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Gradient-based Reconfiguration of Cyber-Physical Production Systems

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... Reconfiguration aims at recovering a system from a fault by automatically adapting the system configuration, such that production, which was interrupted due to the fault, can be maintained, possibly by an adapted control [5]. In other words, the goal of reconfiguration is to transfer the system to a valid configuration, i.e. a configuration that allows normal system operation according [6]. Hence, effects of faults are minimized and production outages are reduced, accepting a degradation of the system performance, e.g. a reduction of speed, if necessary [5]. ...
... An algorithm solving the reconfiguration problem of CPPS should (R3.1) take restrictions on the solution space coming from the CPPS into account since enumerating all possibilities to adapt to a fault is not possible due to combinatorial explosion [5], (R3.2) be compatible with static models containing qualitative system information, i.e. information about causal dependencies in the system, and a binary validity of configurations [6], and (R3.3) enable a direct integration of expert knowledge and intuitive modeling. Propositional logic is used widely for diagnosis [12] and planning [9] since it mimics human reasoning [15]. ...
... The main drawbacks of ontologies are the high modeling efforts and expert knowledge required for creating the system models [14]. 6) Novelty of Our Approach: Table I summarizes the capabilities and limitations of existing approaches from FTC, e.g. [13], [17], [18], MBD, e.g. ...
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The increasing size and complexity of Cyber-Physical Production Systems (CPPS) lead to an increasing number of faults such as broken components or interrupted connections. Nowadays, faults are handled manually which is time-consuming because for most operators mapping from symptoms (i.e. warnings) to repair instructions is rather difficult. To enable CPPS to adapt to faults autonomously, reconfiguration, i.e. the identification of a new configuration that allows either reestablishing production or a safe shutdown, is necessary. This article addresses the reconfiguration problem of CPPS and presents a novel algorithm called AutoConf. AutoConf operates on a hybrid automaton that models the CPPS and a specification of the controller to construct a qualitative system model. This qualitative system model is based on propositional logic and represents the CPPS in the reconfiguration context. Evaluations on an industrial use case and simulations from process engineering illustrate the effectiveness and examine the scalability of AutoConf.
... The RL supports the parameters adjustment through the learning process. Authors in [32] present a reconfiguration algorithm based on first-order logic, which can be integrated directly into the automation software. The goal of reconfiguration is to identify the necessary changes to a system in the presence of faults. ...
... No No Galaske et al. [28] No No No Zhang et al. [29] No No No Park et al. [30] No No No Balzereit et al. [32] No No No Bicocchi et al. [4] No No No Our Approach Yes Yes Yes as well as an initial repository of recovery services. In a nutshell, the context model is organised over the three perspectives of the product that is being produced, of the production process and of the structure of smart machines in the CPPS. ...
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Cyber-physical systems are hybrid networked cyber and engineered physical elements that record data (e.g. using sensors), analyse them using connected services, influence physical processes and interact with human actors using multi-channel interfaces. Examples of CPS interacting with humans in industrial production environments are the so-called cyber-physical production systems (CPPS), where operators supervise the industrial machines, according to the human-in-the-loop paradigm. In this scenario, research challenges for implementing CPPS resilience, promptly reacting to faults, concern: (i) the complex structure of CPPS, which cannot be addressed as a monolithic system, but as a dynamic ecosystem of single CPS interacting and influencing each other; (ii) the volume, velocity and variety of data (Big Data) on which resilience is based, which call for novel methods and techniques to ensure recovery procedures; (iii) the involvement of human factors in these systems. In this paper, we address the design of resilient cyber-physical production systems (R-CPPS) in digital factories by facing these challenges. Specifically, each component of the R-CPPS is modelled as a smart machine, that is, a cyber-physical system equipped with a set of recovery services, a Sensor Data API used to collect sensor data acquired from the physical side for monitoring the component behaviour, and an operator interface for displaying detected anomalous conditions and notifying necessary recovery actions to on-field operators. A context-based mediator, at shop floor level, is in charge of ensuring resilience by gathering data from the CPPS, selecting the proper recovery actions and invoking corresponding recovery services on the target CPS. Finally, data summarisation and relevance evaluation techniques are used for supporting the identification of anomalous conditions in the presence of high volume and velocity of data collected through the Sensor Data API. The approach is validated in a food industry real case study.
... For the experimental results, we use the Three-Tank System and a Two-Tank system which has already been used for reconfiguration purposes [28]. The system consists of two tanks T 1 , T 2 . ...
Preprint
Reconfiguration aims at recovering a system from a fault by automatically adapting the system configuration, such that the system goal can be reached again. Classical approaches typically use a set of pre-defined faults for which corresponding recovery actions are defined manually. This is not possible for modern hybrid systems which are characterized by frequent changes. Instead, AI-based approaches are needed which leverage on a model of the non-faulty system and which search for a set of reconfiguration operations which will establish a valid behavior again. This work presents a novel algorithm which solves three main challenges: (i) Only a model of the non-faulty system is needed, i.e. the faulty behavior does not need to be modeled. (ii) It discretizes and reduces the search space which originally is too large -- mainly due to the high number of continuous system variables and control signals. (iii) It uses a SAT solver for propositional logic for two purposes: First, it defines the binary concept of validity. Second, it implements the search itself -- sacrificing the optimal solution for a quick identification of an arbitrary solution. It is shown that the approach is able to reconfigure faults on simulated process engineering systems.
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This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.
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Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.
Conference Paper
In this work we propose several algorithms to solve the reconfiguration problem for linear and hybrid systems. In particular, we consider the decision about the usage of redundant hardware in order to compensate for faults. While this problem can be translated into a constrained model predictive control framework, the computational complexity grows very fast, as the number of possible decisions increases. In this work we propose schemes, that require low computational effort. We discuss the applicability of the methods considering the reconfiguration of the three tank benchmark system
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The bond-graph method is a graphical approach to modeling in which component energy ports are connected by bonds that specify the transfer of energy between system components. Power, the rate of energy transport between components, is the universal currency of physical systems. Bond graphs are inherently energy based and thus related to other energy-based methods, including dissipative systems and port-Hamiltonians. This article has presented an introduction to bond graphs for control engineers. Although the notation can initially appear daunting, the bond graph method is firmly grounded in the familiar concepts of energy and power. The essential element to be grasped is that bonds represent power transactions between components
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A standardized language for reuse and exchange of models is needed. An international design group has designed such a language called Modelica. Modelica is a modern language built on non-causal modeling with mathematical equations and object-oriented constructs to facilitate reuse of modeling knowledge.
  • I Matei
  • J De Kleer
  • A Feldman
  • R Rai
  • S Chowdhury
I. Matei, J. de Kleer, A. Feldman, R. Rai, and S. Chowdhury, "Hybrid modeling: Applications in real-time diagnosis," arXiv preprint arXiv:2003.02671, 2020.
Qualitative simulation
  • B Kuipers
B. Kuipers, "Qualitative simulation," Encyclopedia of physical science and technology, vol. 3, pp. 287-300, 2001.
IEC 61360 -Standard data element types with associated classification scheme
IEC, IEC 61360 -Standard data element types with associated classification scheme, 2017.
Hybrid modeling: Applications in real-time diagnosis
  • I Matei
  • J De Kleer
  • A Feldman
  • R Rai
  • S Chowdhury
Standard data element types with associated classification scheme
  • Iec