Diagnostic Column Reasoning Based on Multi-Valued Evaluation of Residuals and the Elementary Symptoms Sequence

Abstract

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.

Integrated Approach to Diagnostics of Failures and Cyber-Attacks in Industrial Control Systems

“…Fault detection can be carried out using different types of models: analytical [1][2][3], neural [4][5][6], fuzzy [7], and statistical [8,9]. A among them, techniques based on partial parametric models identified in the fault-free state deserve special attention.…”

“…A among them, techniques based on partial parametric models identified in the fault-free state deserve special attention. In such cases, there are two options for obtaining knowledge about the impact of faults on residuals: using expert knowledge [1][2][3] or learning [5]. The learning technique requires the acquisition of experimental data not only for the normal state but also for all faulty states that are to be recognized.…”

“…The consideration of the sequence of symptoms. The works [2,3] show that in the case of a fault, taking into account the knowledge of the sequence of symptoms leads to an increase in the discrimination of faults and thus the precision of diagnosis.…”