Detection of Mean Shifts by Statistical Approacches. Part 2: Context of Non-Gaussian or Autocorrelated Observations

This article is the second part of a global overview of the research on the detection of mean shifts through statistical techniques. Various categories of control charts used in the field of Statistical Process Control (SPC) are reviewed in the particular context represented by autocorrelated observations or distributed under non-Gaussian distributions

Detection of Mean Shifts by Statistical Approacches. Part 1: Context of Univariate Independent Normally Distributed Observations

This article is intended to be a first part of a global overview of the research on the detection of mean shifts through statistical techniques. The paper provides a description of the main developments over time in the field of statistical process control and more particularly in the construction of control charts. Various categories of univariate control charts that have emerged in research are reviewed in the particular context represented by independent observations and distributed under the normal distribution.

An exploratory study of the Multidimensional Health Locus of Control among female students in a French context

Background: The aim of this study is to test with an exploratory approach the relevance of the 3 MHLC sub-scales while examining the answers of a group of 61 female students in a French context. The Locus of control is acknowledged to be a personality variable which proves to be a good predictor of health problems according to its internal or external orientation. Methods: In order to study the relationships between the different items of the 3 sub-scales and the way the students’ answers are configured, we performed a multiple correspondence analysis (MCA) and we grouped the female students by using a hierarchical cluster analysis (HCA).   Results: Analysis with MCA clearly reveals the 3 subscales and within our sample 4 classes of female students are identified by means of HCA. Conclusion: While confirming the relevance of the 3 MHLC subscales, this study shows that in this sample of French female students, 4 personality profiles appear with differentiated perceptions of control

Sciences humaines et ingénierie : application dans le champ routier

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Fault detection and identification with a new feature selection based on mutual information

This paper presents a fault diagnosis procedure based on discriminant analysis and mutual information. In order to obtain good classification performances, a selection of important features is done with a new developed algorithm based on the mutual information between variables. The application of the new fault diagnosis procedure on a benchmark problem, the Tennessee Eastman Process, shows better results than other well known published methods

Distance rejection in a bayesian network for fault diagnosis of industrial systems

The purpose of this article is to present a method for industrial process diagnosis with Bayesian network. The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a bayesian network in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault