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The paper proposes an original, comprehensive, and methodically consistent graph theory-based approach to the description of the diagnosed process and the diagnosing system. The main baseline of the presented approach is in the dichotomous approach to diagnosing. It involves a separate description of both the process and the diagnostic system. This approach reflects the practice of designing implementable diagnostic systems. Thus, it can be seen as a proposal of a new, alternative, and, at the same time, flexible design procedure with great potential for applications. The primary motivation behind it was an attempt to circumvent the numerous limitations of well-known and well-established diagnosis approaches proposed by the communities working on fault detection and isolation (FDI) and artificial intelligence theories for diagnosis (DX). Accordingly, the paper identifies and provides an extensive discussion and a critical analysis of the existing limitations. Numerous examples and references to practical applications of the approach are indicated.

This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named “cybernetic faults” that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its model and tracking changes in the values of selected control loop performance indicators to supervise the control circuit. A binary diagnostic matrix was used to isolate anomalies. The presented approach requires only standard operating data (process variable (PV), setpoint (SP), and control signal (CV). The proposed concept was tested using the example of a control system for superheaters in a steam line of a power unit boiler. Cyber-attacks targeting other parts of the process were also included in the study to test the proposed approach’s applicability, effectiveness, and limitations and identify further research directions.

This paper is concerned with the issue of the diagnostics of process faults and the detection of cyber-attacks in industrial control systems. This problem is of significant importance to energy production and distribution, which, being part of critical infrastructure, is usually equipped with process diagnostics and, at the same time, is often subject to cyber-attacks. A commonly used approach would be to separate the two types of anomalies. The detection of process faults would be handled by a control team, often with a help of dedicated diagnostic tools, whereas the detection of cyber-attacks would be handled by an information technology team. In this article, it is postulated here that the two can be usefully merged together into one, comprehensive, anomaly detection system. For this purpose, firstly, the main types of cyber-attacks and the main methods of detecting cyber-attacks are being reviewed. Subsequently, in the analogy to “process fault”—a term well established in process diagnostics—the term “cyber-fault” is introduced. Within this context a cyber-attack is considered as a vector containing a number of cyber-faults. Next, it is explained how methods used in process diagnostics for fault detection and isolation can be applied to the detection of cyber-attacks and, in some cases, also to isolation of the components of such attacks, i.e., cyber-faults. A laboratory stand and a simulator have been developed to test the proposed approach. Some test results are presented, demonstrating that, similarly to equipment/process faults, residua can be established and cyber-faults can be identified based on the mismatch between the real data from the system and the outputs of the simulation model.

The paper analyses the research problem of conducting diagnostic reasoning for dynamic objects to eliminate the possibility of formulating false diagnoses resulting from different delays of the symptoms related to a particular fault while simultaneously striving to obtain high distinguishability. The research aimed to develop a new diagnostic inference method robust to symptom delays and characterised by high accuracy of generated diagnosis. Known methods ensuring the correctness of inference in the case of symptom delays but at the cost of reducing distinguishability of faults have been characterised. A new inference method was developed, which uses the three-valued residual evaluation and knowledge regarding elementary symptom sequences. A formal description of the diagnosing system and the proposed method are given. The method of obtaining the knowledge about the order of symptoms based on a cause-and-effect graph and was characterised. The method’s effectiveness was presented in simulation studies on the example of diagnosing a set of serially connected tanks. The comparison of the fault distinguishability obtained using the proposed method and other approaches illustrates the new method’s advantages.

The paper concerns a significant problem in the diagnostics of industrial processes, which is the need to achieve high fault distinguishability. High distinguishability results in the generation of precise diagnoses that enable making appropriate security decisions. In the known approaches, the efforts to obtain high distinguishability are focused on the selection of an appropriate set of generated residuals. The paper presents a new method of diagnostic reasoning using the notation of faults/diagnostic signals’ relations in the form of a Fault Isolation System (FIS), which enables the use of multivalent diagnostic signals. In addition, the proposed method uses knowledge (usually incomplete) about the sequence of symptoms. Reasoning was carried out on the basis of simple, physically possible signatures, resulting from the FIS. Assumptions and a diagnostic algorithm are given. The reasoning algorithm works in a step-by-step manner, after observing further symptoms. In each reasoning step, two diagnoses are generated in parallel. A more accurate, but less certain diagnosis is formulated on the basis of the value of all diagnostic signals, and the diagnosis is less accurate, but more reliable, solely on the basis of symptoms. An example of using the method for diagnosing a set of connected liquid tanks is given. The method was compared with other reasoning methods based on columns (signatures) and, also, with row-based reasoning methods. It is shown that the proposed method allows the increase of the distinguishability of faults compared to other methods. The distinguishability grows with the knowledge of elementary symptom sequences. It is also noted that the proposed approach makes possible diagnosing not only faults, but also cyber attacks.

The definitions and conditions for fault isolability of single faults for various forms of the diagnostic relation are reviewed. Fault isolability and unisolability on the basis of a binary diagnostic matrix are analyzed. Definitions for conditional and unconditional isolability and unisolability on the basis of a fault information system (FIS), symptom sequences and directional residuals are formulated. General definitions for conditional and unconditional isolability and unisolability in the cases of simultaneous evaluation of diagnostic signal values and a sequence of symptoms are provided. A comprehensive example is discussed.

This paper reports on development of a laboratory set-up, for testing the concept and the components of Industry 4.0 enabled mechatronic system. A simple mock manufacturing lane is connected to a collaborating robot. There are three different programmable logic controllers (PLC) and various other elements which need to talk to each other using a variety of communication protocols. The controllers, and consequently the schedule of operations, can be programmed remotely. Moreover, the information is exchanged through the cloud service which could also assume the role of a soft-PLC. From the perspective of cyber-security, this set-up enables defining benchmarks and performing tests for resilience of the control structure. Some test scenarios are discussed in the paper.

The paper is aimed at presenting a study of the main limitations and problems influencing the robustness of diagnostic algorithms used in diagnostics of complex chemical processes and to present the selected exemplary solutions of how to increase it. The five major problems were identified in the study. They are associated with: uncertainties of fault detection and reasoning, changes of the diagnosed process structure, delays of fault symptoms formation and multiple faults. A brief description and exemplary solutions allowing increase of the robustness of diagnostic algorithms were given. Proposed methods were selected keeping in mind applicability for the on-line monitoring and diagnostics of complex chemical processes.

This chapter deals with the problems of the interpretation of diagnostic relation uncertainty and its parallel application with multiple-valued residual evaluation. First, the summary of notation of the diagnostic relation uncertainty and its proposed extensions are described. The basic rules of translating the expert knowledge about the uncertainties to the diagnostic relation uncertainty, expressed by proper certainty factors, are given. Then, the problem of the influence of diagnostic relation uncertainty on conditional isolability is introduced and discussed, especially taking into account the multiple-valued residual evaluation. The main advantages and disadvantages of the utilization of the extended interpretation of diagnostic relation uncertainty description, with respect to fault isolability issue, are underlined. Finally, the basic rules of simultaneous use of multiple-valued residual evaluation and application of diagnostic relation uncertainty and its extensions are formulated.

This chapter presents structure and functional properties of the Advanced Monitoring and Diagnostic System “AMandD” dedicated for large-scale industrial processes. The system arose within the framework of research project “CHEM” of 5th European Framework Program. Applied in the system up-to-date algorithms of modeling methods and fault detection and isolation are described in the chapter. Also a brief description of used IT solutions is presented. As a conclusion, the further directions of development and researche are mentioned.