-powered decision assistance decrease operational disruptions. Analyzing massive sensor data reveals discrepancies for accurate predictions and preventive maintenance. Analysis of data and interactions notifies maintenance staff. This full strategy accelerates issue discovery, diagnosis, and maintenance. This work designs an artificial intelligence-based Multiple Electrical Units (MEU) for a fault detection system of traction motors to identify faults and ensure safe and stable operation reliably. This study first uses the system's optical fiber sensor, digital-to-analog converter module, system control unit, and phase-locked loop module to identify traction motor problems using artificial intelligence. Second, the optical fiber sensor collects the motor signal, and phase-locked loops monitor the traction motor's frequency. Third, improved cuckoo and difference analysis detects motor problems. The enhanced cuckoo algorithm can overcome the poor efficiency of motor fault identification, and the diverse analyses can minimize fundamental wave interference to a motor fault and combine their benefits to complete traction motor fault detection. The presented defect detection method performs well in experiments. The proposed approach improves motor defect detection accuracy, reduces error, and ensures MUE safety and stability.

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“…It is of great significance to ensure that the traction motor is free from accidents. However, the safe operation of the traction motor is seriously threatened by the heavy work condition and complex structure [1]. It has been found that almost half of motor failures originate from bearings [2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive neighborhood selection based on locally linear embedding for the degradation index construction of traction motor bearing

Zhang¹,

Yu²,

Liu³

et al. 2021

Meas. Sci. Technol.

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The degradation index construction is significant for bearing degradation assessment which ensures the reliability of machines. Statistical features extracted from vibration signals contain abundant information about the bearing operation state, but not all features have good characterization ability. Therefore, a multi-criteria weighted evaluation criterion is introduced to select features that can properly describe degradation assessment. To construct a more effective and reliable degradation index from the high-dimensional feature set, an adaptive neighborhood selection algorithm based on locally linear embedding (ANS-LLE) is proposed in this paper. The initial neighborhood parameters are determined based on cosine similarity analysis. Then neighborhood parameters are adjusted based on the analysis of sample distribution density and manifold curvature. The effectiveness of the proposed method is validated by an accelerated life experiment and a fault simulation experiment. The results show that the proposed method can effectively describe the bearing degradation process, and ANS-LLE performs better compared with comparison methods.

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<b>Fault Detection for Railway Traction Motor Bearing through Leakage Current</b>

Cited by 3 publications

References 11 publications

<b>Research and Development Trends Related to Vehicle Technology at RTRI</b>

<b>Research and Development Trends Related to Vehicle Technology at RTRI</b>

Designing an Intelligent Decision Support System for Traction Motor Fault Detection using Artificial Intelligence

Adaptive neighborhood selection based on locally linear embedding for the degradation index construction of traction motor bearing

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