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Chiyoda has been granted a patent for a plant management method that involves acquiring correlation information between components affected by a cyberattack and zoning the components accordingly. The correlation information is based on a fault tree analysis of important elements that influence plant operation and is generated from safety evaluation data. GlobalData’s report on Chiyoda gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Chiyoda, syngas production was a key innovation area identified from patents. Chiyoda's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.

Plant management method for zoning components after a cyberattack

Source: United States Patent and Trademark Office (USPTO). Credit: Chiyoda Corp

A recently granted patent (Publication Number: US11665193B2) describes a plant management method that aims to enhance cybersecurity in industrial plants. The method involves acquiring correlation information that indicates the relationship between a component that has been targeted by a cyberattack and another component that may be affected by the attack. This correlation information is used to zone the various components of the plant based on their level of influence on plant operations.

The correlation information is represented as a fault tree, where an important element that significantly impacts plant operations is designated as the upper event. The fault tree is generated by analyzing information collected during safety evaluations of the plant, such as Hazard and Operability Studies (HAZOP) or Safety Integrity Level (SIL) assessments.

The method also includes extracting operation troubles that may arise due to component failures, abnormalities, or stoppages, and using this information to generate the fault tree. Additionally, the method involves acquiring state values that indicate the current states of the plant components during operation. These state values are used to predict future values and calculate an index that represents the importance or urgency of the influence on the important element.

The calculated index is then presented, and a matrix displaying the importance and urgency indices is displayed. The predicted values are generated using machine learning algorithms trained on past actual values of the state values. The index calculation algorithm considers the magnitude of the influence on the important element when weighting the difference or change rate of the difference calculated for each component's state value.

Furthermore, the patent describes a plant design device that implements this method. The device includes an acquirer to collect information during safety evaluations, a correlation information generator to generate the fault tree based on the acquired information, and a zoning executer to zone the plant components according to the correlation information.

In the event of a cyberattack, the method allows for the switching of control methods for components within each zone based on the calculated importance or urgency index. It also enables the isolation of the affected zone from the rest of the plant.

Overall, this patent presents a plant management method and device that utilize correlation information and fault trees to enhance cybersecurity and improve the response to cyberattacks in industrial plants.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.