Model-based Health Monitoring of Hybrid Systems

Wang, Danwei; Yu, Ming; Low, Chor Ping; Arogeti, Shai

doi:10.1007/978-1-4614-7369-5

Cited by 68 publications

(32 citation statements)

References 1 publication

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“…System execution, however, provides many opportunities for surprising deviations from the model: faults may cause the system to function improperly [43], sensors may deliver uncertain values, actuators may suffer from disturbance, or the formal verification may have assumed simpler ideal-world dynamics for tractability reasons or made unrealistically strong assumptions about the behavior of other agents in the environment. Simpler models are often better for time-critical decisions and optimizations, because they make it possible to compute predictions at the rate required for real-time decisions.…”

Section: Introductionmentioning

confidence: 99%

ModelPlex: verified runtime validation of verified cyber-physical system models

Mitsch

Platzer

2016

Form Methods Syst Des

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Formal verification and validation play a crucial role in making cyber-physical systems (CPS) safe. Formal methods make strong guarantees about the system behavior if accurate models of the system can be obtained, including models of the controller and of the physical dynamics. In CPS, models are essential; but any model we could possibly build necessarily deviates from the real world. If the real system fits to the model, its behavior is guaranteed to satisfy the correctness properties verified with respect to the model. Otherwise, all bets are off. This article introduces ModelPlex, a method ensuring that verification results about models apply to CPS implementations. ModelPlex provides correctness guarantees for CPS executions at runtime: it combines offline verification of CPS models with runtime validation of system executions for compliance with the model. ModelPlex ensures in a provably correct way that the verification results obtained for the model apply to the actual system runs by monitoring the behavior of the world for compliance with the model. If, at some point, the observed behavior no longer complies with the model so that offline verification results no longer apply, ModelPlex initiates provably safe fallback actions, assuming the system dynamics deviation is bounded. This article, furthermore, develops a systematic technique to synthesize provably correct monitors automatically from CPS proofs in differential dynamic logic by a correct-by-construction approach, leading to verifiably correct runtime model validation. Overall, ModelPlex generates provably correct monitor conditions that, if checked to hold at runtime, are provably guaranteed to imply that the offline safety verification results about the CPS model apply to the present run of the actual CPS implementation.

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Section: Introductionmentioning

confidence: 99%

ModelPlex: verified runtime validation of verified cyber-physical system models

Mitsch

Platzer

2016

Form Methods Syst Des

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“…1,5,7 ARR is the constraint relationship (f(u, u, y) = 0) between the system input (u) and output (i.e. measurement, y) using the nominal parameter values (u), known a priori either from product catalog or from estimation through experimentation.…”

Section: Arr Formulation and Robust Diagnosismentioning

confidence: 99%

“…Hence, to avoid these circumstances, suitable actions, that is, real-time fault detection and isolation (FDI) and fault tolerant control (FTC), should be applied continuously to ensure safe and efficient operation of the system. 1,2 The detail modeling, monitoring and diagnostic techniques for fluid power systems has been given by Watton. 3 It is well known in the field of model-based FDI that the constitutive relations between the inputs, measured outputs and known parameters of any system lead to analytical redundancy relations (ARRs), and numerical evaluations of these ARRs are residuals.…”

Section: Introductionmentioning

confidence: 99%

Multi-fault identification of a hydraulic lifting system via partitioning of unstructured residuals

Tripathi

Ghoshal

2016

Proceedings of the Institution of Mechanical Engineers, Part I:

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The structured residual monitoring approach attributed to binary fault signatures is most common for fault isolation in model-based fault detection and isolation. When residuals are unstructured due to lesser number of sensors, their corresponding parameters values are estimated numerically through optimization. Thereafter, the estimated values of the parameters are compared with their corresponding nominal values for fault isolation. This article presents a state of the art fault localization scheme by partitioning of a typical array of unstructured residuals where a number of parameters commonly affected the unstructured as well as structured residuals. A reduced set of parameters that are necessary to be estimated by optimization are found and are estimated through least-square optimization for fault isolation. The problem space (dimension of the objective function) in optimization thus gets reduced and optimization performance shows improvement. A high-pressure hydraulic circuit used in the dumper and several other lifting systems is used as a simulation example for the proposed approach.

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“…In the tool suite MoTHS, hybrid bond graphs are transformed into block diagrams by using block diagram models that can be efficiently reconfigured for those parts of a hybrid bond graph that need a re-assignment of causality after a system mode change has happened [20]. Wang and his co-workers [26] also map hybrid bond graphs onto block diagrams for simulation with Simulink ®1 [27]. …”

Section: Fig 211mentioning

confidence: 99%

Bond Graph Representations of Hybrid System Models

Borutzky

2014

Bond Graph Model-Based Fault Diagnosis of Hybrid Systems

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This chapter addresses the modelling abstraction of fast dynamic state transitions into instantaneous discrete state changes and its consequences and surveys various bond graph representations of hybrid system models proposed in the literature. The bond graph model-based quantitative approach to FDI in hybrid systems presented in Chap. 4 uses a bond graph representation with system mode independent computational causalities. As a result, a single set of equations in the form of a DAE system can be derived that holds for all system modes. The chapter concludes by addressing the index of the DAE system as it is an information that is relevant with regard to a symbolic and numerical processing of the DAE system.

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Model-based Health Monitoring of Hybrid Systems

Cited by 68 publications

References 1 publication

ModelPlex: verified runtime validation of verified cyber-physical system models

ModelPlex: verified runtime validation of verified cyber-physical system models

Multi-fault identification of a hydraulic lifting system via partitioning of unstructured residuals

Bond Graph Representations of Hybrid System Models

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