Alexis Todoskoff

Reliability assessment of mechatronic systems: operating field data analysis

The reliability analysis of complex mechatronic systems is a very important engineering issue, in order to guarantee their functional behavior. We propose the evaluation of mechatronic systems reliability using censored data for operating field for different technologies, e.g. mechanics, electronics, and software. For ultra reliable system for which we have little data, we use an estimation method, stochastic expectation maximization (SEM), to increase the evaluation accuracy. The SEM method is used to estimate the reliability parameters with better accuracy than with maximization likelihood method.

Multivariate analysis of human behavior data using fuzzy windowing: Example with driver-car-environment system

In most human component system studies performed in simulators, several factors (or independent variables) (at least two, i.e., individual and time) and many variables (or dependent variables) are present. Large and complex databases have to be analyzed. Instead of using rather automatic procedures, this article suggest that, for a very first analysis at least, the human being must be present and he/she must choose a method being adapted to the data, which is different to run a method supposing that the data fit such or such model. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.

Dependability analysis of complex mechatronic systems

Abstract The dependability analysis of complex mechatronic systems is a very important engineering issue, in order to guarantee their functional behavior. Most of the critical failures are generated by the interactions between the sub-systems, implemented in different technologies, e.g. mechanics, electronics, and software. Therefore, the analysis of the system as a whole is not enough and it becomes necessary to study all the interactions in order to estimate the systems dependability.

MC/DC Test Case Generation Approaches for Decisions

Modified Condition/Decision Coverage (MC/DC) is a structural coverage criterion that aims to prove that all conditions involved in a Boolean expression (decision) can influence the result of that expression. In the context of aeronautic and automotive, MC/DC is highly recommended and even required for most critical applications structural coverage. However, due to complex decision that are often embedded in those applications, generating a set of MC/DC compliant test cases for any of these decisions is a non trivial and time consuming task for testers. In this paper we present an early work of an approach to automatically generate MC/DC test cases for different kinds of decisions. Thus, we introduce three different techniques to deal with MC/DC test case generation for decisions.

Detection of Evolutions in the Driver Behaviour Using Reliability Centred Maintenance Methods

In this paper, the authors look at the application of reliability centered maintenance techniques in detecting the time evolution of a drivers behavior in a simulated driving task of long duration. They describe the problems that are raised by these systems and the involvement of a human operator in the control loop. They then propose a solution which uses exploratory data analysis techniques.

A novel adaptable approach for sentiment analysis on big social data

Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis in order to accompany big events by tracking people’s behavior. In this paper, we propose an adaptable sentiment analysis approach that analyzes social media posts and extracts user’s opinion in real-time. The proposed approach consists of first constructing a dynamic dictionary of words’ polarity based on a selected set of hashtags related to a given topic, then, classifying the tweets under several classes by introducing new features that strongly fine-tune the polarity degree of a post. To validate our approach, we classified the tweets related to the 2016 US election. The results of prototype tests have performed a good accuracy in detecting positive and negative classes and their sub-classes.

Big Data Analytics: A Comparison of Tools and Applications

With an ever-increasing amount of both data volume and variety, traditional data processing tools became unsuitable for the big data context. This has pushed toward the creation of specific processing tools that are well aligned with emerging needs. However, it is often hard to choose the adequate solution as the wide list of available tools are continuously changing. For this, we present in this paper both a literature review and a technical comparison of the most known analytics tools in order to help mapping it to different needs. Moreover, we underline how much important choosing the appropriate tool is acting for different kind of applications and especially for smart cities environment.

Using Multiple Correspondence Analysis for large driving signals database exploration. Example with lane narrowing and curves

In most driving studies, several factors (at least two, i.e. individual and time) and many variables are collected via multidimensional signals (MS). This article suggests starting the analysis while keeping the three main aspects of time, i.e. simultaneity, chronology and duration. To achieve this aim, with the possibility to show nonlinear relationships, a MS set exploratory investigation is performed using the pair space-time windowing/Multiple Correspondence Analysis. This article shows how intra and inter-individual differences can be underscored.

Suitability of Multiple Correspondence Analysis for a database exploration before inference analysis. Example with a driving simulator based study.

Abstract In most human component system studies performed in simulators, several factors (at least two, i.e. individual and time) and many variables are present. This article suggests starting the analysis while keeping both the multifactorial (MF) and multivariate (MV) aspects. To achieve this aim, with the possibility to show nonlinear relationships, a MFMV exploration of the experimental database is performed using the pair (fuzzy space windowing, Multiple Correspondence Analysis). Then may come an inference analysis. This long (due to multiple large graphical views) but rich procedure is illustrated and discussed using a car driving study example.

Reliability Estimation of Mechanical Components Using Accelerated Life Testing Models

This chapter presents an overview of using accelerated life testing (ALT) models for reliability estimation on mechanical components. The reliability is estimated by considering two test plans: a classical one testing a sample system under accelerated conditions only and a second plan with previous accelerated damage. The principle of the test plan with previous accelerated damage is testing the sample under step-stress. In the beginning (until time N 1), the sample is tested under stress s 1 (accelerated testing: \(s_{1}>s_{0}\)); when the tested units have used many of their “resources,” the stress s 1 is replaced by the operating conditions s 0 (until the time N 2). Therefore, failure times under the accelerated conditions can be used to estimate reliability function in operating conditions. The time transformation function is considered as log-linear and four types of estimation are studied: parametric, Extended Hazard Regression (GPH), semi-parametric, and nonparametric models. The chapter is illustrated by a simulation example of ball bearings testing. The results are used to analyze and compare these estimation methods. The simulations have been performed both with censored data and without censoring, in order to examine the asymptotic behavior of the different estimates.