A Review and Analysis of the Mahalanobis—Taguchi System

Woodall, William H.; Koudelik, Rachelle; Tsui, Kwok Leung; Kim, Seoung Bum; Stoumbos, Zachary G.; Carvounis, Christos P.

doi:10.1198/004017002188618626

Cited by 122 publications

(71 citation statements)

References 17 publications

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“…MTS와 로지스틱 회귀의 성능 비교 X2, X3, X4, X6, X7, X8, X9, X10, X11, X12, X15, X17, X18, X19, X20, X22, X24, X25, X26, X27 (Taguchi et al, 2001;43-44 (Woodall et al, 2003). 이러 한 문제점을 해결하기 위한 한가지 방법으로 Kim et al(2009 …”

Section: Mahalanobis-taguchi System(mts) 기법unclassified

Performance Comparison of Mahalanobis-Taguchi System and Logistic Regression : A Case Study

Lee¹,

Lim²

2013

Journal of Korean Institute of Industrial Engineers

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The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set. †

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Section: Mahalanobis-taguchi System(mts) 기법unclassified

Performance Comparison of Mahalanobis-Taguchi System and Logistic Regression : A Case Study

Lee¹,

Lim²

2013

Journal of Korean Institute of Industrial Engineers

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“…The Taguchi methods have been extensively applied in industry, despite the criticisms and discussions raised by several statisticians (Woodall et al 2003;Su and Hsiao 2007;Huang et al 2009). …”

Section: Introductionmentioning

confidence: 98%

Applying the Mahalanobis-Taguchi strategy for software defect diagnosis

2011

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The Mahalanobis-Taguchi (MT) strategy combines mathematical and statistical concepts like Mahalanobis distance, Gram-Schmidt orthogonalization and experimental designs to support diagnosis and decision-making based on multivariate data. The primary purpose is to develop a scale to measure the degree of abnormality of cases, compared to "normal" or "healthy" cases, i.e. a continuous scale from a set of binary classified cases. An optimal subset of variables for measuring abnormality is then selected and rules for future diagnosis are defined based on them and the measurement scale. This maps well to problems in software defect prediction based on a multivariate set of software metrics and attributes. In this paper, the MT strategy combined with a cluster analysis technique for determining the most appropriate training set, is described and applied to well-known datasets in order to evaluate the fault-proneness of software modules. The measurement scale resulting from the MT strategy is evaluated using ROC curves and shows that it is a promising technique for software defect diagnosis. It compares favorably to previously evaluated methods on a number of publically available data sets. The special characteristic of the Autom Softw Eng (2012) 19:141-165 MT strategy that it quantifies the level of abnormality can also stimulate and inform discussions with engineers and managers in different defect prediction situations.

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“…VIPR uses the Mahalanobis-Taguchi System [8] to score the diagnostic frequency pattern data and optimize which frequencies to use within the Sorting Module. The Mahalanobis Distance [9] calculates the similarity of a given part to the central tendency of the known acceptable (good) population or the similarity to the central tendency of the known unacceptable (bad) population (Bias).…”

Section: Pcrt Analysis Methodsmentioning

confidence: 99%

Detection and quantification of creep strain using process compensated resonance testing (PCRT) sorting modules trained with modeled resonance spectra

Heffernan

Biedermann

Mayes

et al. 2018

AIP Conference Proceedings

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Process compensated resonance testing modeling for damage evolution and uncertainty quantification AIP Conference Proceedings 1806, 090005 (2017) Abstract. Process Compensated Resonant Testing (PCRT) is a full-body nondestructive testing (NDT) method that measures the resonance frequencies of a part and correlates them to the part's material and/or damage state. PCRT testing is used in the automotive, aerospace, and power generation industries via automated PASS/FAIL inspections to distinguish parts with nominal process variation from those with the defect(s) of interest. Traditional PCRT tests are created through the statistical analysis of populations of "good" and "bad" parts. However, gathering a statistically significant number of parts can be costly and time-consuming, and the availability of defective parts may be limited. This work uses virtual databases of good and bad parts to create two targeted PCRT inspections for single crystal (SX) nickel-based superalloy turbine blades. Using finite element (FE) models, populations were modeled to include variations in geometric dimensions, material properties, crystallographic orientation, and creep damage. Model results were verified by comparing the frequency variation in the modeled populations with the measured frequency variations of several physical blade populations. Additionally, creep modeling results were verified through the experimental evaluation of coupon geometries. A virtual database of resonance spectra was created from the model data. The virtual database was used to create PCRT inspections to detect crystallographic defects and creep strain. Quantification of creep strain values using the PCRT inspection results was also demonstrated.

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A Review and Analysis of the Mahalanobis—Taguchi System

Cited by 122 publications

References 17 publications

Performance Comparison of Mahalanobis-Taguchi System and Logistic Regression : A Case Study

Performance Comparison of Mahalanobis-Taguchi System and Logistic Regression : A Case Study

Applying the Mahalanobis-Taguchi strategy for software defect diagnosis

Detection and quantification of creep strain using process compensated resonance testing (PCRT) sorting modules trained with modeled resonance spectra

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