Model-Based Run-Time Error Detection

Hooman, Jozef; Hendriks, Teun

doi:10.1007/978-3-540-69073-3_24

“…The TRADER project [7] has a different target. Its final goal is to "allow a device to correct at run time important, user noticeable, failure modes".…”

Section: Related Workmentioning

confidence: 99%

Intelligent monitoring of software components

Moisan¹

2012

2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)

0

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We propose to use Artificial Intelligence techniques to monitor and control complex processing chains of software components. We consider software systems that run in an evolving environment and thus may require adaptation at run time. Our approach relies on knowledge representations of both structural and dynamic aspects of components and processing chains.The paper concentrates more precisely on run time adaptation to cope with context changes. Run time policies are expressed by means of inference rules. At run time an inference engine uses these rules to orchestrate the component chain, in particular to achieve run time adaptations, such as component parameter tuning or re-assembly of the processing chain. We describe the general evaluation-repair mechanism which involves to evaluate environment changes and execution results and then to trigger the suitable reconfigurations. I. CONFIGURATION OF SOFTWARE PROCESSING CHAINSConfiguring and adapting complex chains of processing components to environment changes is an important challenge that has concerned us for years. This problem occurs in many domains, such as signal processing, image processing, or scientific computing. It necessitates to reason on the usage of components at configuration as well as at run time. Our goal is to automate, as much as possible, the correct (re)configurations of such processing chains during their life time. To this end, we followed a cross-disciplinary approach that works in two directions.One the one hand, we have applied a software engineering approach to design knowledge-based system generators. We propose a set of toolkits (mainly component frameworks) to design and customize the different elements necessary to produce a knowledge-based system, e.g., interfaces, inference engines, or knowledge acquisition tools. This approach allows knowledge-based system developers a significant gain in time, and improves software readability and safeness; this "software engineering for artificial intelligence" direction is out of the scope of this paper and is described in details in [1]. We also propose an extensible domain specific language (DSL), YAKL [2]. It provides knowledge base developers with a user-friendly syntax and a well defined semantics. This language can be checked for consistency and translated into various formats.On the other hand, we have developed dedicated artificial intelligence methods to capture and to operationalize the organization and the dynamic behavior of processing chains. Our knowledge-based approach, called supervision [3], aims at monitoring processing chains composed of software components. This means to automate the decisions necessary to manage components at configuration as well as at run time, in accordance with the current context, the intermediate results, and the changes in the environment.The paper focuses more specifically on the mechanism to dynamically monitor and control processing chains at run time. This mechanism relies on knowledge representation presented in section II. Section III describ...

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“…Aivaliotis, Georgoulias, Arkouli, and Makris (2019); Almasi (2016); Alvarez, Gutierrezzea, Bilb, Napolitano, and Fravolini (2019); de Azevedo, Araújo, and Bouchonneau (2016); Rehman et al (2013)), data (e.g. Furukawa and Deng (2020);Susto, Schirru, Pampuri, McLoone, and Beghi (2014)), state machines (Hooman & Hendriks, 2007) or combinations thereof.…”

Section: Predictive Fault Managementmentioning

confidence: 99%

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

Shahsavari

¹

,

Rabah

²

,

Immonen

³

et al. 2022

JID

0

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We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

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“…Aivaliotis, Georgoulias, Arkouli, and Makris (2019); Almasi (2016); Alvarez, Gutierrezzea, Bilb, Napolitano, and Fravolini (2019); de Azevedo, Araújo, and Bouchonneau (2016); Rehman et al (2013)), data (e.g. Furukawa and Deng (2020);Susto, Schirru, Pampuri, McLoone, and Beghi (2014)), state machines (Hooman & Hendriks, 2007) or combinations thereof.…”

Section: Predictive Fault Managementmentioning

confidence: 99%

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

Shahsavari

¹

,

Rabah

²

,

Immonen

³

et al. 2021

JID

0

View full text Add to dashboard Cite

We propose an adaptive run-time failure recovery control system for quadcopter drones, based on remote real-time processing of measurement data streams. Particularly, the measured RPM values of the quadcopter motors are transmitted to a remote machine which hosts failure detection algorithms and performs recovery procedure. The proposed control system consists of three distinct parts: (1) A set of computationally simple PID controllers locally onboard the drone, (2) a set of computationally more demanding remotely hosted algorithms for real-time drone state detection, and (3) a digital twin co-execution software platform — the ModelConductor-eXtended — for two-way signal data exchange between the former two. The local on-board control system is responsible for maneuvering the drone in all conditions: path tracking under normal operation and safe landing in a failure state. The remote control system, on the other hand, is responsible for detecting the state of the drone and communicating the corresponding control commands and controller parameters to the drone in real time. The proposed control system concept is demonstrated via simulations in which a drone is represented by the widely studied Quad-Sim six degrees-of-freedom Simulink model. Results show that the trained failure detection binary classifier achieves a high level of performance with F1-score of 96.03%. Additionally, time analysis shows that the proposed remote control system, with average execution time of 0.49 milliseconds and total latency of 6.92 milliseconds in two-way data communication link, meets the real-time constraints of the problem. The potential practical applications for the presented approach are in drone operation in complex environments such as factories (indoor) or forests (outdoor).

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Model-Based Run-Time Error Detection

Cited by 11 publications

References 13 publications

Intelligent monitoring of software components

Intelligent monitoring of software components

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

Remote Run-Time Failure Detection and Recovery Control For Quadcopters

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