scholarly journals LUNA Condition-Based Monitoring Update: Mahalanobis Distance for Excess Load and External Leak Cases

2021 ◽  
Author(s):  
Andre Green
Keyword(s):  
Mahalanobis Distance ◽  
Condition Based Monitoring ◽  
Excess Load

Development of Fault Diagnosis Classification Method System using Mahalanobis Distance in Micro Wind Turbine

2015 ◽  
Vol 135 (9) ◽  
pp. 577-578
Author(s):  
Toshio Bouno ◽  
Shinichi Arimori ◽  
Toshifumi Yuji ◽  
Hiroyuki Kinoshita
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Mahalanobis Distance ◽  
Classification Method ◽  
Diagnosis Classification

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

Research on the application of Mahalanobis distance in catastrophic faults detection of analog circuits

2009 ◽  
Vol 2009 (7) ◽  
pp. 41-45 ◽  
Author(s):  
Qingqiang Qin ◽  
Xiaoan Zhang ◽  
Aihua Li ◽  
Zhao Yang
Keyword(s):  
Analog Circuits ◽  
Mahalanobis Distance ◽  
Catastrophic Faults

Financial Applications of the Mahalanobis Distance

2013 ◽  
Author(s):  
Sebastian Stöckl ◽  
Michael Hanke
Keyword(s):  
Mahalanobis Distance ◽  
Financial Applications

An AERONET-based aerosol classification using the Mahalanobis distance

2016 ◽  
Vol 140 ◽  
pp. 213-233 ◽  
Author(s):  
Patrick Hamill ◽  
Marco Giordano ◽  
Carolyne Ward ◽  
David Giles ◽  
Brent Holben
Keyword(s):  
Mahalanobis Distance ◽  
Aerosol Classification

scholarly journals Asymptotic Inference for Optimal Rerandomization Designs

2021 ◽  
Vol 1 (1) ◽  
pp. 49-58
Author(s):  
Mårten Schultzberg ◽  
Per Johansson
Keyword(s):  
Experimental Design ◽  
Normal Distribution ◽  
Mahalanobis Distance ◽  
Design Strategy ◽  
Sampling Distribution ◽  
Optimal Designs ◽  
Asymptotic Inference ◽  
Optimal Allocations ◽  
Asymptotic Sampling

AbstractRecently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.

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