Monitoring and diagnostics of the technical condition of the asynchronous traction motor of locomotives using artificial neural networks on the railways of the Republic of Uzbekistan

2020 ◽  
Vol 17 (4) ◽  
pp. 514-524
Author(s):  
A. V. Grishchenko ◽  
◽  
О. R. Khamidov ◽  
Keyword(s):  
Neural Networks ◽  
Practical Importance ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Rolling Bearings ◽  
Frequency Spectra ◽  
Software Complex ◽  
Asynchronous Traction Motor ◽  
The Republic ◽  
Analysis Of Failures

Objective: Diagnostics of malfunctions of rolling bearings of an asynchronous traction electric motor (ATEM) of locomotives using artifi cial neural networks. Methods: To control and diagnose the technical condition of the ATEM bearing units of locomotives, a hardware-software complex and data analysis methods are used. Results: We investigated the malfunctions of the ATEM rolling bearing of locomotives. The analysis of failures of locomotive bearing units is carried out. Vibration and current signals and the corresponding frequency spectra of an ATEM operating under normal conditions and with various bearing faults are considered. A model for assessing the technical condition of rolling bearings of locomotives has been developed, and the importance of anticipatory diagnostics has been substantiated, which makes it possible to identify defects in advance at the earliest stage of their development. Practical importance: The results of the research can be used in the system for diagnosing the technical condition of rolling bearings of traction electric motors of locomotives in real time.

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

scholarly journals Vibration diagnostic of electric motor roller bearings

2019 ◽  
Vol 124 ◽  
pp. 02008 ◽  
Author(s):  
N. V. Hruntovich ◽  
N. V. Hruntovich ◽  
A. A. Kapanski ◽  
I. V. Petrov ◽  
E. E. Kostyleva
Keyword(s):  
Electric Motor ◽  
Power Plants ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Diagnostic Model ◽  
Rolling Bearings ◽  
Accuracy Class ◽  
Vibration Diagnostics ◽  
Bearing Defects ◽  
Asynchronous Motors

The low quality of the new rolling bearings leads to additional costs for electric motor repair, and additional expenses connected with the technological process, value of which can reach from several thousand to tens of thousand dollars. To increase detection, the reliability of rolling bearing defects of asynchronous motors, complex vibration diagnostics was used at informative frequencies in the vibration frequency and amplitude in the range of 5-5000 Hz. Based on the diagnostic model of rolling bearings the software program “Tayamnitsa” is developed, which allows to calculate the diagnostic frequency corresponding to certain defects, determine the defect level and form a diagnostic table for defects. Vibration diagnostics of new and used rolling bearings have been conducted for various regional enterprises and power plants. It has been determined that 40-50% of used bearings are removed in good technical condition. When new bearings were diagnosed in the 500-5000 Hz range, only 48.4% of the bearings are considered serviceable due to the low accuracy class of metal processing and unacceptably high vibration level.

Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 919-922 ◽  
Author(s):  
Yuan Yan Lin ◽  
Bin Wu Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Wavelet Neural Networks ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Bearing Fault Diagnosis ◽  
Fault Type

According to the fault type and fault signal of rolling bearing is difficult to predict, the paper proposed a new method to diagnose fault of rolling bearings with the wavelet neural network optimizated by simulated annealing particle swarm optimization. And it was applied to the fault diagnosis of rolling bearing. The experiment shows that this method can reduce the iteration time and improve the accuracy of convergence.

scholarly journals Locomotive asynchronous traction motor rolling bearing fault detection based on current intelligent methods

2021 ◽  
Vol 2131 (4) ◽  
pp. 042084
Author(s):  
O R Khamidov ◽  
A V Grishchenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Technical Condition ◽  
Traction Motors ◽  
Squirrel Cage ◽  
Asynchronous Traction Motor ◽  
Intelligent Methods

Abstract The paper is devoted to current issues of locomotive asynchronous traction motor (ATEM) fault detection using neural networks. Developed sophisticated intelligent methods for monitoring and inspecting the technical condition of ATE bearings. Current absorption spectra are analysed to assess the technical condition of the induction bearing units. The mechanical vibration frequencies of a squirrel cage induction motor are presented. The method of artificial neural networks which are universal approximators and can effectively and efficiently solve problems of monitoring and diagnostics of technical condition of locomotive induction traction motors is applied. A neural network model and framework for monitoring the technical condition of ATED bearings has been developed. They are based on rules and user-provided facts to recognise the situation, make a diagnosis, formulate a solution or make a recommendation. The main failures of the bearing units of squirrel cage ATED are analysed. A methodology has been developed to build a neural network model of the ATED. The structure and architecture of the artificial neural network is defined. An experimental research has been conducted. The results enable the determination of bearing faults in asynchronous traction motors with squirrel cage rotor.

ROLLING BEARING TECHNICAL CONDITION FORECASTING USING VIBRATION ACCELERATION OVERALL LEVEl

2020 ◽  
pp. 12-19
Author(s):  
Yu. V. Sarapulov ◽  
V. A. Sidorov ◽  
A. E. Sushko ◽  
R. A. Khasanov
Keyword(s):  
Practical Importance ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Normal Operation ◽  
Threshold Values ◽  
Vibration Acceleration ◽  
Extensive Experience ◽  
Corporate System ◽  
Vibration Parameters ◽  
Bearing Damage

Traditionally, the assessment of changes in the technical condition of individual components and mechanisms of rotation machines in industry is associated with trends analysis of various vibration parameters. Over the decades of using vibration analysis, we have accumulated extensive experience in faults locating and critically determining, however, it is the assessment of the remaining life that regulates the timing of maintenance and repair activities that is of great practical importance. This article uses the example of a pump unit rolling bearing to consider approaches to predicting the growsup stage of defects based on the analysis of values of vibration acceleration levels. The stages of normal operation and other stages of bearing damage are highlighted, threshold values are calculated and dependences of changes in diagnostic criteria for each stage of the life cycle are constructed. The obtained dependencies show results that are similar in general, but individual in their values, therefore, the accumulation of possible scenarios of events allows creating a knowledge base for predicting the behavior of a mechanical system. The necessary tools for multifactor forecasting were implemented within the SAFE PLANT software platform (LLC SPA DIATECH, Moscow) and are successfully applied at Uralkali PJSC for monitoring the technical condition of all technological equipment and managing MRO processes by integrating the results of diagnostics and forecast assessments into the ORACLE corporate system.

Neural networks within the systems of converter control of electric rolling stock

2016 ◽  
pp. 57-66
Author(s):  
Timofey Kochkar ◽  
Aleksandr Maznev
Keyword(s):  
Neural Networks ◽  
Frequency Control ◽  
Practical Importance ◽  
Comparison Method ◽  
Structure Design ◽  
Rolling Stock ◽  
Electric Rolling Stock ◽  
Asynchronous Traction Motor ◽  
Moment Control ◽  
Network Technologies

Objective: To improve the performance and accuracy of the system of voltage and frequency control for asynchronous traction motor (ATD) of rolling stock. Methods: To estimate the influence of artificial neural networks (INS) implementation to ATD control systems on the control indexes the comparison method was used. Results: The paper examines the possibility of INS implementation within the systems of direct control of traction electric motor moment. It also suggests a method of INS implementation as a ATD flow monitor. It provides the options of implementation and indicates the features of monitor structure design. Practical importance: Integration of the method of direct moment control and using of neural network technologies allows to improve the structure of converters and to improve the accuracy and the performance of output parameters, that, in its turn, decreases the power consumption, as well as losses in traction converters.

Development of software and hardware complex of operative diagnostics of structural part of 35-220 kV overhead lines operated in Northern regions of Western Siberia

2018 ◽  
pp. 69-77
Author(s):  
S. V. Grigorieva ◽  
A. V. Olshansky
Keyword(s):  
Western Siberia ◽  
Technical Condition ◽  
Structural Part ◽  
Software Complex ◽  
Overhead Lines ◽  
Far North ◽  
Hardware Complex ◽  
Northern Regions ◽  
Software And Hardware ◽  
Overhead Power Lines

The article is devoted to the problems of maintenance of overhead power lines in the Far North of Western Siberia. For improvement of quality and reliability of power supply of consumers in the conditions of the Far North of Western Siberia, decrease in operational costs and volumes of the carried-out emergency works the hardware-software complex for expeditious inspection, assessment and forecasting of change of technical condition of constructive part of air lines (VL) of 35-220 kV is developed, the structure of the hardware-software complex of registration of the condition of constructive part of VL of 35-220 kV and structure of the hardware-software complex of storage., processing and analysis of the obtained data on the States of the constructive part of the VL 35-220 kV. 

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