SYSTEM FOR DIAGNOSING THE TECHNICAL CONDITION OF TRACTION ELECTRIC MOTORS OF DIESEL LOCOMOTIVES CHME3 USING ARTIFICIAL NEURAL NETWORKS

The article contains an analysis of the order of forensic building-technical expertise and expert research to determine the reasons for the deterioration of the technical condition of the structural elements of buildings. The conditions for forming expert conclusions about the possible correlation between the appearance of negative changes in the technical condition of the structural elements that have become the subject of forensic building-technical expertise and the various factors of influence of the environment are investigated. In doing so, the focus is on the impact factors associated with carrying out renovation work in adjacent premises. In addition, issues related to the fuzzy uncertainty of the different nature of the expert researches are highlighted. Some of these problems are proposed to be solved by the using of artificial neural networks in the fuzzy subsystem of the system of support of forensic building-technical expertise. It is shown that a considerable part of the materials of forensic building-technical expertise and expert research is represented by photographs of injuries. Fixation of damaged structures is reflected in the plans of premises and schemes of placement of structures in the buildings. The graphic information of the research materials is accompanied by textual information, the processing of which requires the use of models and methods of fuzzy mathematics. The fragment of the knowledge base is provided, which contains information on the geometric parameters of damage to building structures and an example of a fuzzy rule that reflects an expert conclusion. The expediency of using fuzzy neural networks of adaptive resonance theory of the Cascade ARTMAP category is substantiated. Cascade ARTMAP memory card schematic is shown.

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Neural network technique when distribution of vehicle component parts on the technological repair routes, taking into account their technical condition

MATEC Web of Conferences ◽

10.1051/matecconf/201817005011 ◽

2018 ◽

Vol 170 ◽

pp. 05011

Author(s):

Valentin Krasovsky ◽

Nina Krasovskaya ◽

Victor Poptsov ◽

Irina Nordman

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Oil And Gas ◽

Technical Condition ◽

Probabilistic Methods ◽

Practical Application ◽

Repair Work ◽

Vehicle Component ◽

Repair Enterprise ◽

Artificial Neural

Increase of repair efficiency is achieved due to the formation of centralized specialized production facilities which implement the vehicle component parts repair technique with the use of industrial technological processes to restore the technical state of the units and their components. In this case, the establishment of the expediency of sending the unit to repair, as well as the defining of volumes and nomenclature for necessary repair actions, should be performed at the stage of pre-repair diagnosis for each individual unit taking into account its actual technical condition. However, the effectiveness of pre-repair diagnosis using both deterministic and probabilistic methods of processing and analyzing the information obtained is significantly reduced by the presence of errors in the recognition of defects and the distribution of aggregates in accordance with the repair work variety preformed at the repair enterprise. Using promising cognitive technology based on neural networks it is possible to completely avoid the losses associated with the repetition of repair work. Therefore, the formation of scientific and methodological bases for the development, training and practical application of artificial neural networks in the subsystems of the pre-repair diagnosis of the repair fund of automobile vehicle omponent parts is an important and urgent task. The paper presents the results of analytical studies and a number of original techniques for the formation of scientific and methodological foundations for the development, training and practical application of artificial neural networks in the process of diagnosis the car vehicle component parts and special oil and gas equipment entering the centralized repair according to their technical condition

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A Method of Predicting Wear and Damage of Pantograph Sliding Strips Based on Artificial Neural Networks

Materials ◽

10.3390/ma15010098 ◽

2021 ◽

Vol 15 (1) ◽

pp. 98

Author(s):

Małgorzata Kuźnar ◽

Augustyn Lorenc

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Current Collector ◽

Technical Condition ◽

Training Data ◽

Training Algorithms ◽

The Neural Network ◽

Artificial Neural ◽

The Impact

The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.

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Complex Identification Technique of Aircraft Gas Turbine Engine’s Health

Volume 1: Turbo Expo 2003 ◽

10.1115/gt2003-38704 ◽

2003 ◽

Cited By ~ 2

Author(s):

A. M. Pashayev ◽

R. A. Sadiqov ◽

P. S. Abdullayev

Keyword(s):

Neural Networks ◽

Fuzzy Logic ◽

Artificial Neural Networks ◽

Gas Turbine ◽

Soft Computing ◽

Technical Condition ◽

New Approach ◽

Identification Technique ◽

Artificial Neural

The new approach to identification of the aviation GTE technical condition is considered (examined) at an fuzzy, limitation and uncertainty of the information. This approach is based on applicability of fuzzy logic and artificial neural networks (Soft computing).

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