MOST CITED
Update: 2021-07-01
Task „Implementation of procedures ensuring the originality of scientific papers published in the quarterly „Eksploatacja i Niezawodność – Maintenance and Reliability” financed under contract 532/P-DUN/2018 from the funds of the Minister of Science and Higher Education for science dissemination activities.
Tomasz Szolc
Identification of safety hazards and operating conditions of the low-floor tram with independently rotating wheels with various drive control algorithms
The aim of the article is to develop a method for the analysis of tram dynamics related to safety during operation. To achieve this, a mathematical model of the vehicle represented by a multibody simulation MBS system is used. Models of tram with a classic and innovative drive, based on a system of independently rotating wheels on crank axles are analyzed. A new configuration of an innovative drive control of the considered vehicle with the use of braking of independent wheels is proposed. A new geometry of test track is presented. During numerical investigation the values of ‘Y’ leading forces of tram wheels with the considered innovative drive proved to be lower than in the corresponding vehicle with standard wheelsets. It has been demonstrated that the active control systems are of key importance and should be applied in such innovative tram drives.
Predicting length of fatigue cracks by means of machine learning algorithms in the small-data regime
In this paper several statistical learning algorithms are used to predict the maximal length of fatigue cracks based on a sample composed of 31 observations. The small-data regime is still a problem for many professionals, especially in the areas where failures occur rarely. The analyzed object is a high-pressure Nozzle of a heavy-duty gas turbine. Operating parameters of the engines are used for the regression analysis. The following algorithms are used in this work: multiple linear and polynomial regression, random forest, kernel-based methods, AdaBoost and extreme gradient boosting and artificial neural networks. A substantial part of the paper provides advice on the effective selection of features. The paper explains how to process the dataset in order to reduce uncertainty; thus, simplifying the analysis of the results. The proposed loss and cost functions are custom and promote solutions accurately predicting the longest cracks. The obtained results confirm that some of the algorithms can accurately predict maximal lengths of the fatigue cracks, even if the sample is small.