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arXiv2024

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics

This work presents a novel data augmentation solution for non-stationary multivariate time series and its application to failure prognostics. The method extends previous work from the authors which is based on time-varying autoregressive processes. It can be employed to extract key information from a limited number of samples and generate new synthetic samples in a way that potentially improves the performance of PHM solutions. This is especially valuable in situations of data scarcity which are very usual in PHM, especially for failure prognostics. The proposed approach is tested based on the CMAPSS dataset, commonly employed for prognostics experiments and benchmarks. An AutoML approach from PHM literature is employed for automating the design of the prognostics solution. The empirical evaluation provides evidence that the proposed method can substantially improve the performance of PHM solutions.

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arXiv2024

AI-based Identification of Most Critical Cyberattacks in Industrial Systems

Modern industrial systems face a growing threat from sophisticated cyberattacks that can cause significant operational disruptions. This work presents a novel methodology for identification of the most critical cyberattacks that may disrupt the operation of such a system. Application of the proposed framework can enable the design and development of advanced cybersecurity solutions for a wide range of industrial applications. Attacks are assessed taking into direct consideration how they impact the system operation as measured by a defined Key Performance Indicator (KPI). A simulation model (SM), of the industrial process is employed for calculation of the KPI based on operating conditions. Such SM is augmented with a layer of information describing the communication network topology, connected devices, and potential actions an adversary can take based on each device or network link. Each possible action is associated with an abstract measure of effort, which is interpreted as a cost. It is assumed that the adversary has a corresponding budget that constrains the selection of the sequence of actions defining the progression of the attack. A dynamical system comprising a set of states associated with the cyberattack (cyber-states) and transition logic for updating their values is also proposed. The resulting augmented simulation model (ASM) is then employed in an artificial intelligence-based sequential decision-making optimization to yield the most critical cyberattack scenarios as measured by their impact on the defined KPI. The methodology is successfully tested based on an electrical power distribution system use case.

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