Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

A Global Expectation–Maximization Approach Based on Memetic Algorithm for Vibration-Based Structural Damage Detection

Abstract: This paper proposes a novel unsupervised damage detection approach based on a memetic algorithm that establishes the normal or undamaged condition of a structural system as data clusters through a global xpectation–maximization technique, using only damage-sensitive features extracted from output-only vibration measurements. The health state is then discriminated by considering the Mahalanobis squared distance between the learned clusters and a new observation. The proposed approach is compared with state-of-the-art ones by taking into account real-world data sets from the Z-24 Bridge (Switzerland), where several damage scenarios were performed. The results indicated that the proposed approach can be applied in structural health monitoring applications where life safety, economic, and reliability issues are the most important motivations to consider.

A program for calculating Mahalanobis distances using principal component analysis

Abstract: A technique for pattern recognition analysis of near-infrared reflectance spectra is described. Classification of samples is achieved by using the Mahalanobis distances of spectra in a principal component subspace. Probability levels for class membership are determined from the Chi-squared distribution.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

Abstract: This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Energy theft detection in advanced metering infrastructure

Abstract: Energy theft is one of the key concern in Advanced Metering Infrastructure (AMI). In developed and developing countries, the financial losses due to energy theft are billions of dollar per year. In this paper, we have proposed a Principal Component based Theft Detection scheme to detect energy theft in AMI. Principal components have been found using consumers' consumption data. Mahalanobis distance is calculated between transformed testing samples and historical consumption data. If the Mahalanobis distance is outside the predefined threshold range, the testing sample is considered as malicious. The proposed method is tested under different attack scenarios using real smart meter data. The experimental results show that the proposed scheme detects energy theft attacks with high detection rate.