This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, possibly as multiple disjoint continuum ranges, and under both disturbances and stochastic noise. To ensure the worst-case fault sensitivity in the considered frequency ranges and mitigate the effects of disturbances and noise, an optimization framework incorporating a mixed H_{_}/H₂ performance index is developed to compute the optimal detection filter. Moreover, a thresholding rule is proposed to guarantee both the false alarm rate (FAR) and the fault detection rate (FDR). Next, shifting attention to fault estimation in specific frequency ranges, an exact reformulation of the optimal estimation filter design using the restricted H_∞ performance index is derived, which is inherently non-convex. However, focusing on finite frequency samples and fixed poles, a lower bound is established via a highly tractable quadratic programming (QP) problem. This lower bound together with an alternating optimization (AO) approach to the original estimation problem leads to a suboptimality gap for the overall estimation filter design. The effectiveness of the proposed approaches is validated through applications of a non-minimum phase hydraulic turbine system and a multi-area power system.

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Ground fault detection in inverter-based microgrid (IBM) systems is challenging, particularly in a real-time setting, as the fault current deviates slightly from the nominal value. This difficulty is reinforced when there are partially decoupled disturbances and modeling uncertainties. The conventional solution of installing more relays to obtain additional measurements is costly and also increases the complexity of the system. In this brief, we propose a data-assisted diagnosis scheme based on an optimization-based fault detection filter with the output current as the only measurement. Modeling the microgrid dynamics and the diagnosis filter, we formulate the filter design as a quadratic programming (QP) problem that accounts for decoupling partial disturbances, robustness to nondecoupled disturbances and modeling uncertainties by training with data, and ensuring fault sensitivity simultaneously. To ease the computational effort, we also provide an approximate but analytical solution to this QP. Additionally, we use classical statistical results to provide a thresholding mechanism that enjoys probabilistic false-alarm guarantees. Finally, we implement the IBM system with Simulink and real-time digital simulator (RTDS) to verify the effectiveness of the proposed method through simulations.

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Advancements in technology and societal demands have led to increasing complexity, size, and automation in modern industrial systems. This trend makes these systems more safety-critical, as the occurrence of faults in system components or subsystems may cause the entire system to fail, resulting in significant economic losses and casualties. Consequently, developing an effective fault diagnosis method is crucial for ensuring the reliability, safety, and performance of industrial systems, especially energy systems, which are so relevant to our lives. However, most model-based fault diagnosis systems developed based on observers and parity space relations have the same order as that of the system. This can cause a significant computational burden when dealing with large-scale and high-dimensional systems. This thesis is dedicated to the design of fault diagnosis filters in the framework of differential-algebraic equations, which produce scalable residual generators with design flexibility. Meanwhile, we consider the impact of disturbances and stochastic noise ondiagnosis results, as well as the fault diagnosis problem within the finite frequency domain. In order to design filters capable of handling these issues, we solve filter parameters through optimization problems that are constructed based on specific diagnosis requirements.@en

We study a diagnosis scheme to reliably detect the active mode of discrete-time, switched affine systems in the presence of measurement noise and asynchronous switching. The proposed scheme consists of two parts: (i) the construction of a bank of filters, and (ii) the introduction of a residual/threshold-based diagnosis rule. We develop an exact finite optimization-based framework to numerically solve an optimal bank of filters in which the contribution of measurement noise to the residual is minimized. The design problem is safely approximated through linear matrix inequalities and thus becomes tractable. We further propose a thresholding policy along with probabilistic false-alarm guarantees to estimate the active system mode in real-time. In comparison with the existing results, the guarantees improve from a polynomial dependency in the probability of false alarm to a logarithmic form. This improvement is achieved under the additional assumption of sub-Gaussianity, which is expected in many applications. The performance of the proposed approach is validated through a numerical example and an application of the building radiant system.

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Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.

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In this paper, we propose an approach to detect mode transitions and to isolate active modes in discrete-time, switched affine systems. The proposed approach is in particular constructed for systems in which the controller is oblivious of the switching signal. The diagnosis approach consists of two main parts: construction of a bank of output filters (that generate desired residuals) and definition of a certain type of residual/threshold-based diagnosis rules. The filters' construction is cast as linear feasibility problems. These feasibility problems enforce desirable diagnosis relationships between each subsystem's affine constant and each residual. The diagnosis rules are inspired by the well-known generalized observer scheme. Moreover, we provide a method to compute each mode's diagnosis time based on the diagnosis rules and properly chosen residual thresholds. A numerical example is presented to show the performance of the proposed approach.

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The fourth industrial revolution is elevating the overall flexibility and controllability of the production processes, leading to better consistency in quality and improved feasibility of personalization. However, an evident downside is observed from the boosting system openness because the security and safety protocols are not ready for the potential impacts and threats. In this work, an in-depth analysis about the novel challenges is firstly presented. Then, the mechanism of false data injection attack on the industrial cyber-physical systems is studied. The target is to reveal the condition when the existing fault diagnosis systems fail to detect the false data injection attack on the distributed and networked systems. A theorem is proposed to support the discussions. It is elaborated that establishing interconnections between the subsystems will help prevent undetectable attacks, and thus improving the systems' safety.

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The high penetration of renewable energy resources and power electronic-based components has led to a low-inertia power grid which would bring challenges to system operations. The new model of load frequency control (LFC) must be able to handle the modern scenario where controlled areas are interconnected by parallel AC/HVDC links and storage devices are added to provide virtual inertia. Notably, vulnerabilities within the communication channels for wide-area data exchange in LFC loops may make them exposed to various cyber attacks, while it still remains largely unexplored how the new LFC in the AC/HVDC interconnected system with emulated inertia would be affected under malicious intrusions. Thus, in this article, we are motivated to explore possible effects of the major types of data availability and integrity attacks—Denial of Service (DoS) and false data injection (FDI) attacks—on such a new LFC system. By using a system-theoretic approach, we explore the optimal strategies that attackers can exploit to launch DoS or FDI attacks to corrupt the system stability. Besides, a comparison study is performed to learn the impact of these two types of attacks on LFC models of power systems with or without HVDC link and emulated inertia. The simulation results on the the exemplary two-area system illustrate that both DoS and FDI attacks can cause large frequency deviations or even make the system unstable; moreover, the LFC system with AC/HVDC interconnections and emulated inertia could be more vulnerable to these two types of attacks in many adversarial scenarios.

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