Expert error in trouble-shooting: an exploratory study in electronics

International audienceIt is known that novices show poor problem-solving performances and that they engage in a relatively inefficient inferential reasoning mode. Experts show high performances in routine situations in which they only activate knowledge. The main purpose of this work was to test the hypothesis that, under some conditions, novices may develop a more efficient diagnostic reasoning than experts, i.e. they may discover the cause of a faulty system conducting fewer tests while avoiding fixation errors. This hypothesis mainly relies on the possibility that experts may be victims of their own knowledge format (French and Sternberg, manuscript). It is tested in a faulty electronic circuit trouble-shooting task. Data suggest that novices perform better than experts. Results are discussed with reference to the concepts of schema and expert error

Troubleshooting in Mechanics: A Heuristic Matching Process

International audienceThis paper deals with expert operators' reasoning processes in troubleshooting. We want to know more about the information that experienced operators use. In a previous study we studied electronics troubleshooting. We found that experts used surface cues in order to implement heuristic rules even if the latter are not relevant to the current fault. We now wish to study the field of mechanics. An experiment was conducted in order to test the hypothesis of a heuristic rule-based level of control responsible for errors among experts. This paper adopts a naturalistic and ergonomic point of view about troubleshooting in mechanics. Our results show that expert mechanics operators' errors rely on heuristics in the troubleshooting process. This strategy relies on an automated matching process between symptoms and procedures. Although this strategy is usually powerful, it is rigid and may lead the operator to not locate the fault of the latter is atypica

Automatización planta de embotellado de la línea de proceso de transformaciones vegetales de la facultad de ingeniería de La Universidad La Gran Colombia Seccional Armenia, aplicando control borroso para la detección de alarmas.

This article presents the automation of bottling plant, and it suggests a predictablemaintenance system based on Logic Fuzzy techniques. For the implementation ofthe prototype, the programming software was used for the controller Zelio of Schneider Electric.Este artículo presenta la automatización de una planta de embotellamiento ypropone un sistema de mantenimiento predictivo basado en técnicas de Lógica Borrosa.Para la implementación del prototipo, se utilizó el software de programación para los autómatas Zelio de Schneider Electric

Experimental calibration and validation of a simulation model for fault detection of HVAC systems and application to a case study

Automated fault detection and diagnostics (FDD) could provide a cornerstone for predictive maintenance of heating, ventilation and air-conditioning (HVAC) systems based on the development of simulation models able to accurately compare the faulty operation with respect to nominal conditions. In this paper, several experiments have been carried out for assessing the performance of the HVAC unit (nominal cooling/heating capacity of 5.0/5.0 kW) controlling the thermo-hygrometric comfort inside a 4.0 × 4.0 × 3.6 m test room at the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Italy); then, a detailed dynamic simulation model has been developed and validated by contrasting the predictions with the measured data. The model has also been used to analyze the dynamic variations of key parameters associated to faulty operation in comparison to normal performance, in order to identify simplified rules for detection of any non-optimal states of HVAC devices. Finally, the simulated performance of the HVAC unit has also been investigated while serving a typical Italian building office with and without the occurrence of typical faults with the main aim of assessing the impact of the faults on thermo-hygrometric comfort conditions as well as electric energy consumption

Design Methodology for Unmanned Aerial Vehicle (UAV) Team Coordination

Unmanned Aerial Vehicle (UAV) systems, despite having no onboard human pilots, currently require extensive human involvement to accomplish successful mission operations. Further, successful operations also require extensive colalboration between mission stakeholders, including operators, mission commanders, and information consumers (e.g. ground troops relying on intelligence reports in their area). Existing UAV system interfaces provide little to no support for collaboration between remote operators or for operators to collaborate with information consumers. As reliance on UAVs continues to increase in military and civilian operations, this lack of support for collaboration will likely become a substantial limitation of existing UAV systems. In order to introduce effective collaboration support to UAV system interfaces, it is essential to understand, and be able to derive system design requirements that address, the necessary group interactions that occur in UAV task enviroments. However, few collaborative requirements analysis methods exist, and to our knowledge, no method exists that captures design requirements for collaborative decision making in complex, time-critical environments. This report describes the development of a new design requirements analysis method for deriving information and functional requirements that address the collaboration needs of UAV (and other complex task) operators, and the needs of stakeholders interacting with these operators. More specifically, theis method extends a recently developed requirements analysis method, called the Hybrid Cognitive Task Analysis (CTA) method, which enables the generation of information and functional requirements for futuristic UAV system interfaces. The original Hybrid CTA method focused on deriving single user system interface requirements. This work extends this method by introducing analytic steps to identify task and decision-making dependencies between different UAV operations collaborators. This collaborative extension to the Hybrid CTA utilizes the notion of boundary objects, an analytic construct commonly used in the study of group work. Boundary objects are physical or information artifacts that cross the task boundaries between members of distinct groups. Identifying boundary objects in complex task operations help the analyst to identify task and decision-making dependencies between local and remote collaborators. Understanding these dependencies helps to identify information sharing requirements that the UAV system should support. This report describes the analytic steps of the collaborative extension, and provides background information on the original Hybrid CTA method and the boundary object construct. The report also describes a project in which the new design requirements method was used to revise a proposed set of UAV operator displays.Prepared For Boeing Phantom Work

RULES BASED MODELING OF DISCRETE EVENT SYSTEMS WITH FAULTS AND THEIR DIAGNOSIS

Failure diagnosis in large and complex systems is a critical task. In the realm of discrete event systems, Sampath et al. proposed a language based failure diagnosis approach. They introduced the diagnosability for discrete event systems and gave a method for testing the diagnosability by first constructing a diagnoser for the system. The complexity of this method of testing diagnosability is exponential in the number of states of the system and doubly exponential in the number of failure types. In this thesis, we give an algorithm for testing diagnosability that does not construct a diagnoser for the system, and its complexity is of 4th order in the number of states of the system and linear in the number of the failure types. In this dissertation we also study diagnosis of discrete event systems (DESs) modeled in the rule-based modeling formalism introduced in [12] to model failure-prone systems. The results have been represented in [43]. An attractive feature of rule-based model is it\u27s compactness (size is polynomial in number of signals). A motivation for the work presented is to develop failure diagnosis techniques that are able to exploit this compactness. In this regard, we develop symbolic techniques for testing diagnosability and computing a diagnoser. Diagnosability test is shown to be an instance of 1st order temporal logic model-checking. An on-line algorithm for diagnosersynthesis is obtained by using predicates and predicate transformers. We demonstrate our approach by applying it to modeling and diagnosis of a part of the assembly-line. When the system is found to be not diagnosable, we use sensor refinement and sensor augmentation to make the system diagnosable. In this dissertation, a controller is also extracted from the maximally permissive supervisor for the purpose of implementing the control by selecting, when possible, only one controllable event from among the ones allowed by the supervisor for the assembly line in automaton models