Optimization of the norms of overhaul runs for traction electric motors of locomotives based on the results of the distribution of failures at the range of the Far Eastern Railway

Objective: Optimization of the rates of overhaul runs on the basis of the results of the study of the distribution of failures of traction motors caused by a critical decrease in the insulation resistance of the windings to reduce the cost of eliminating the consequences of unscheduled repairs of locomotives. Methods: Mathematical statistics, probability theory, theory of reliability of technical systems are applied. Results: A cause-and-effect analysis was carried out of cases when locomotives were placed for unscheduled repairs in 2019–2020. An element has been determined that limits the rates of overhaul mileage of traction electric motors of locomotives. The theoretical distribution of failures of traction electric motors due to a critical decrease in the insulation resistance of its windings is obtained. The optimization of the norms of overhaul runs of locomotives was carried out according to the conditions of current repairs in the amount of TR-2 relative to traction electric motors. It was revealed that the failures are mainly associated with the first period of operation of traction motors. As a result of the analysis of the reasons for the failure of traction electric motors, it was found that most cases of unscheduled repairs fall on the first interval of locomotive runs due to the poor quality of repairs. In this case, the first normal peak of failures falls on the mileage in the modal interval from 156 to 234 thousand kilometers, when the overhaul mileage on TP-2 is 250 thousand kilometers, which necessitates its optimization within the preceding modal interval. Practical importance: The presented version of optimization of overhaul runs can be used to determine the rationality of establishing the norms for the periods of maintenance and repair operations on locomotive units under the conditions of a planned preventive control system for their technical condition

Marks: A New Interval Tool for Uncertainty, Vagueness and Indiscernibility

The system of marks created by Dr. Ernest Gardenyes and Dr. Lambert Jorba was first published as a doctoral thesis in 2003 and then as a chapter in the book Modal Interval Analysis in 2014. Marks are presented as a tool to deal with uncertainties in physical quantities from measurements or calculations. When working with iterative processes, the slow convergence or the high number of simulation steps means that measurement errors and successive calculation errors can lead to a lack of significance in the results. In the system of marks, the validity of any computation results is explicit in their calculation. Thus, the mark acts as a safeguard, warning of such situations. Despite the obvious contribution that marks can make in the simulation, identification, and control of dynamical systems, some improvements are necessary for their practical application. This paper aims to present these improvements. In particular, a new, more efficient characterization of the difference operator and a new implementation of the marks library is presented. Examples in dynamical systems simulation, fault detection and control are also included to exemplify the practical use of the marks.

Predicting temperature curve based on fast kNN local linear estimation of the conditional distribution function

Predicting the yearly curve of the temperature, based on meteorological data, is essential for understanding the impact of climate change on humans and the environment. The standard statistical models based on the big data discretization in the finite grid suffer from certain drawbacks such as dimensionality when the size of the data is large. We consider, in this paper, the predictive region problem in functional time series analysis. We study the prediction by the shortest conditional modal interval constructed by the local linear estimation of the cumulative function of $Y$ given functional input variable $X$. More precisely, we combine the $k$-Nearest Neighbors procedure to the local linear algorithm to construct two estimators of the conditional distribution function. The main purpose of this paper is to compare, by a simulation study, the efficiency of the two estimators concerning the level of dependence. The feasibility of these estimators in the functional times series prediction is examined at the end of this paper. More precisely, we compare the shortest conditional modal interval predictive regions of both estimators using real meteorological data.

Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty

Complex systems are usually affected by various sources of uncertainty, and it is essential to account for mechanisms that ensure the proper management of such disturbances. This paper introduces a novel approach to solve symbolic regression problems, which combines the potential of Grammatical Evolution to obtain solutions by describing the search space with context-free grammars, and the ability of Modal Interval Analysis (MIA) to handle quantified uncertainty. The presented methodology uses an MIA solver to evaluate the fitness function, which represents a novel method to manage uncertainty by means of interval-based prediction models. This paper first introduces the theory that establishes the basis of the proposed methodology, and follows with a description of the system architecture and implementation details. Then, we present an illustrative application example which consists of determining the outer and inner approximations of the mean velocity of the water current of a river stretch. Finally, the interpretation of the obtained results and the limitations of the proposed methodology are discussed.

Modal Interval Probability: Application to Bonus-Malus Systems

Classical intervals have been a very useful tool to analyze uncertain and imprecise models, in spite of operative and interpretative shortcomings. The recent introduction of modal intervals helps to overcome those limitations. In this paper, we apply modal intervals to the field of probability, including properties and axioms that form a theoretical framework applied to the Markovian analysis of Bonus-Malus systems in car insurance. We assume that the number of claims is a Poisson distribution and in order to include uncertainty in the model, the claim frequency is defined as a modal interval; therefore, the transition probabilities are modal interval probabilities. Finally, the model is exemplified through application to two different types of Bonus-Malus systems, and the attainment of uncertain long-run premiums expressed as modal intervals.

Statics-Based Model-Free Damage Detection under Uncertainties Using Modal Interval Analysis

Deterministic damage detection methods often fail in practical applications due to ever-present uncertainties. Moreover, vibration-based model updating strategies are easily affected by measurement noises and could encounter ill-conditioning problems during inverse solutions. On this account, a model-free method has been proposed combining modal interval analyses with static measurements. Structural geometrical dimensions, material parameters and external loads are expressed by interval variables representing uncertainties. Mechanical formulas for static responses are then extended to their interval forms, which are subsequently solved using classic interval and modal interval analyses. The analytical interval envelopes of static responses such as deflections and strains are defined by the interval solutions, and damage can be detected when the measured responses intersect the envelopes. By this approach, potential damage can be found in a fast and rough way without any inverse solution process such as model updating. The proposed method has been verified against both numerical and experimental reinforced concrete beams whose strains were taken as the desirable responses. It was found that the strain envelopes provided by modal interval analysis were narrower than those by classic interval analysis. Modal interval analysis effectively avoids the phenomenon of interval overestimation. In addition, the intersection point also identifies the current external load, providing a loading alarm for structures.