Alarm floods remain a challenge in industrial operations, potentially overwhelming human operators with excessive alarm notifications during abnormal situations. To address this, alarm flood classification (AFC) methods utilize historical data to classify recurring alarm patterns automatically. However, the practical utility of these methods may be limited by potential degradation in alarm data quality, resulting from sensor faults, communication errors, or detection delays, which can substantially compromise their classification accuracy and reliability. This paper proposes a novel methodology to systematically analyze the robustness of AFC methods against realistic alarm data quality issues. We introduce four distinct perturbations: missing alarms, false alarms, delayed alarm flood detection, and alarm reordering, to replicate real-world alarm data degradation. We evaluate our methodology using a novel alarm dataset derived from the Tennessee-Eastman process, while examining the robustness of six relevant AFC methods from the literature. The results demonstrate significant variations in robustness across different AFC methods and perturbation types, providing insights into their practical reliability under various realistic scenarios.

In this article, an adaptive tracking controller based on radial basis function neural networks (RBFNNs) is proposed for nonlinear plants with unmatched uncertainties and smooth reference signals. The concept of valid RBFNN adaptive control is introduced where all closed-loop arguments of the involved RBFNNs should always remain inside their corresponding compact sets. Considering the local approximation capacity of RBFNNs, validity requirements are necessary for ensuring reliable closed-loop approximation accuracy and stability. To obtain valid RBFNN adaptive controllers, a novel iterative design method is proposed and embedded into the traditional backstepping approach. In the initial iteration, an ideal RBFNN and its online estimated version are introduced in each step where the initial compact set guarantees the validity requirement for only a finite time interval. Then, by carefully investigating the dependence among different signals and introducing some auxiliary variables, the compact sets are redesigned for prolonging the time interval satisfying validity requirements to infinity as the iteration goes on. Consequently, a closed-loop system model can be formulated during the entire control process, which underlies a rigorous proof on closed-loop stability and some guidelines on practical implementation. Meanwhile, rigorous analysis from validity requirements reveals, for the first time, a new feature of RBFNN adaptive controllers in the presence of unmatched uncertainties: excessively large scales of RBFNNs in intermediate steps may impair the closed-loop performance. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

Alarm floods are periods of intense alarm activity that may hinder control room operators' ability to diagnose and respond to process abnormalities. In this context, a method to guide and assist operators during alarm floods would provide critical support in preventing abnormalities from escalating into serious accidents. Therefore, this study introduces a novel approach for the online classification of alarm floods based on their fault categories. Historical alarm data are used to train an ensemble of Natural Language Processing models, specifically word2vec, which learn contextual relationships between alarms under different fault conditions. As a new alarm flood appears, the models predict the most probable context alarms by exploiting the knowledge gained during training. Finally, a scoring system is proposed to reward the models that make correct predictions and eventually identify the most probable fault category. The efficacy of the method has been tested on simulated alarm data from the Tennessee Eastman Process benchmark. The results are encouraging, as the models achieved relatively high accuracy in most fault categories.

This article studies neural-network (NN) adaptive control for strict-feedback nonlinear systems with matched uncertainties and event-triggered communication. Radial basis function NNs (RBFNNs) are used in the backstepping design approach to compensate for nonlinear uncertain functions. The concept of valid compact sets for RBFNN adaptive controllers is proposed, where a local RBFNN approximator is defined and the closed-loop state can remain. To guarantee the existence of such valid compact sets, a new property on RBFNNs is presented, which shows that, in some properly designed RBFNNs, the norm of their ideal weight vectors can always become arbitrarily small. By utilizing this property, the selections on valid compact sets are investigated, resulting in rigorous proof on RBFNN adaptive controllers to solve a local tracking problem with a given smooth enough reference signal. Subsequently, to save limited communication resources, a Zeno-free event-triggering mechanism in controller-to-actuator channels is proposed. Under this event-triggered adaptive controller, the corresponding tradeoff among the tracking performance, computational burden, and communication consumption is analyzed. Furthermore, two extensions are made to the general local function approximator, which is in the form of a weight vector multiplying a group of basis functions, and to the communication in sensor-to-controller channels. Finally, several simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

The excessive use of nodes and communication links in a wireless control system (WCS) causes unnecessary utilization of resources. In this article, a strategic topological formation is studied for a WCS, where a previously proposed topology consisting of a plant system, a controller system, and an intermediate network system is further developed. More specifically, this article presents a modeling framework and a design procedure for the topology that results in the utilization of a reduced number of nodes and communication links. It also discusses several conditions for the connectivity of the nodes under different topological scenarios. This article uses a four-tank process system as an application example to demonstrate the strategic topological formation of its WCS.

This article studies an event-triggered tracking control problem for linear systems subject to output feedback and disturbances, where a new configuration incorporating filtered outputs and impulsive observers is proposed. The transmissions in the sensor-to-controller and controller-to-actuator channels are scheduled by dynamic event-triggered control (ETC) mechanisms to save communication resources. To eliminate the effects of the derivatives of output noises on the tracking and transmission performance, a low-pass filter is introduced to preprocess the raw output signals. Both the filter state and raw output will be transmitted to the controller node while the latter is only utilized by an impulsive observer at some discrete instants. Then, it is proved that the proposed dynamic ETC schemes can solve the practical tracking control problem with fixed reference points and avoid Zeno behavior in both channels. Meanwhile, when some user-specified parameters in the event-triggering conditions are small enough, the tracking control problem can be solved asymptotically for disturbance-free systems. In addition, to further improve the transient performance, reduced-order impulsive observers and optimization of impulsive gain matrices are studied. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

Early classification of ongoing alarm floods in industrial monitoring systems is crucial to provide a safe and efficient operation. It can provide online decision support for plant operators to take timely action, without waiting for the end of an alarm flood. In this article, a data-driven approach is proposed to address the early classification problem with unlabeled historical data. To prioritize earlier activated alarms and take advantage of the triggering time information of alarms, a vector representation called exponentially attenuated component (EAC) is used to represent alarm floods. This makes alarm sequences fit for different powerful machine learning algorithms, which can be easily implemented online with acceptable computational complexities. A method based on the time information of unlabeled historical alarm floods is formulated to determine the attenuation coefficient for EAC representation. With the Gaussian mixture model, an efficient semisupervised approach is proposed to provide an early classification of alarm floods using unlabeled historical data. It includes two phases: offline clustering and online classification, where the clustering step is automated in terms of choosing the optimal number of clusters by applying an efficient cluster validity index. The efficiency of the proposed method is validated by the Tennessee Eastman process benchmark and a real industrial dataset.

Alarm systems are critical for process safety and efficiency of complex industrial facilities. However, the presence of alarm floods severely compromises the performance of alarm systems. To cope with alarm floods, data mining has been applied to discover interesting patterns from historical alarm data, and such patterns can be used for alarm suppression, root cause analysis, and decision supports. However, most existing methods ignored the timestamps in pattern extraction or obtained complete patterns with significant redundancy. In this article, a new method is proposed to extract alarm flood patterns using a modified CloFAST algorithm. The contributions are twofold: first, a closed alarm sequence mining approach is proposed based on the CloFAST algorithm with improvements to incorporate timestamps and tolerate alarm order switchings; second, a pattern distillation strategy is designed to merge similar alarm sequences and export compact alarm sequential patterns. The proposed method is capable of avoiding influences of order ambiguities and also minimizing the redundancy of extracted patterns. The effectiveness of the proposed method is demonstrated by an industrial case study involving alarm data from a large-scale industrial facility.

This paper studies the problem of false-data injection attacks on remote state estimation. In contrast to existing work that presupposed linear attack models, the optimal information-based attack policy that can cause the maximum estimation quality degradation and deceive the interval χ2detector is obtained. The scenarios that attackers have different information sets from the remote estimator are studied in a unified framework. It is shown that, with the given information set, there does not exist another attack policy outperforms the proposed one. The result in this work reduces to the optimal innovation-based linear attack in a special case. The optimality of the information-based strategy is verified by theoretical analysis and numerical examples.

The objective of this paper is to facilitate the improvement of the control and operation of industrial facilities, by providing decision support tools. More specifically, this paper has three main contributions. First, this paper presents the definition of operation metrics that provide insight into the behavior of: 1) annunciated alarms, 2) alarm floods, and 3) operator actions in industrial facilities. Second, this paper presents visualization plots named multilayered radar plots that can present information in an elegant, dense, and comprehensive fashion. Three types of plots are proposed, which collectively compare the behavior of metrics, variables, and operation times in industrial facilities. Third, this paper presents a ranking method and a reordering design procedure of displayed alarms during an alarm flood to reorder the alarms based on the proposed alarm-flood criticality index. The purpose is to provide additional assistance to operators to focus on more critical issues. As the operation metrics, visualization plots, and the ranking in alarm floods heavily utilize historized data and given the industrial-oriented application of these decision support tools, this paper also addresses the extraction of information and the integration of the tools into industrial automation platforms. Note to Practitioners-In a control system used for an industrial facility, a large amount of data is collected and historized. The data include sensor measurements, status of actuators, alarms, and operator actions, and it therefore contains valuable information. The information can be extracted and utilized to assist in the improvement of the control and operation of the industrial facility. The objective of this paper is to provide decision support tools by: 1) defining operation metrics that can characterize the information extracted from the historized data; 2) presenting visualization plots that allow for a clear presentation and comparison of the metrics, where three types of plots are proposed for different purposes of comparison; and 3) a ranking method and a reordering design procedure of displayed alarms during an alarm flood. Furthermore, this paper discusses how the information is extracted from the historized data and how to integrate the proposed decision support tools into existing industrial automation platforms.

Alarm systems play critically important roles for the safe and efficient operation of modern industrial plants. However, most existing industrial alarm systems suffer from poor performance, noticeably having too many alarms to be handled by operators in control rooms. Such alarm overloading is extremely detrimental to the important role played by alarm systems. This paper provides an overview of industrial alarm systems. Four main causes are identified as the culprits for alarm overloading, namely, chattering alarms due to noise and disturbance, alarm variables incorrectly configured, alarm design isolated from related variables, and abnormality propagation owing to physical connections. Industrial examples from a large-scale thermal power plant are provided as supportive evidences. The current research status for industrial alarm systems is summarized by focusing on existing studies related to these main causes. Eight fundamental research problems to be solved are formulated for the complete lifecycle of alarm variables including alarm configuration, alarm design, and alarm removal. Note to Practitioners-Alarm systems are critical assets for operational safety and efficiency of plants in various industrial sectors, such as power and utility, process and manufacturing, and oil and gas. However, industrial alarm systems are generally suffering from alarm overloading. This paper provides an overview of industrial alarm systems, by proposing main causes for alarm overloading, summarizing current research status and formulating open problems. In presenting this overview, we hope to attract direct attentions from more researchers and engineers into the study of industrial alarm systems.

This paper studies the delay robustness of a class of synchronous hybrid event- and time-driven consensus protocols in undirected networks. These protocols can ensure the system performance at reduced data-sampling rates. We consider three types of time delays in feedbacks, including one common time delay, multiple time-invariant delays, and multiple time-varying delays; and by sampled-data control techniques, we characterize the maximum allowable time delay and the event-detecting period for solving the average consensus problem in terms of the algebraic structure of interaction topologies. Simulations are given to show the effectiveness of theoretical results.

The 17 papers in this special section focus on integrated optimization of industrial automation.

Flooding of alarms is a very crucial problem in process industries. An alarm flood makes an operator ineffective of taking necessary actions, and often risking an emergency shutdown or a major upset. In this work, the flooding of alarms is discussed based on the standards presented in ISA 18.2. A new analysis method is proposed to investigate similar alarm floods from the historic alarm data and group them on the basis of the patterns of alarm occurrences. A case study on real industrial alarm data is also presented to demonstrate the utility of the proposed analysis.

Accuracy and efficiency of alarm systems are of paramount importance in safe operations of industrial processes. Accuracy is measured by false and missed alarm rates (probabilities); while efficiency relates to the detection delay and complexity of the technique used. Moving average filters are often employed in industry for improved alarm accuracy. Can one do better than moving average filters? The following two problems are studied in this paper: First, given both normal and abnormal statistic distributions, how to design an optimal alarm filter (of fixed complexity) for best alarm accuracy, minimizing a weighted sum of false and missed alarm rates? Second, in what cases are moving average filters optimal? For the first problem, design of optimal linear FIR alarm filters is studied, and a numerical optimization based procedure is proposed. For the second problem, a sufficient condition is given under which the moving average filters are optimal.

This paper studies the statistical analysis for alarm signals in order to detect whether two alarm signals are correlated. First, a similarity measurement, namely, Sorgenfrei coefficient, is selected among 22 similarity coefficients for binary data in the literature. The selection is based on the desired properties associated with specialities of alarm signals. Second, the distribution of a so-called correlation delay is shown to be indispensable and effective for the detection of correlated alarms. Finally, a novel method for detection of correlated alarms is proposed based on Sorgenfrei coefficient and distribution of the correlation delay. Numerical and industrial examples are provided to illustrate and validate the obtained results.

The performance of a univariate alarm system can be assessed in many cases by three indices, namely, the false alarm rate (FAR), missed alarm rate (MAR), and averaged alarm delay (AAD). First, this paper studies the definition and computation of the FAR, MAR, and AAD for the basic mechanism of alarm generation solely based on a trip point, and for the advanced mechanism of alarm generation by exploiting alarm on/off delays. Second, a systematic design of alarm systems is investigated based on the three performance indices and the tradeoffs among them. The computation of FAR, MAR, and AAD and the design of alarm systems require the probability density functions (PDFs) of the univariate process variable in the normal and abnormal conditions. Thus, a new method based on mean change detection is proposed to estimate the two PDFs. Numerical examples and an industrial case study are provided to validate the obtained theoretical results on the FAR, MAR and AAD, and to illustrate the proposed performance assessment and alarm system design procedures.

A wireless sensor network (WSN) consists of low-cost and energy-limited sensors to measure a distributed phenomenon. The finite energy constraint limits the synchronization of sensors at every sampling instant which introduces sampling jitters. In this letter, we model sampling jitters using fractional delay transfer functions. The WSN is modeled using a hybrid multirate filter bank where the objective is to design discrete-time, causal and stable synthesis filters to minimize the effect of sampling jitters. Using a norm-invariant discretization, the hybrid and multirate problem is reduced to a model-matchingH2optimization problem involving linear time-invariant and discrete-time systems. A numerical example is also presented to show the effectiveness of the proposed approach.

This paper investigates the problem of$H_{\infty }$fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi--Sugeno (T--S) fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links existing between the plant and controller are assumed to be imperfect (that is, data packet dropouts occur intermittently, which appear typically in a network environment), and stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the unreliable communication links. Attention is focused on the design of$H_{\infty }$controllers such that the closed-loop system is stochastically stable and preserves a guaranteed$H_{\infty }$performance. Two approaches are developed to solve this problem, based on the quadratic Lyapunov function and the basis-dependent Lyapunov function, respectively. Several examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper presents a new approach to the design of robust filters for uncertain 2-D systems described by the Fornasini-Marchesini (FM) model. The polynomially parameter-dependent approach is developed to solve the addressed filtering problem, with a new linear matrix inequality condition obtained for the existence of desired Hinfinfilters. An example is given to show the reduced conservatism of the proposed method.

This paper investigates the problem of H∞fuzzy control of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the control strategy takes the form of parallel distributed compensation. The communication links, existing between the plant and controller, are assumed to be imperfect (that is, data-packet dropouts occur intermittently, which appear typically in a network environment), and stochastic variables satisfying the Bernoulli random binary distribution are utilized to model the unreliable communication links. Attention is focused on the design of H∞controllers such that the closedloop system is stochastically stable and preserves a guaranteed H∞performance. Two approaches are developed to solve this problem, based on quadratic Lyapunov function and basisdependent Lyapunov function respectively. Several examples are provided to illustrate the usefulness and applicability of the developed theoretical results.

This paper investigates the problem of stabilization for a Takagi-Sugeno (T-S) fuzzy system with nonuniform uncertain sampling. The sampling is not required to be periodic, and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound. By using the input delay approach, the T-S fuzzy system with variable uncertain sampling is transformed into a continuous-time T-S fuzzy system with a delay in the state. Though the resulting closed-loop state-delayed T-S fuzzy system takes a standard form, the existing results on delay T-S fuzzy systems cannot be used for our purpose due to their restrictive assumptions on the derivative of state delay. A new condition guaranteeing asymptotic stability of the closed-loop sampled-data system is derived by a Lyapunov approach plus the free weighting matrix technique. Based on this stability condition, two procedures for designing state-feedback control laws are given: one casts the controller design into a convex optimization by introducing some over design and the other utilizes the cone complementarity linearization idea to cast the controller design into a sequential minimization problem subject to linear matrix inequality constraints, which can be readily solved using standard numerical software. An illustrative example is provided to show the applicability and effectiveness of the proposed controller design methodology.

Crosstalk between multiple services transmitting through the same telephone cable is the primary limitation to digital subscriber line (DSL) services. From a spectrum management point of view, it is important to have an accurate map of all the services that generate crosstalk into a given pair. This paper on crosstalk identification is motivated by an important practical consideration: the signals constituting the crosstalk are transmitted at different rates in xDSL systems. Therefore, we here propose to use the "blocking technique," we derive blocked state-space models for multirate xDSL networks, and we set up the mapping relationship between available input and output data. Further, we use the least-squares principle to identify the crosstalk functions, and study the convergence rate and upper bound of the parameter-estimation error. Finally, we illustrate and verify the theoretical findings with simulation examples

We first use the Fourier series to find the steady-state response of discrete-time linear periodically time-varying (LPTV) systems to periodic inputs. Then, in order to obtain the response of LPTV systems to general inputs, we use the Fourier transform (FT) and establish a direct link between alias-component matrices and LPTV systems modeled by periodically time varying difference equations. This is given in terms of the Fourier series of the parameters in the model.

Scale-space construction based on Gaussian filtering requires convolving signals with a large bank of Gaussian filters with different widths. We propose an efficient way for this purpose by /spl Lscr//sub 1/ optimal approximation of the Gaussian kernel in terms of linear combinations of a small number of basis functions. Exploring total positivity of the Gaussian kernel, the method has the following properties: 1) the optimal basis functions are still Gaussian and can be obtained analytically; 2) scale-spaces for a continuum of scales can be computed easily; 3) a significant reduction in computation and storage costs is possible. Moreover, this work sheds light on some issues related to use of Gaussian models for multiscale image processing.>