Locomotive Diesel Gas-Air Tract Diagnostics with the Use of Intellectual Classifier

Grachev, Vladimir; Fedotov, Mihail; Grischenko, Aleksandr; Bazilevskiy, Fedor; Sharapov, Aleksandr

doi:10.20295/2223-9987-2022-2-124-140

Bulletin of Scientific Research Results

2022

DOI: 10.20295/2223-9987-2022-2-124-140

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Locomotive Diesel Gas-Air Tract Diagnostics with the Use of Intellectual Classifier

Vladimir Grachev

Mihail Fedotov

Aleksandr Grischenko

et al.

Abstract: Purpose: Efficiency increase of operational monitoring of technical state of gas-air tract (GAT) of a locomotive diesel, assessment of possibility of using locomotive regular measuring tools to monitor diesel GAT technical state; feasibility justification for expanding the list of GAT parameters that are monitored by locomotive board diagnostics. Methods: An intelligent classifier based on Support Vector Machine (SVM) algorithm is used to determine the current technical condition (TC) class of GAT. Training sa… Show more

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Problems of diagnostics of modern diesel engines

Belyaev,

Kononov,

Krotov

2024

Bulletin of Scientific Research Results

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Objective: to consider the issue of diagnostics of modern diesel engines, in particular vibration diagnostics. Consider modern strategies and methods based on vibroacoustic signals that allow you to track and diagnose diesel engine malfunctions, as well as an assessment of the working process in the engine. To determine which means of measuring vibration parameters and methods of processing vibration signals can be considered the most reliable and informative. Propose to make adjustments to the currently used methods of processing vibration signals. Methods: comparison of the effectiveness of vibration measurement tools and mathematical methods of vibration signal processing. Results: the necessity of choosing mathematical methods of vibration signal processing is indicated, both for individual components of a diesel locomotive engine and for evaluating the workflow. Modern mathematical methods, applied to vibration diagnostics, require updating, due to an increase in computing power, as well as due to the development of powerful signal processing methods. To increase the reliability of the comparison results, it is necessary to take into account the large variability of the components and processes of the diesel engine. The need for additional study of modern mathematical methods of vibration signal processing is revealed. Practical importance: the necessity of introducing more modern methods of vibration signal processing is shown, which will optimize the durability of the components’ design using long operating cycles, reduce maintenance costs, track the service life of the internal combustion engine during the operation of the locomotive, improve the monitoring and diagnostics systems of the locomotive engine. The presented methods of assessing the use of certain methods of vibration diagnostics for diesel locomotives can be recommended for practical use.

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Problems of diagnostics of modern diesel engines

Belyaev,

Kononov,

Krotov

2024

Bulletin of Scientific Research Results

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Improving the Training Quality of Reference Diagnostic Models of Complex Technical Objects by Augmentation of Training Data

Grachev,

Fedotov

2023

Transport Automation Research

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One of the most serious problems limiting the possibility of using intelligent methods of processing diagnostic information in the tasks of diagnosing complex technical objects is the difficulty of forming a training sample for all classes of the state of the object in an amount sufficient for high-quality training of reference diagnostic models or classifiers, due to high absolute reliability indicators of such objects. An effective way to solve the problem is to augment (artificially expand) training data. A feature of training samples in technical diagnostics tasks is the generally unknown type of their distribution in the space of features, while additional "synthetic" data should be distributed similarly to the actual training set to ensure high-quality training of the diagnostic model. As a result of the analysis of existing data augmentation methods, it was established that the possibility of determining data distribution parameters of the training sample in the course of training with subsequent reproduction of these parameters in the generated samples can be implemented in generative models based on variational autoencoders (VAE) and generative-adversarial networks (GAN). At the same time, the best results are achieved using GAN. In the tasks of intelligent classification of the state of a diagnostic object with marked training samples for generating additional data, it is preferable to use conditional GAN (CGAN). A serious problem that arises in solving practical problems related to the generation of additional data on the available sample (training sample of a small volume) is the assessment of the uniformity of the training and generated samples, the results of which determine the duration (number of eras) of the training process of the generative model. The paper proposes and substantiates an original method of estimating uniformity of multidimensional samples based on Ripley’s G and F functions used in spatial cluster analysis of point processes. Based on it, a quantitative indicator has been determined for quality control and training duration of the generative model. The efficiency of the proposed method is confirmed by the example of solving the problem of augmentation of training data for the reference diagnostic model of the gas-air path of a diesel locomotive.

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Analysis of UMAP, the method for reducing the dimensionality of initial data in machine learning for the purpose of failure prediction in a motive power service

Проневич

Klokova

2022

Nadežnostʹ

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Aim. Feature transformation is one of the stages of machine learning application that has a significant effect on the quality of regression models. The paper aims to develop criteria for evaluating the quality of data dimensionality reduction at the stage of feature transformation and adaptation of the UMAP method to the problem of prediction of the number of days to failure in the locomotives of JSC RZD. Methods. The data transformation methods are divided into two groups, those that attempt to preserve the global data structure, and those that attempt to preserve the distances between points. The paper examines in detail the UMAP no-linear method of dimensionality reduction, whose low-dimensional data presentation is based on a transformation of a nearest neighbour graph retaining the data structure. The structure of the initial data manifold is examined using topological data analysis and simplified fuzzy set construction methods. Results. The analysis of UMAP theory conducted in the Russian language for the first time enabled a substantiated identification of the three primary parameters of the method, whose variation significantly affects the type of data obtained as the result of a transformation. In particular, that pertains to the quality of class separation over a two-dimensional space. Additionally, the characteristics of the input set of parameters were identified that affect the UMAP results. Practical results of UMAP application weredemonstrated. Intermediate results included a list of nearest neighbours, a weighted graph of nearest neighbours. The fundamental result is a low-dimensional data representation (out of 44 initial measurements) over a two-dimensional space with class separation, which is confirmed both by calculations, and visually. Conclusions. It was identified that UMAP is an efficient and substantiated method of dimensionality reduction that allows – through parameter variation – transforming data in such a way as to improve the quality of data submitted to machine learning models by the criterion of “evident class separation”. The transformation is an intermediate stage of data preparation for regression model application, and class separation was performed for the purpose of eliminating the probability of gross regression errors.

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Locomotive Diesel Gas-Air Tract Diagnostics with the Use of Intellectual Classifier

Cited by 3 publications

References 1 publication

Problems of diagnostics of modern diesel engines

Problems of diagnostics of modern diesel engines

Improving the Training Quality of Reference Diagnostic Models of Complex Technical Objects by Augmentation of Training Data

Analysis of UMAP, the method for reducing the dimensionality of initial data in machine learning for the purpose of failure prediction in a motive power service

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