Exploring real-time fault detection of high-speed train traction motor based on machine learning and wavelet analysis

2021 ◽  
Author(s):  
Yanshu Li
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Wavelet Analysis ◽  
Real Time ◽  
High Speed ◽  
High Speed Train ◽  
Traction Motor

Effect of unbalanced magnetic pull on the thermal characteristics of traction motor bearing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tingting Wang ◽  
Dongli Song ◽  
Weihua Zhang ◽  
Shiqi Jiang ◽  
Zhiwei Wang
Keyword(s):  
Thermal Analysis ◽  
High Speed ◽  
Thermal Characteristics ◽  
Expansion Method ◽  
Fourier Series Expansion ◽  
High Speed Train ◽  
Traction Motor ◽  
Content Type ◽  
Unbalanced Magnetic Pull ◽  
Motor Bearing

Purpose The purpose of this paper is to analyze the unbalanced magnetic pull (UMP) of the rotor of traction motor and the influence of the UMP on thermal characteristics of traction motor bearing. Design/methodology/approach The unbalanced magnetic pull on the rotor with different eccentricity was calculated by Fourier series expansion method. A bearing thermal analysis finite element model considering both the vibration of high-speed train caused by track irregularity and the UMP of traction motor rotor was established. The validity of the model is verified by experimental data obtained from a service high-speed train. Findings The results show that thermal failure of bearing subassemblies most likely occurs at contact area between the inner ring and rollers. The UMP of rotor of traction motor has a significant effect on the temperature of the inner ring and roller of the bearing. When the eccentricity is 10%, the temperature can even be increased by about 12°C. Therefore, the UMP of rotor of traction motor must be considered in thermal analysis of traction motor bearing. Originality/value In the thermal analysis of the bearing of the traction motor of high-speed train, the UMP of the rotor of the traction motor is considered for the first time

A Real-Time Information Service Platform for High-Speed Train

2012 ◽  
Vol 7 (9) ◽  
Author(s):  
Ruidan Su ◽  
Tao Wen ◽  
Weiwei Yan ◽  
Kunlin Zhang ◽  
Dayu Shi ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Information Service ◽  
High Speed Train ◽  
Service Platform ◽  
Real Time Information ◽  
Time Information

scholarly journals A Kind of Real-time Speed Measuring System of High Speed Train Based on Ethernet

2012 ◽  
Vol 29 ◽  
pp. 1218-1222
Author(s):  
Wang Qingmin ◽  
Su Mubiao ◽  
Liu Yuhong ◽  
Yang Yaoen
Keyword(s):  
Real Time ◽  
High Speed ◽  
Measuring System ◽  
High Speed Train

scholarly journals A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

2020 ◽  
Vol 10 (6) ◽  
pp. 6122
Author(s):  
Elmahdi Khoudry ◽  
Abdelaziz Belfqih ◽  
Tayeb Ouaderhman ◽  
Jamal Boukherouaa ◽  
Faissal Elmariami
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power System ◽  
Real Time ◽  
High Speed ◽  
Learning Algorithms ◽  
Time Estimation ◽  
Machine Learning Algorithms ◽  
Diagnosis System ◽  
Fault Diagnosis System

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

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