Robust Detection and Estimation in Dynamic Systems and Statistical Signal Processing: Intersections, Parallel Paths and Applications

Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems∗

Robust probabilistic predictable feature analysis and its application for dynamic process monitoring

The model-based approach to fault detection in dynamic systems has been receiving more and more attention over the last two decades, in the contexts of both research and real plant application.KeywordsFuzzy LogicFault DetectionFault DiagnosisResidual EvaluationResidual GenerationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier and innovational outlier detection problems in dynamic process dataset. Compared with current outlier detection methods, the new method enjoys the following advantages: (a) no prior knowledge of the process model is needed; (b) it is easy to tune; (c) it can be applied to both univariate and multivariate outlier detection; (d) it is applicable to both on‐line and off‐line operation; (e) it cleans outliers while maintains the integrity of the original dataset. © 2014 American Institute of Chemical Engineers AIChE J, 61: 419–433, 2015

Robust Change Detection in Dynamic Systems with Model Uncertainties

In statistics and engineering the problem of change point detection in dynamical systems can be addressed using the class of sequential detection rules called limited generalized likelihood ratio (GLR) schemes. In this paper, some open problems concerning the statistical theory and practical implementation of these procedures are discussed. A method to select the window size and the threshold value, based on the joint use of Monte Carlo simulation and theoretical results, is proposed and illustrated through an example.

Effects of indicating failure odds and smoothed outputs on human failure detection in dynamic systems : Tzelgov, J., Tsach, U., and Sheridan, T.B.Ergonomics, 1985, 28.2, 449–462

Modified Schemes of Sequential Probability Ratio Test for Incipient Failure Detection in Dynamic Systems

This paper determines the theoretical limits of failure detectability in systems with modeling errors. Such errors are the main obstacle to accurate failure detection in aircraft, jet engines, power generation systems, and most other systems that include mechanical components and/or thermofluid processes. The paper then introduces the reachable measurement intervals (RMI) failure detection method that performs at the limit of detectability. RMI is capable of detecting the smallest theoretically detectable failures in systems with modeling errors, and is implementable in real time on microprocessors. The method is illustrated on a jet engine example. Nomenclature a — entry of the matrix A A = system matrix b = entry of the matrix B B = input matrix Bj = input matrix equal to the identity matrix C = output matrix C{ = output matrix for additional measurements D = direct transmission matrix H = Pontryagin's function K — matrix of gains for additional measurements L = vector of Lagrange multipliers m = entry of the matrix M M = matrix of Lagrange multipliers associated with uncertain entries of A n = entry of the matrix N N = matrix of Lagrange multipliers associated with uncertain entries of B p = discrete-time index corresponding to current time P = performance index for optimization q - vector of system parameters / = time t0 — current time ts = length of the processing window T - sampling time u = input vector x = state vector y = output vector z - vector of additional measurements Subscripts d = discrete e = uncertain and uncorrelated n = nominal v = uncertain

Walking activity and gait parameters are considered among the most relevant mobility-related parameters. Currently, gait assessments have been mainly analyzed in laboratory or hospital settings, which only partially reflect usual performance (i.e., real world behavior). In this study, we aim to validate a robust walking detection algorithm using a single foot-worn inertial measurement unit (IMU) in real-life settings. We used a challenging dataset including 18 individuals performing free-living activities. A multi-sensor wearable system including pressure insoles, multiple IMUs, and infrared distance sensors (INDIP) was used as reference. Accurate walking detection was obtained, with sensitivity and specificity of 98 and 91% respectively. As robust walking detection is needed for ambulatory monitoring to complete the processing pipeline from raw recorded data to walking/mobility outcomes, a validated algorithm would pave the way for assessing patient performance and gait quality in real-world conditions.Graphical Supplementary InformationThe online version contains supplementary material available at 10.1007/s11517-023-02826-x.

This paper analyzes the effect of using different feature selection algorithms for robust backscatter DDoS detection. To achieve this, we analyzed four different training sets with four different feature sets. We employed two well-known feature selection algorithms, namely Chi-Square and Symmetrical Uncertainty, together with the Decision Tree classifier. All the datasets employed are publicly available and provided by CAIDA. Our experimental results show that it is possible to develop a robust detection system that can generalize well to the changing backscatter DDoS behaviours over time using a small number of selected features.

The presence of a communication channel model in a feedback loop is known to impose a new class of fundamental limitations over its channel signal-to-noise ratio (SNR) when the discrete-time plant model is unstable. These type of fundamental limitations, in the presence of unstable plant poles, are worsened by non-minimum phase zeros (either from the plant or channel model) and plant time delay. In this paper we extend the analysis of such feedback systems by studying the effects of model uncertainties on the infimal SNR required to stabilize the plant. We consider here the additive coloured Gaussian channel (ACGN) model with memory, for which we then obtain the infimal SNR subject to plant, channel and noise model uncertainties. We observe that the infimal channel SNR is increased by these model uncertainties. Furthermore we use the main result to study the effect of channel model time delay.

Region duplication is one of the image-tampering techniques in which a part of image is copied and pasted into another region of the same image. In this paper, a robust duplication detection algorithm is proposed against severe degradations such as illumination changes, blurring, large scaling, noise contamination and JPEG compression. We introduce an adaptive phase correlation scheme in the log-polar domain, which is effective to locate the most salient local feature of an image patch on frequency band. By using the information collected from the band limitation, duplicated regions can be correctly located. Our contribution is to present a robust image duplication detection algorithm which can handle large scaling manipulation while preserving detection performance under other manipulations or degradations. We perform degradation and comparison test on various tampered images, and experimental results show that the proposed algorithm achieves satisfactory performance.

This work presents a robust speaker's location detection algorithm using a single linear microphone array that is capable of detecting multiple speech sources under the assumption that there exist nonoverlapped speech segments among sources. Namely, the overlapped speech segments are treated as uncertainty and are not used for detection. The location detection algorithm is derived from a previous work (2006), where Gaussian mixture models (GMMs) are used to model location-dependent and content and speaker-independent phase difference distributions. The proposed algorithm is proven to be robust against the complex vehicular acoustics including noise, reverberation, near-filed, far-field, line-of-sight, and non-line-of-sight conditions, and microphones' mismatch. An adaptive system architecture is developed to adjust the Gaussian mixture (GM) location model to environmental noises. To deal with unmodeled speech sources as well as overlapped speech signals, a threshold adaptation scheme is proposed in this work. Experimental results demonstrate high detection accuracy in a noisy vehicular environment.

Aims. We describe a fast, robust and automatic detection algorithm, TRUFAS, and apply it to data that are being expected from the CoRoT mission. Methods. The procedure proposed for the detection of planetary transits in light curves works in two steps: 1) a continuous wavelet transformation of the detrended light curve with posterior selection of the optimum scale for transit detection, and 2) a period search in that selected wavelet transformation. The detrending of the light curves are based on Fourier filtering or a discrete wavelet transformation. TRUFAS requires the presence of at least 3 transit events in the data. Results. The proposed algorithm is shown to identify reliably and quickly the transits that had been included in a standard set of 999 light curves that simulate CoRoT data. Variations in the pre-processing of the light curves and in the selection of the scale of the wavelet transform have only little effect on TRUFAS' results. Conclusions. TRUFAS is a robust and quick transit detection algorithm, especially well suited for the analysis of very large volumes of data from space or ground-based experiments, with long enough durations for the target-planets to produce multiple transit events.