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...

Most distance metric learning (DML) approaches focus on learning a Mahalanobis metric for measuring distances between examples. However, for particular feature representations, e.g., histogram features like BOW and SPM, Mahalanobis metric could not model the correlations between these features well. In this work, we define a nonMahalanobis distance for histogram features, via Expected Hitting T...

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 ...

In this study Jaccard Distance was performed by measuring the asymmetric information on binary variable and the comparison between vectors component. It compared two objects and notified the degree of similarity of these objects. After thorough preprocessing tasks; like translation, rotation, invariance scale content and noise resistance done onto the hand sketch object, Jaccard distance still ...

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...

In a fuzzy classifier with ellipsoidal regions, a fuzzy rule, which is based on the Mahalanobis distance, is defined for each class. Then the fuzzy rules are tuned so that the recognition rate of the training data is maximized. In most cases, one fuzzy rule per one class is enough to obtain high generalization ability. But in some cases, we need to partition the class data to define more than o...

The Mahalanobis-Taguchi System (MTS) is a data mining method employing Mahalanobis distance (MD) and Taguchi′s Robust Engineering philosophy to explore and exploit data in a multidimensional system. The MD calculation provides a measurement scale to discriminate sample data and gives an approach of measuring the level of severity among them. One unique feature of MTS lies its robustness to asse...

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...

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...