Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

Moradi, Mehrdad; Acker, Bert Van; Denil, Joachim

doi:10.3390/s23042166

Cited by 3 publications

(2 citation statements)

References 66 publications

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“…Furthermore, we create a game emulator to convert fault diagnosis into an orderly decision-making scenario, thereby leveraging DRL effectively. 37 In this section, firstly, the fundamental working principle of fault detection agent in the context of MATD3PG agents is provided. After that, the autonomous learning structure is presented explaining the details of collecting the 1D time series data from the PCC and converting these data into 2D image form such that it can be provided as input to the CNN.…”

Section: Working With Td3pg Control Agentsmentioning

confidence: 99%

“…Drawing inspiration from prior references, this study reframes inverter fault diagnosis as a form of deduction game. Furthermore, we create a game emulator to convert fault diagnosis into an orderly decision‐making scenario, thereby leveraging DRL effectively 37 . In this section, firstly, the fundamental working principle of fault detection agent in the context of MATD3PG agents is provided.…”

Section: Fault‐tolerant Action Using Td3pg Agentsmentioning

confidence: 99%

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Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach

Malik,

Haque,

Kurukuru

et al. 2023

Circuit Theory & Apps

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SummaryGrid‐tied photovoltaic system (GTPS) are widely favored due to their inherent benefits. The interface of parallel power electronic converters in these systems is rapidly increasing. The inverter in GTPS has a very important role in power conversion, transfer, and control. However, their challenging working conditions contribute to susceptibility to power switching device failures. Traditional fault diagnosis and tolerant approaches exhibit limitations in control efficiency, model dependency, and slow recovery from fault. This study proposes a multi‐agent twin delayed deep deterministic policy gradient (MATD3PG) configuration for intelligent parallel inverter control, fault diagnosis, and fault‐tolerant operation in GTPS structure. By leveraging the multi‐agent reinforcement learning (RL) framework, an optimal control of the parallel inverter can be achieved, encompassing fault‐tolerant operation using MATLAB Simulink/PLECS. The major advantage the given technique offers is that it carries out optimum fault‐tolerant operation without causing the system derating. Experimental findings demonstrate that the proposed fault‐tolerant model based on RL traditional methods is able to ensure continuous supply to the connected loads even during fault events. Further, the transition time from fault occurrence to recovery is found to be 6 ms, which is quite less compared with the fault‐tolerant techniques presented in literature. Through real‐time fault diagnosis‐based results, the proposed approach ensures precise tracking of reference currents, quicker response times, uninterrupted supply, and smooth transition to the post‐fault operation mode.

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Section: Working With Td3pg Control Agentsmentioning

confidence: 99%

Section: Fault‐tolerant Action Using Td3pg Agentsmentioning

confidence: 99%

Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach

Malik,

Haque,

Kurukuru

et al. 2023

Circuit Theory & Apps

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Multidomain Fault Models Covering the Analog Side of a Smart or Cyber–Physical System

Tosoni,

Dall’Ora,

Fraccaroli

et al. 2024

IEEE Trans. Comput.

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Over the last decade, the industrial world has been involved in a massive revolution guided by the adoption of digital technologies. In this context, complex systems like cyber-physical systems play a fundamental role since they were designed and realized by composing heterogeneous components. The combined simulation of the behavioral models of these components allows to reproduce the nominal behavior of the real system. Similarly, a smart system is a device that integrates heterogeneous components but in a miniaturized form factor. The development of smart or cyber-physical systems, in combination with faulty behaviors modeled for the different physical domains composing the system, enables to support advanced functional safety assessment at the system level. A methodology to create and inject multi-domain fault models in the analog side of these systems has been proposed by exploiting the physical analogy between the electrical and mechanical domains to infer a new mechanical fault taxonomy. Thus, standard electrical fault models are injected into the electrical part, while the derived mechanical fault models are injected directly into the mechanical part. The entire flow has been applied to two case studies: a direct current motor connected with a gear train, and a three-axis accelerometer.

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Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

Moradi

Acker

Denil

2023

Sensors

Self Cite

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The safety assessment of cyber-physical systems (CPSs) requires tremendous effort, as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPSs is fault injection (FI). The goal of fault injection is to find a catastrophic fault that can cause the system to fail by injecting faults into it. These catastrophic faults are less likely to occur, and finding them requires tremendous labor and cost. In this study, we propose a reinforcement learning (RL)-based method to automatically configure faults in the system under test and to find catastrophic faults in the early stage of system development at the model level. The proposed method provides a guideline to utilize high-level domain knowledge about a system model for constructing the reinforcement learning agent and fault injection setup. In this study, we used the system (safety) specification to shape the reward function in the reinforcement learning agent. The reinforcement learning agent dynamically interacted with the model under test to identify catastrophic faults. We compared the proposed method with random-based fault injection in two case studies using MATLAB/Simulink. Our proposed method outperformed random-based fault injection in terms of the severity and number of faults found.

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Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

Cited by 3 publications

References 66 publications

Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach

Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach

Multidomain Fault Models Covering the Analog Side of a Smart or Cyber–Physical System

Failure Identification Using Model-Implemented Fault Injection with Domain Knowledge-Guided Reinforcement Learning

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