| 61. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
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From factory floor to process models: A data gathering approach to generate, transform, and visualize manufacturing processes
- 2019
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Ingår i: CIRP Journal of Manufacturing Science and Technology. - : Elsevier BV. - 1755-5817 .- 1878-0016. ; 24, s. 6-16
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Forskningsöversikt (refereegranskat)abstract
- The need for tools to help guide decision making is growing within the manufacturing industry. The analysis performed by these tools will help operators and engineers to understand the behaviour of the manufacturing stations better and thereby take data-driven decisions to improve them. The tools use techniques borrowed from fields such as Data Analytics, BigData, Predictive Modelling, and Machine Learning. However, to be able to use these tools efficiently, data from the factory floor is required as input. This data needs to be extracted from two sources, the PLCs, and the robots. In practice, methods to extract usable data from robots are rather scarce. The present work describes an approach to capture data from robots, which can be applied to both legacy and current state-of-the-art manufacturing systems. The described approach is developed using Sequence Planner - a tool for modelling and analyzing production systems - and is currently implemented at an automotive company as a pilot project to visualize and examine the ongoing process. By exploiting the robot code structure, robot actions are converted to event streams that are abstracted into operations. We then demonstrate the applicability of the resulting operations, by visualizing the ongoing process in real-time as Gantt charts, that support the operators performing maintenance. And, the data is also analyzed off-line using process mining techniques to create a general model that describes the underlying behaviour existing in the manufacturing station. Such models are used to derive insights about relationships between different operations, and also between resources.
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| 62. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
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MIDES: A Tool for Supervisor Synthesis via Active Learning
- 2021
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2021-August, s. 792-797
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Konferensbidrag (refereegranskat)abstract
- A tool, MIDES, for automatic learning of models and supervisors for discrete event systems is presented. The tool interfaces with a simulation of the target system to learn a behavioral model through interaction. There are several different algorithms to choose from depending on the intended outcome. Moreover, given a set of specifications, the tool learns a supervisor that can help ensure the controlled system guarantees the specifications. Furthermore, the state-space explosion problem is addressed by learning a modular supervisor. In this paper, we introduce the tool, its interfaces, and algorithms. We demonstrate the usefulness through several case studies.
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| 63. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
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Real-time Visualization of Robot Operation Sequences
- 2018
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Ingår i: IFAC-PapersOnLine. - : Elsevier BV. - 2405-8963. ; 51:11, s. 576-581
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Konferensbidrag (refereegranskat)abstract
- Evaluation of manufacturing systems requires large amounts of accurate data from the factory floor. This data is then processed to calculate Key Performance Indicators (KPIs), evaluation metrics used within the manufacturing industry by engineers and managers in order to make data-driven decisions. Mechanisms to capture large scales of usable data, which is both reliable and scalable is, more often than not, scarce. In this paper, we provide an approach to capture data from robot actions, which can be applied to both legacy and current state-of-the-art manufacturing systems. By exploiting the robot code structure, robot actions are converted to event streams that are transformed into a higher usable abstraction of data. Applicability of this data is demonstrated, primarily, by visualizations. The described approach is developed in Sequence Planner - a tool for modeling and analyzing production systems - and is currently implemented at an automotive company as a pilot project to visualize and examine what goes on on the factory floor.
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| 64. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
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Synthesis of Supervisors for Unknown Plant Models Using Active Learning
- 2019
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; August-2019, s. 502-508
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Konferensbidrag (refereegranskat)abstract
- This paper proposes an approach to synthesize a discrete-event supervisor to control a plant, the behavior model of which is unknown, so as to satisfy a given specification. To this end, the $L^{*}$ algorithm is modified so that it can actively query a plant simulation and the specification to hypothesize a supervisor. The resulting hypothesis is the maximally permissive controllable supervisor from which the maximally permissive controllable and non-blocking supervisor can be extracted. The practicality of this method is demonstrated by an example.
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| 65. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
-
Towards Automatic Learning of Discrete-Event Models from Simulations
- 2018
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2018-August, s. 857-862
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Konferensbidrag (refereegranskat)abstract
- Model-based techniques are, these days, being embraced by the manufacturing industry in their development frameworks. While model-based approaches allow for offline verification and validation before physical commissioning, and have other advantages over existing methods, they do have their own challenges. Firstly, models are typically created manually and hence are prone to errors. Secondly, once a model is created, tested, and put into use on the factory floor, there is an added effort required to maintain and update it. This paper is a preliminary study of the feasibility of automatically obtaining formal models from virtual simulations. We apply the foundational algorithm from the active automata learning community to study the requirements and enhancements needed to be able to derive discrete event models from virtual simulations. An abstract model in the form of operations is learned by applying this algorithm on a simulation model composed of discrete operations. While a major bottleneck to be solved is the generation of counterexamples, the results seem promising to apply model learning in practice.
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| 66. |
- Farooqui, Ashfaq Hussain, 1990, et al.
(författare)
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Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations
- 2020
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Ingår i: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; 58:16, s. 4947-4963
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Tidskriftsartikel (refereegranskat)abstract
- Published by Informa UK Limited, trading as Taylor & Francis Group. The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner–a tool for modelling and analysing production systems–and is currently implemented at an automotive company as a pilot project.
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| 67. |
- Farooqui, Ashfaq, et al.
(författare)
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On Active Learning for Supervisor Synthesis
- 2022
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Ingår i: IEEE Transactions on Automation Science and Engineering. - : Institute of Electrical and Electronics Engineers Inc.. - 1545-5955 .- 1558-3783.
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Tidskriftsartikel (refereegranskat)abstract
- Supervisory control theory provides an approach to synthesize supervisors for cyber-physical systems using a model of the uncontrolled plant and its specifications. These supervisors can help guarantee the correctness of the closed-loop controlled system. However, access to plant models is a bottleneck for many industries, as manually developing these models is an error-prone and time-consuming process. An approach to obtaining a supervisor in the absence of plant models would help industrial adoption of supervisory control techniques. This paper presents, an algorithm to learn a controllable supervisor in the absence of plant models. It does so by actively interacting with a simulation of the plant by means of queries. If the obtained supervisor is blocking, existing synthesis techniques are employed to prune the blocking supervisor and obtain the controllable and non-blocking supervisor. Additionally, this paper presents an approach to interface the with a PLC to learn supervisors in a virtual commissioning setting. This approach is demonstrated by learning a supervisor of the well-known example simulated in Xcelgo Experior and controlled using a PLC. interacts with the PLC and learns a controllable supervisor for the simulated system. Note to Practitioners—Ensuring the correctness of automated systems is crucial. Supervisory control theory proposes techniques to help build control solutions that have certain correctness guarantees. These techniques rely on a model of the system. However, such models are typically unavailable and hard to create. Active learning is a promising technique to learn models by interacting with the system to be learned. This paper aims to integrate active learning and supervisory control such that the manual step of creating models is no longer needed, thus, allowing the use of supervisory control techniques in the absence of models. The proposed approach is implemented in a tool and demonstrated using a case study.
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| 68. |
- Fasth, Åsa, 1978, et al.
(författare)
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From Task Allocation Towards Resource Allocation when Optimising Assembly Systems
- 2012
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Ingår i: Procedia CIRP - Elsevier. - 2212-8271. ; 3, s. 400-405
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Tidskriftsartikel (refereegranskat)abstract
- The article discusses the question; is it possible to reach route flexibility and system proactivity through resource allocation and task optimisation. In order to answer this, differences between three types of optimisation regarding task and resource allocation are discussed: Global Task and Resource optimisation, Task optimisation and local resource allocation, but with resource alternatives, Task optimisation and local resource allocation (optimisation), with prioritised resources, shown as a possible solution in this paper in order to increase the route flexibility and proactivity in the system planning. An example of the last approach will be shown using a logic language (SOP) with help of software tool called Sequence Planner (SP).
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| 69. |
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| 70. |
- Flordal, Hugo, 1977, et al.
(författare)
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Automatic model generation and PLC-code implementation for interlocking policies in industrial robot cells
- 2007
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Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661. ; 15:11, s. 1416-1426
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Tidskriftsartikel (refereegranskat)abstract
- In industrial production lines, for example in the automotive industry, cells with multiple industrial robots are common. In such cells, each robot has to avoid running into static obstacles and when the robots work together in a shared space they must also avoid colliding with each other. Typically. the latter is enforced by manually implementing interlocks in programmable logic controllers (PLCs). This is a tedious. error-prone task that is a bottleneck in the development of production lines. The PLC-code being man-made also greatly complicates the maintenance and reconfiguration of such production lines. However, in industry today, a lot of development of robot cells is made offline in 3D simulation environments which enables the use of computers also for deciding and implementing the necessary coordination. This paper presents a method that makes use of information in a robot simulation environment in order to automatically extract finite state models. These models can be used to generate supervisors for ensuring that the deadlock situations that may arise as a consequence of the introduced interlocks are avoided. It is also possible to optimize the work cycle time for the cell. Finally, PLC-code to supervise the production cell can be automatically generated from the deadlock-free and possibly optimized system model. This approach results in a high flexibility in that the coordination function can be quickly reimplemented whenever necessary. A prototype implementation has been developed making use of a commercial 3D robot simulation tool, and a software tool for supervisor synthesis and code generation. The approach is general and should be possible to implement in most offline robot simulation tools. (c) 2007 Elsevier Ltd. All rights reserved.
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