Masters Theses
Keywords and Phrases
Condition Monitoring; Mahalanobis Distance
Abstract
"Classification and forecasting are useful concepts in the field of condition monitoring. Condition monitoring refers to the analysis and monitoring of system characteristics to understand and identify deviations from normal operating conditions. This can be performed for prediction, diagnosis, or prognosis or a combination of any these purposes. Fault identification and diagnosis are usually achieved through data classification, while forecasting methods are usually used to accomplish the prediction objective. Data gathered from monitoring systems often consists of multiple multivariate time series and is fed into a model for data analysis using various techniques. One of the data analysis techniques used is the Mahalanobis-Taguchi strategy (MTS) because of its suitability for multivariate data analysis. MTS provides a means of extracting information in a multidimensional system by integrating information from different variables into a single composite metric. MTS is used to conduct analysis on the measurement parameters and seeks a correlation with the result while also seeking to optimize the analysis by identifying variables of importance strongly correlated with a defect or fault occurrence. This research presents the application of a MTS based system for predicting faults in heavy duty vehicles and the application of MTS in a multiclass classification problem. The benefits and practicality of the methodology in industrial applications are demonstrated through the use of real world data and discussion of results."--Abstract, page iv.
Advisor(s)
Cudney, Elizabeth A.
Committee Member(s)
Corns, Steven
Smith, Brian Keith
Department(s)
Engineering Management and Systems Engineering
Degree Name
M.S. in Systems Engineering
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2014
Journal article titles appearing in thesis/dissertation
- A review of literature on Mahalanobis-Taguchi strategy in condition monitoring
- Predicting faults in heavy duty vehicles using the Mahalanobis-Taguchi strategy
- Mahalanobis-Taguchi system for multiclass classification of steel plates fault
Pagination
67 pages
Note about bibliography
Includes bibliographical references.
Rights
© 2014 Adebolaji A. Jobi-Taiwo, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Machinery -- Monitoring -- Data processingCommercial vehicles -- Monitoring -- Data processingTaguchi methods (Quality control)Multivariate analysis
Thesis Number
T 10453
Electronic OCLC #
882478844
Recommended Citation
Jobi-Taiwo, Adebolaji A., "Data classification and forecasting using the Mahalanobis-Taguchi method" (2014). Masters Theses. 7248.
https://scholarsmine.mst.edu/masters_theses/7248