Cyber-Virtual Systems: Simulation, Validation & Visualization

We describe our ongoing work and view on simulation, validation and visualization of cyber-physical systems in industrial automation during development, operation and maintenance. System models may represent an existing physical part - for example an existing robot installation - and a software simulated part - for example a possible future extension. We call such systems cyber-virtual systems. In this paper, we present the existing VITELab infrastructure for visualization tasks in industrial automation. The new methodology for simulation and validation motivated in this paper integrates this infrastructure. We are targeting scenarios, where industrial sites which may be in remote locations are modeled and visualized from different sites anywhere in the world. Complementing the visualization work, here, we are also concentrating on software modeling challenges related to cyber-virtual systems and simulation, testing, validation and verification techniques for them. Software models of industrial sites require behavioural models of the components of the industrial sites such as models for tools, robots, workpieces and other machinery as well as communication and sensor facilities. Furthermore, collaboration between sites is an important goal of our work.Comment: Preprint, 9th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2014

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

On Falsification of Large-Scale Cyber-Physical Systems

In the development of modern Cyber-Physical Systems, Model-Based Testingof the closed-loop system is an approach for finding potential faults andincreasing quality of developed products. Testing is done on many differentabstraction levels, and for large-scale industrial systems, there are severalchallenges. Executing tests on the systems can be time-consuming and largenumbers of complex specifications need to be thoroughly tested, while manyof the popular academic benchmarks do not necessarily reflect on this complexity.This thesis proposes new methods for analyzing and generating test casesas a means for being more certain that proper testing has been performed onthe system under test. For analysis, the proposed approach can automaticallyfind out how much of the physical parts of the system that the test suite hasexecuted.For test case generation, an approach to find errors is optimization-basedfalsification. This thesis attempts to close the gap between academia and industryby applying falsification techniques to real-world models from VolvoCar Corporation and adapting the falsification procedure where it has shortcomingsfor certain classes of systems. Specifically, the main contributionsof this thesis are (i) a method for automatically transforming a signal-basedspecification into a formal specification allowing an optimization-based falsificationapproach, (ii) a new collection of specifications inspired by large-scalespecifications from industry, (iii) an algorithm to perform optimization-basedfalsification for such a large set of specifications, and (iv) a new type of coveragecriterion for Cyber-Physical Systems that can help to assess when testingcan be concluded.The proposed methods have been evaluated for both academic benchmarkexamples and real-world industrial models. One of the main conclusions isthat the proposed additions and changes to the analysis and generation oftests can be useful, given that one has enough information about the systemunder test. The methods presented in this thesis have been applied to realworldmodels in a way that allows for higher-quality products by finding morefaults in early phases of development

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components can lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic (STL) specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Falsification of Signal-Based Specifications for Cyber-Physical Systems

In the development of software for modern Cyber-Physical Systems, testing is an integral part that is rightfully given a lot of attention. Testing is done on many different abstraction levels, and especially for large-scale industrial systems, it can be difficult to know when the testing should conclude and the software can be considered correct enough for making its way into production. This thesis proposes new methods for analyzing and generating test cases as a means of being more certain that proper testing has been performed for the system under test. For analysis, the proposed approach includes automatically finding how much a given test suite has executed the physical properties of the simulated system. For test case generation, an up-and-coming approach to find errors in Cyber-Physical Systems is simulation-based falsification. While falsification is suitable also for some large-scale industrial systems, sometimes there is a gap between what has been researched and what problems need to be solved to make the approach tractable in the industry. This thesis attempts to close this gap by applying falsification techniques to real-world models from Volvo Car Corporation, and adapting the falsification procedure where it has shortcomings for certain classes of systems. Specifically, the thesis includes a method for automatically transforming a signal-based specification into a formal specification in temporal logic, as well as a modification to the underlying optimization problem that makes falsification more viable in an industrial setting. The proposed methods have been evaluated for both academic benchmark examples and real-world industrial models. One of the main conclusions is that the proposed additions and changes to analysis and generation of tests can be useful, given that one has enough information about the system under test. It is difficult to provide a general solution that will always work best -- instead, the challenge lies in identifying which properties of the given system should be taken into account when trying to find potential errors in the system

An industry 4.0 framework for the quality inspection in gearboxes production

Nowadays, the development of Internet of Things (IoT) technologies have been enhancing the factory digitalization with several advantages in terms of production efficiency, product quality, and cost reduction. This opportunity encourages the implementation of digital twins related to physical systems for controlling the production workflow in real time. Firstly, the paper studies the enabling technologies for supporting the defect analysis in the context of Industry 4.0 for mechanical workpieces. Secondly, the approach aims to study the integration between the CAD geometry and the quality check process for the inspection planning. A Knowledge-Based tool has been proposed to support the configurations of the quality control chain for each CAD geometry. The test case is focused on the fragmented production of customized gearbox parts