Robust Change Detection in Dynamic Systems with Model Uncertainties

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

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

Punching shear is one of the possible failures that must be taken into consideration in designing concrete slabs and foundations. Several factors control the occurrence of this failure mechanism, such as slab thickness, concentrated loads, and the cross-section area of the supporting columns. The cross-section shape of columns may also affect the punching shear in flat slabs. Therefore, this research was specified to study the effect of the columns' cross-section shape on the punching shear in flat slabs by conducting a simulation analysis for two multi-story concrete buildings with a flat slab but with different structural stability systems, building layout, and column distribution, where five cross-section shapes (square, rectangular, circular, hexagonal, and octagonal) were tested. ETABS 2016 software was adopted for modeling and analysis to identify the optimum column shape leading to minimizing punching shear. Although the cross-section area and the reinforcement area of the columns were fixed while changing the models’ column shapes, it was found, in both buildings, that hexagonal columns performed better than others under punching shear and provided the lowest punching shear ratio. In contrast, circular columns gave the lowest resistance. Octagonal, square, and rectangular columns, respectively, follow the hexagonal columns in resisting punching shear.

Aside from relying on the robustness of the model predictive control (MPC) against model uncertainties, tracking performance degradation in the transient time is an inevitable challenge which is the main motivation of the adaptive MPC (AMPC). To that aim, in this study, a fast online identification scheme is suggested to identify a suitable linear state‐space model used in the control algorithm. Then, the authors prove the convergence of the identification error sequence using Lyapunov candidate function in the time domain and also, in the frequency domain (graph topology) point of view. Also, due to the dependency of the convergence error on the initial selection of the identification model, the authors introduce a stability and convergence ball as a function of generalised stability margin and the v‐gap criteria. Then, the stability and feasibility of the proposed AMPC is guaranteed when using the identified linear model at each sampling time. Moreover, the identification error convergence applies a constraint on the MPC in which, with a suitable selection of the update rate, MPC cost function is been relaxed from the constraint. In the simulations, the authors consider two examples of highly non‐linear dynamic systems. In both cases the proposed control strategy gives satisfactory conclusions.

Change detection methods aim to identify significantly changed areas in co-registered bitemporal images taken of the same area. Since not only do bitemporal images usually have different environmental conditions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> different weather conditions, noises, and seasonal changes) but also changes irrelevant to the purpose of change detection ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> road changes when detecting building change), which should not be detected as changed areas, change detection methods often suffer from the problem of pseudo-change detection. To alleviate this problem, we propose an encoder-decoder-based Siamese network with a channel-wise co-attention module that considers the channel-wise correlations between a feature map in one image and all feature maps in the other image. By comparing the feature map in one image with the revised feature map in the other image considering the correlations, we are able to reduce the differences between the feature maps when pseudo-changes exist, thereby rendering the proposed method more robust to pseudo-changes. In addition, we apply a contrastive loss function that encourages the pairs of feature maps corresponding to unchanged regions to be similar, which can help improve the performance of change detection. We verified the performance of the proposed method through experiments using datasets such as the change detection dataset (CDD) and building change detection dataset (BCDD). In the experiment, the proposed method achieved significantly improved performance compared with existing methods in terms of recall, precision, f1-score, and overall accuracy.

Uncertainty in structural dynamics: Experimental validation of a Wishart random matrix model

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.

Numerous system representation forms are first presented in this chapter, which allow us to model nominal dynamic systems both in the time and frequency domains. These types of models are then extended to address disturbances (unknown inputs) and to model different types of faults. Also model uncertainties are dealt with in this chapter. As a major tool for the subsequent studies in this book, the so-called coprime factorization technique is introduced.The second part of this chapter is dedicated to the application (case) examples and their mathematical models. They are: Speed control system of a DC motor, inverted pendulum control system, three-tank system, vehicle lateral dynamic system, and continuous stirred tank heater.KeywordsTransfer MatrixInverted PendulumState Space RepresentationActuator FaultLinear Time Invariant SystemThese 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.