A method of robust multivariate outlier replacement

Robust multivariate methods for dealing with problems caused by outliers in the data are essential especially when process data are used to validate mechanistic models, develop regression models, and in applications such as controller design and process monitoring. Gross outliers are detected easily by simple methods such as range checking, however, a multivariate outlier is very difficult to discern and techniques that rely on data to generate empirical models may produce erroneous results. In this work, a methodology to perform multivariate outlier replacement in the score space generated by principal component analysis (PCA) is proposed. The objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for monitoring and fault detection and identification. The methodology uses the concept of winsorization to provide robust estimates of the mean (location) and S.D. (scale) iteratively, yielding a robust set of data. The paper develops the approach, discusses the concept of robust statistics and winsorization, and presents the procedures for robust multivariate outlier filtering. One simulated and two industrial examples are provided to demonstrate the approach. (C) 2002 Elsevier Science Ltd. All rights reserved.