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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First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems

2021 , Diedrich, Alexander , Balzereit, Kaja , Niggemann, Oliver

Maintaining modern production machinery requires a significant amount of time and money. Still, plants suffer from expensive production stops and downtime due to faults within individual components. Often, plants are too complex and generate too much data to make manual analysis and diagnosis feasible. Instead, faults often occur unnoticed, resulting in a production stop. It is then the task of highly-skilled engineers to recognise and analyse symptoms and devise a diagnosis. Modern algorithms are more effective and help to detect and isolate faults faster and more precise, thus leading to increased plant availability and lower operating costs.In this paper we attempt to solve some of the described challenges. We describe a concept for an automated framework for hybrid cyberphysical production systems performing two distinct tasks: 1) fault diagnosis and 2) reconfiguration. For diagnosis, the inputs are connection and behaviour models of the components contained within the system and a model describing their causal dependencies. From this information the framework is able to automatically derive a diagnosis provided a set of known symptoms. Taking the output of the diagnosis as a foundation, the reconfiguration part generates a new configuration, which, if applicable, automatically recovers the plant from its faulty state and resumes production. The described concept is based on predicate logic, specifically Satisfiability-Modulo-Theory. The input models are transformed into logical predicates. These predicates are the input to an implementation of Reiter's diagnosis algorithm, which identifies the minimum-cardinality diagnosis. Taking this diagnosis, a reconfiguration algorithm determines a possible, alternative control, if existing. Therefore the current system structure described by the connection and component models is analysed and alternative production plans are searched. If such an alternative plan exists, it is transmitted to the control of the system. Otherwise, an error that the system is not reconfigurable is returned.

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Diagnosing Systems through Approximated Information

2021 , Diedrich, Alexander , Niggemann, Oliver

This article presents a novel approach to diagnose faults in production machinery. A novel data-driven approach is presented to learn an approximation of dependencies between variables using Spearman correlation. It is further shown, how the approximation of the dependencies are used to create propositional logic rules for fault diagnosis. The article presents two novel algorithms: 1) to estimate dependencies from process data and 2) to create propositional logic diagnosis rules from those connections and perform consistency based fault diagnosis. The presented approach was validated using three experiments. The first two show that the presented approach works well for injection molding machines and a simulation of a four-tank system. The limits of the presented method are shown with the third experiment containing sets of highly correlated signals.

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Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods

2021 , Zimmering, Bernd , Niggemann, Oliver , Hasterok, Constanze , Pfannstiel, Erik , Ramming, Dario , Pfrommer, Julius

In the field of Cyber-Physical Systems (CPS), there is a large number of machine learning methods, and their intrinsic hyper-parameters are hugely varied. Since no agreed-on datasets for CPS exist, developers of new algorithms are forced to define their own benchmarks. This leads to a large number of algorithms each claiming benefits over other approaches but lacking a fair comparison. To tackle this problem, this paper defines a novel model for a generation process of data, similar to that found in CPS. The model is based on well-understood system theory and allows many datasets with different characteristics in terms of complexity to be generated. The data will pave the way for a comparison of selected machine learning methods in the exemplary field of unsupervised learning. Based on the synthetic CPS data, the data generation process is evaluated by analyzing the performance of the methods of the Self-Organizing Map, One-Class Support Vector Machine and Long Short-Term Memory Neural Net in anomaly detection.

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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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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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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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On Residual-based Diagnosis of Physical Systems

2022 , Diedrich, Alexander , Niggemann, Oliver

In this article we describe a novel diagnosis methodology for physical systems such as industrial production systems. The article consists of two parts: Part one analyzes the differences between using sensor values and using residual values for fault diagnosis. Residual values denote the health of a component by comparing sensor values to a predefined model of normal behaviour. We further analyse how faults propagate through components of a physical system and argue for the use of residual values for diagnosing physical systems. In part two we extend the theory of established consistency-based diagnosis algorithms to use residual values. We also illustrate how users of the presented diagnosis methodology are free to substitute the residual generating equations and the diagnosis algorithm to suit their specific needs. For diagnosis, we present the algorithm HySD, based on Satisfiability Modulo Linear Arithmetic. We present an implementation of HySD using threshold values and a symbolic diagnosis approach. However, the approach is also suitable to integrate modern machine learning methods for anomaly detection and combine them with a multitude of diagnosis approaches. Through experiments on the process-industry benchmark Tennessee Eastman Process and another benchmark consisting of multiple tank systems we show the feasibility of our approach. Overall we show how our novel diagnosis approach offers a practical methodology that allows industry to advance from current state of the art anomaly detection to automated fault diagnosis.

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A Generic DigitalTwin Model for Artificial Intelligence Applications

2021 , Niggemann, Oliver , Diedrich, Alexander , Kühnert, Christian , Pfannstiel, Erik , Schraven, Joshua

Services for Cyber-Physical Systems based on Artificial Intelligence and Machine Learning require a virtual representation of the physical. To reduce modeling efforts and to synchronize results, for each system, a common and unique virtual representation used by all services during the whole system life-cycle is needed-i.e. a DigitalTwin. In this paper such a DigitalTwin, namely the AI reference model AITwin, is defined. This reference model is verified by using a running example from process industry and by analyzing the work done in recent projects.

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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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