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- Gopinath, Varun, 1982-
(författare)
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On Safe Collaborative Assembly With Large Industrial Robots
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis pertains to industrial safety in relation to human-robot collaboration. The aim is to enhance understanding of the nature of systems where large industrial robots collaborate with humans to complete assembly tasks. This understanding may support development and safe operations of future collaborative systems.Industrial robots are widely used to automate manufacturing operations across several industries. The automotive industry is the largest user of robots and have identified robot-based automation as a strategy to improve efficiency in manufacturing operations.Recently, a class of machines referred to as collaborative robots have been developed by robot manufacturers to support operators in assembly tasks. The use of these robots to support human workers in an industrial context are referred to as collaborative operations.Presently, collaborative robots have limited reach and load carrying capacity compared to standard industrial robots. Large/standard industrial robots are widely used for applications such as welding or painting. They can, in principle support operators in assembly tasks as well.Two laboratory demonstrators representing the final results from a series of research activities will be presented. They were developed to investigate issues related to personnel and process safety while working with large industrial robots in collaborative operations. The demonstrators were partially based on assembly workstations that are currently operational and they exemplify challenges faced by the automotive industry.Demonstrator-based Research, a methodology for collaborative research that emphasizes development of demonstrators as a research tool, forms the rationale for carrying out research operations presented in this thesis. An evaluation of the laboratory demonstrators by industrial participants suggests an increased interest and confidence in collaborative operations with large robots. The demonstrators have served as a tentative platform for participants to identify and discuss manufacturing and safety challenges in relation to their organization.A main outcome presented in this thesis relates to specifying requirements for introducing robots in a human-populated environment. Introducing robotic systems in new environments requires reconsideration of the nature of the hazards particular to the domain. An analysis of the laboratory demonstrators suggest that, in addition to hazards associated with normal functioning of the system, limitations in human cognition must be considered. These results will be exemplified and discussed in the context of situational and mode awareness. Additionally, a model of a collaborative workstation will be presented in terms of three constituents – workspace, tasks and interaction.This is particularly significant considering the direction of present-day research aimed at introducing robots across various industries and working environments. In response to this trend, this thesis discusses the relevance of Interactive Research and its emphasis on joint learning that goes on between academic researchers and industrial participants as a valuable principle for collaborative research.
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| 2. |
- Jung, Daniel, 1984-
(författare)
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Diagnosability performance analysis of models and fault detectors
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Model-based diagnosis compares observations from a system with predictions using a mathematical model to detect and isolate faulty components. Analyzing which faults that can be detected and isolated given the model gives useful information when designing a diagnosis system. This information can be used, for example, to determine which residual generators can be generated or to select a sufficient set of sensors that can be used to detect and isolate the faults. With more information about the system taken into consideration during such an analysis, more accurate estimations can be computed of how good fault detectability and isolability that can be achieved.Model uncertainties and measurement noise are the main reasons for reduced fault detection and isolation performance and can make it difficult to design a diagnosis system that fulfills given performance requirements. By taking information about different uncertainties into consideration early in the development process of a diagnosis system, it is possible to predict how good performance can be achieved by a diagnosis system and avoid bad design choices. This thesis deals with quantitative analysis of fault detectability and isolability performance when taking model uncertainties and measurement noise into consideration. The goal is to analyze fault detectability and isolability performance given a mathematical model of the monitored system before a diagnosis system is developed.A quantitative measure of fault detectability and isolability performance for a given model, called distinguishability, is proposed based on the Kullback-Leibler divergence. The distinguishability measure answers questions like "How difficult is it to isolate a fault fi from another fault fj?. Different properties of the distinguishability measure are analyzed. It is shown for example, that for linear descriptor models with Gaussian noise, distinguishability gives an upper limit for the fault to noise ratio of any linear residual generator. The proposed measure is used for quantitative analysis of a nonlinear mean value model of gas flows in a heavy-duty diesel engine to analyze how fault diagnosability performance varies for different operating points. It is also used to formulate the sensor selection problem, i.e., to find a cheapest set of available sensors that should be used in a system to achieve required fault diagnosability performance.As a case study, quantitative fault diagnosability analysis is used during the design of an engine misfire detection algorithm based on the crankshaft angular velocity measured at the flywheel. Decisions during the development of the misfire detection algorithm are motivated using quantitative analysis of the misfire detectability performance showing, for example, varying detection performance at different operating points and for different cylinders to identify when it is more difficult to detect misfires.This thesis presents a framework for quantitative fault detectability and isolability analysis that is a useful tool during the design of a diagnosis system. The different applications show examples of how quantitate analysis can be applied during a design process either as feedback to an engineer or when formulating different design steps as optimization problems to assure that required performance can be achieved.
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