Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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AutoConf: A New Algorithm for Reconfiguration of Cyber-Physical Production Systems

2023 , Balzereit, Kaja , Niggemann, Oliver

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.

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Sound and Complete Reconfiguration for a Class of Hybrid Systems

2021 , Balzereit, Kaja , Niggemann, Oliver

Reconfiguration is the automated recovery from a fault in hybrid systems such as tank systems from process engineering. Until today, many heuristics for this purpose have been developed. However, the reliability of these heuristics is not discussed or approximated using artificial systems. In this article, soundness and completeness properties are defined in the context of reconfiguration. In addition, an algorithm based on the transformation of the reconfiguration problem to search and a subsequent solution using Best-First Search is presented. It is shown that this algorithm is sound and complete for a class of hybrid systems.

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Modeling Quantitative Effects for the Reconfiguration of Hybrid Systems

2020 , Balzereit, Kaja , Niggemann, Oliver

Reconfiguration is the task of recovering a valid system state after an error has occurred, which led to an invalid system state. Especially for hybrid systems, identifying the necessary changes to restore valid system functioning is challenging: Hybrid systems contain continuous and discrete variables, leading to an infinite search space which, in addition, suffers from combinatorial explosion. Existing approaches to the reconfiguration problem mostly require a pre-definition of faults and a large amount of expert knowledge and thus, enable the system to adapt only to known faults. This paper presents a novel approach which does not need a pre-definition of faults such that the system is enabled to adapt even to unknown faults. It works on an representation of the reconfiguration problem in a logical calculus. Therefore, the hybrid system is modeled in first-order logic. To integrate continuous variables, which have infinite domains, they are discretized using intervals. The approach is shown to reconfigure faults on simulated systems from process engineering. This way, the reconfiguration problem of hybrid systems can be modeled and solved efficiently.

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Data-driven Identification of Causal Dependencies in Cyber-Physical Production Systems

2019 , Balzereit, Kaja , Maier, Alexander , Barig, Björn , Hutschenreuther, Tino , Niggemann, Oliver

Cyber-Physical Systems (CPS) are systems that connect physical components with software components. CPS used for production are called Cyber-Physical Production Systems (CPPS). Since the complexity of these systems can be very high, finding the cause of an error takes a lot of effort. In this paper, a data-driven approach to identify causal dependencies in cyber-physical production systems (CPPS) is presented. The approach is based on two different layers of learning algorithms: one low-level layer that processes the direct machine data and a higher-level learning layer that processes the output of the low-level layer. The low-level layer is based on different learning modules that can process differently typed data (continuous, discrete or both). The high-level learning algorithms are based on rule-based and case-based reasoning. Thus, causal dependencies are detected allowing the plant operator to find the error cause quickly.

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An AI benchmark for Diagnosis, Reconfiguration & Planning

2022 , Ehrhardt, Jonas , Ramonat, Malte , Heesch, Rene , Balzereit, Kaja , Diedrich, Alexander , Niggemann, Oliver

To improve the autonomy of Cyber-Physical Production Systems (CPPS), a growing number of approaches in Artificial Intelligence (AI) is developed. However, implementations of such approaches are often validated on individual use-cases, offering little to no comparability. Though CPPS automation includes a variety of problem domains, existing benchmarks usually focus on single or partial problems. Additionally, they often neglect to test for AI-specific performance indicators, like asymptotic complexity scenarios or runtimes. Within this paper we identify minimum common set requirements for AI benchmarks in the domain of CPPS and introduce a comprehensive benchmark, offering applicability on diagnosis, reconfiguration, and planning approaches from AI. The benchmark consists of a grid of datasets derived from 16 simulations of modular CPPS from process engineering, featuring multiple functionalities, complexities, and individual and superposed faults. We evaluate the benchmark on state-of-the-art AI approaches in diagnosis, reconfiguration, and planning. The benchmark is made publicly available on GitHub.

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An Ensemble of Benchmarks for the Evaluation of AI Methods for Fault Handling in CPPS

2021 , Balzereit, Kaja , Diedrich, Alexander , Ginster, Jonas , Windmann, Stefan , Niggemann, Oliver

AI methods for fault handling in Cyber-Physical Production Systems (CPPS) such as production plants and tank systems are an emerging research topic. In the last years many methods for the detection of anomalies and faults, the diagnosis of the root cause and the automated repair have been developed. However, most of the methods are barely evaluated using a wide range of systems but applicability is shown using single use cases. In this paper, an ensemble of simulated benchmark systems is presented, which allows for a broad evaluation of AI methods for fault handling. The ensemble consists of seven different tank systems from process engineering with varying sizes and complexities and is made publicly available on Github. The suitability of the ensemble is shown using AI methods for fault handling such as anomaly detection, diagnosis and reconfiguration.

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Automated Reconfiguration of Cyber-Physical Production Systems using Satisfiability Modulo Theories

2020 , Balzereit, Kaja , Niggemann, Oliver

Today, Cyber-Physical Production Systems (CPPS) are controlled by manually written software, therefore the software is not able to adapt to unforeseen events and faults. So even if a fault is diagnosed automatically, the system normally needs to be repaired manually by a human operator. So to implement the vision of an autonomous system, besides self-diagnosis also a self-reconfiguration or self-repair step is needed. Here reconfiguration is the task of restoring valid system behavior after an invalid system behavior occurred. For complex CPPS, finding such a new valid configuration always requires a system model covering all potential new configurations-only for rather simple systems the possible reconfigurations for a fault can be modeled explicitly. Unfortunately, such models are hardly available for complex systems. This paper presents a novel approach for the automated reconfiguration of CPPS to solve this challenge. It is based on the combination of residual-based fault detection and logical calculi to draw causal coherences. The approach operates on observed system data and information about the system topology. By doing this, the modeling efforts are reduced. To evaluate the new approach, a simulation of such CPPS is used.

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Gradient-based Reconfiguration of Cyber-Physical Production Systems

2021 , Balzereit, Kaja , Niggemann, Oliver

Cyber-physical production systems (CPPS) are susceptible to various faults like failing components or leaking connections. Even though CPPS have many possibilities to adapt to faults like using an alternative path or redundant hardware, the full potential is not exploited: Nowadays, the control software of CPPS is static and not able to adapt to unforeseen situations like an unknown fault. Hence, CPPS are not able to adapt to faults but a machine operator needs to reconfigure the system manually. Reconfiguration is the task of recovering valid system behavior after a fault has occurred. To enable CPPS to adapt autonomously to faults, automated reconfiguration is necessary. But since CPPS usually are dynamic systems consisting of interconnections of various components, identifying the necessary changes for reconfiguration is challenging: The effects of changes may only manifest after some time and lead to deviations in components far away from their root. This paper presents an approach on automated reconfiguration for CPPS, i.e. the automated recovery from faults. The approach is based on the usage of a logical calculus that reasons about the consequences of a fault and the possible adaptions. Therefore, the system is modeled in terms of logic. Gradient information is integrated to capture the system dynamics. The applicability of the algorithm is shown using a benchmark system from process engineering. Thus, CPPS are enabled to adapt to faults autonomously.

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A Concept for the Automated Reconfiguration of Quadcopters

2020 , Balzereit, Kaja , Fullen, Marta , Niggemann, Oliver

Quadcopters are susceptible to internal and external influences, many of which may lead to faults. To ensure a safe and reliable flight, the quadcopter needs to recover autonomously from faults. However, existing approaches mainly rely on parametrical faults or require a predefinition of possible faults which is not realistic for a complex realworld scenario. The recovery from unforeseen faults and structural faults like a failing engine is still an open research gap. Hence, in this paper, a concept for the automated reconfiguration, i.e. the automated recovery from a fault, which only uses information about non-faulty system behavior and is able to handle structural changes is presented. From the information about non-faulty behavior a non-faulty system model is created using established machine learning methods. Thus, faults are detected by learned model and no pre-definition of faults is needed. The system structure is modeled using a logical calculus which allows for modeling available system parts and the causal coherences between these. The approach is applied to a simulation of a quadcopter which underlies a structural fault. It is shown that the approach extends the capabilities of a quadcopter to handle faults autonomously and ensure stability and reliability.

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Model-based routing in flexible manufacturing systems

2019 , Windmann, Stefan , Balzereit, Kaja , Niggemann, Oliver

In this paper, a model-based routing approach for flexible manufacturing systems (FMS) with alternative routes for the work pieces is proposed. For each work piece, an individual task has to be accomplished, which consists of several processing steps. Each processing step can be executed on alternative working stations of the FMS. The proposed routing method employs a model of the conveying system to find energy efficient and fast routes for the respective work pieces. The conveying system model is based on a directed graph, where the individual conveyors are modeled as weighted edges. It can be straightforwardly applied to several types of FMS by adjusting the application-dependent parameters. Efficient computation of the fastest route through the conveying system is accomplished by means of dynamic programming, i. e., by integration of Dijkstra's algorithm in a dynamic programming framework, which is based on the proposed conveying system model. Additional consideration of energy efficiency aspects leads to a Mixed Integer Quadratically Constraint Program (MIQCP), which is solved by substitution of Dijkstra's algorithm by a branch and bound method. Experimental results for an application scenario, where the energy efficient routing method is applied to route work pieces between the working stations of an FMS, lead to 20 % reduction of energy consumption on average.

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