The Mahalanobis Taguchi System is a diagnosis and forecasting method for multivariate data. Mahalanobis distance is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. The Mahalanobis Taguchi System is of interest because of its reported accuracy in forecasting small, correlated data sets. Th...

The Mahalanobis Taguchi System (MTS) is a diagnosis and forecasting method for multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and different patterns that can be identified and analyzed with respect to a base or reference group. MTS is of interest because of its reported accuracy in forecasting small, correlated data sets. This is the type o...

We present a method allowing a signiicant speed-up of the eigen-detection method (detection based on principle component analysis). We derive a formula for an upper bound on the class-conditional probability (or, equivalently, a lower bound on the Mahalanobis distance) on which detection is based. Often, the lower bound of Mahalanobis distance (MD) reaches a preset threshold after computation o...

Distance-based supervised pattern recognition method (PRM) – discriminant analysis (DA) in principal component (PC) space described by Mahalanobis distance (MD) was suggested for definition of batch-to-batch dispersion of 22 drugs, produced by 14 manufacturers. The range of under test medicines numbered 18 pharmacological classes, including biologics, microbiological drugs and combined drugs, c...

This paper presents a novel approach for health monitoring of electronic products using the Mahalanobis distance (MD) and Weibull distribution. The MD value is used as a health index, which has the advantage of both summarizing the multivariate operating parameters and reducing the data set into a fused distance index. The Weibull distribution is used to determine health decision metrics, which...

Today, real-time fault detection and predictive maintenance based on sensor data are actively introduced in various areas such as manufacturing, aircraft, power system monitoring. Many faults motors or rotating machinery like industrial robots, aircraft engines, wind turbines can be diagnosed by analyzing signal vibration noise. In this study, to detect failures data, preprocessing was performe...

Purpose – This paper seeks to present a prognostics approach using the Mahalanobis distance (MD) method to predict the reliability of multilayer ceramic capacitors (MLCCs) in temperature-humidity-bias (THB) conditions. Design/methodology/approach – Data collected during THB testing of 96 MLCCs were analyzed using the MD method. In the THB tests, three parameters (capacitance (C), dissipation fa...

Abstract Motivated by establishing theoretical foundations for various manifold learning algorithms, we study the problem of Mahalanobis distance (MD) and associated precision matrix estimation from high-dimensional noisy data. By relying on recent transformative results in covariance estimation, demonstrate sensitivity MD to measurement noise, determining exact asymptotic signal-to-noise ratio...

With increasing complexity in electronic systems there is a need for system level anomaly detection and fault isolation. Anomaly detection based on vector similarity to a training set is used in this paper through two approaches, one the preserves the original information, Mahalanobis Distance (MD), and the other that compresses the data into its principal components, Projection Pursuit Analysi...

Analog filters play a very important role in insuring the availability of electronic systems. Early detection of anomalies of analog filters can prevent the impending failures and enhance reliability. The complex architecture and the tolerances of multiple components make it very difficult to detect anomalies in analog filters. To address this concern, A Mahalanobis distance (MD) based anomaly ...