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An automated method integrating wavelet processing and techniques from multivariate statistical process control (MSPC) is presented, providing a means for the simultaneous localization, detection, and identification of disturbances in spectral data. A defining property of the wavelet transform is its ability to map a one-dimensional chemical spectrum into a two-dimensional function of wavelength and scale. Therefore, unlike the traditional MSPC approach where disturbance detection is carried out in the original wavelength domain by using a single principal component analysis (PCA) model, detection employing wavelet transform processing results in the generation of multiple models within the wavelength-scale domain. Provided that the spectral disturbance can be localized within a subregion of the wavelength-scale domain through an advantageous choice of basis set, the method described allows the identification of the underlying disturbance. The utility of the proposed method in localizing, detecting, and identifying spectral disturbances is demonstrated by using real near-infrared measurements, suggesting its general applicability in spectroscopic monitoring of chemical processes.

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A novel, automated method based on principal component analysis is presented for the detection and identification of disturbed sensors during a process monitoring application. As opposed to previous approaches, which are capable of identifying a fault in only a single sensor, the backward elimination sensor identification (BESI) algorithm is presented, which can identify upsets in multiple sensors. In the method, disturbed sensors are identified sequentially, or one at a time, using a residual-based criterion. The BESI algorithm is sensitive to changes in sensor correlations detected by conventional multivariate statistical process control but offers the ease of interpretation of conventional univariate methods. The BESI algorithm is successfully employed in the identification of disturbed sensors in simulated spectroscopic data and industrial process data. By examining the disturbance profiles generated over time by the BESI algorithm, it is also possible to distinguish between sensor and process disturbances.

A Dynamic Analysis Environment (DAE) software package is introduced to facilitate group inclusion/exclusion method testing, evaluation and comparison for pre-detonation nuclear forensics applications. Employing DAE, the multivariate signatures of a questioned material can be compared to the signatures for different, known groups, enabling the linking of the questioned material to its potential process, location, or fabrication facility. Advantages of using DAE for group inclusion/exclusion include built-in query tools for retrieving data of interest from a database, the recording and documentation of all analysis steps, a clear visualization of the analysis steps intelligible to a non-expert, and the ability to integrate analysis tools developed in different programming languages. Two group inclusion/exclusion methods are implemented in DAE: principal component analysis, a parametric feature extraction method, and k nearest neighbors, a nonparametric pattern recognition method. Spent Fuel Isotopic Composition (SFCOMPO), an open source international database of isotopic compositions for spent nuclear fuels (SNF) from 14 reactors, is used to construct PCA and KNN models for known reactor groups, and 20 simulated SNF samples are utilized in evaluating the performance of these group inclusion/exclusion models. For all 20 simulated samples, PCA in conjunction with the Q statistic correctly excludes a large percentage of reactor groups and correctly includes the true reactor of origination. Employing KNN, 14 of the 20 simulated samples are classified to their true reactor of origination.