A novel qualitative prospective methodology to assess human error during accident sequences

Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.Fil: Calvo Olivares, Romina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Rivera, Selva Soledad. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Núñez Mc Leod, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentin

The Operation of Autonomous Mobile Robot Assistants in the Environment of Care Facilities Adopting a User-Centered Development Design

The successful development of autonomous mobile robot assistants depends significantly on the well-balanced reconcilements of the technically possible and the socially desirable. Based on empirical research 2 substantiated conclusions can be established for the suitability of "scenario-based design" (Rosson/Carroll 2003) for the successful development of mobile robot assistants and automated guided vehicles to be applied for service functions in stationary care facilities for seniors.User-Centered Technology Development, Knowledge-Transfer, Participative Assessment Methods, Robotics

Sähkönsiirtoverkko-omaisuuden kunnonvalvontajärjestelmä Esineiden Internet -alustalla

Fingrid is making the transition from time-based maintenance to condition-based maintenance in order to increase the cost-efficiency of substation asset condition management and to prevent equipment failures. Digitalization improves real-time visibility to asset condition as Fingrid is developing an Internet of Things (IoT) concept for online asset condition monitoring. The objective of this Thesis was to specify Fingrid's requirements for asset condition data visualization in an Asset Performance Management (APM) application. A secondary objective was to document Fingrid's IoT concept and existing condition monitoring practices. Asset maintenance strategies, digitalization and Internet of Things were discussed as a background. For the IoT data of switchgear, simple illustrations of dashboards were drawn to show how the data could be visualized in an APM application. Regarding power transformers, the necessary basic elements for condition data visualization were reviewed. Specifications for visualization of all the available condition data were listed in Appendices. Online condition monitoring is currently concentrated on switchgear, power transformers and substation buildings. The IoT solution for switchgear consists of low-cost sensor units installed in switchgear control cabinets and bay marshalling cabinets. Power transformers are equipped with online DGA (Dissolved Gas Analysis) instruments which is a well-established practice. In substation buildings, the climate is monitored with low-cost IoT sensors. The conclusion of the study was that there are two types of condition data that determine Fingrid's requirements for data visualization in an APM application: time series data and event data. Monitoring of continuous processes produces a large amount of continuous time series data. A tool with efficient functionalities for time series data presentation and analysis is needed. Switchgear operations and oil sampling produce event data that require visualization in forms of data reporting and special analyses. The amount of event data is small even though it will increase due to IoT.Fingrid on siirtymässä aikaperusteisesta sähköasemien kunnossapidosta kuntoperusteiseen kunnossapitoon kustannustehokkuuden parantamiseksi ja laitevikojen ennalta ehkäisemiseksi. Reaaliaikaista näkyvyyttä omaisuuden kuntoon parannetaan kehittämällä "Esineiden Internet" (\textit{engl. Internet of Things, IoT}) -konsepti omaisuuden käytönaikaiseen kunnonvalvontaan. Tämän diplomityön tavoitteena oli laatia määrittelyt kunnonvalvontadatan visualisoinnille omaisuuden kunnonvalvontajärjestelmässä. Toisena tavoitteena oli dokumentoida Fingridin IoT-konsepti ja käytössä olevat perinteiset kunnonvalvontamenetelmät. Taustana työlle esiteltiin omaisuuden kunnonhallintamalleja, digitalisaatiota ja Esineiden Internetiä. Kytkinlaitteiden IoT-datan visualisoinnin tarpeiden hahmottamiseksi laadittiin luonnoksia kunnonvalvontajärjestelmän näkymistä. Muuntajien kunnonvalvonnan tarpeelliset osa-alueet kunnonvalvontajärjestelmässä käytiin läpi käytössä olevia ratkaisuja tutkimalla. Määrittelyt kaiken käytettävissä olevan kunnonvalvontadatan visualisoinnille listattiin työn liitteissä. Käytönaikainen kunnonvalvonta keskittyy tällä hetkellä kytkinlaitteisiin, muuntajiin ja sähköasemarakennuksiin. Kytkinlaitteiden IoT-ratkaisu koostuu kytkinlaitteen ohjainkaappiin sekä kytkinkentän jakokaappiin asennetuista kustannustehokkaista sensoriyksiköistä. Muuntajat on varustettu jatkuva-aikaisella vikakaasujen seurannalla, mikä on toimivaksi todettu ja vakiintunut kunnonvalvontamenetelmä. Asemarakennusten olosuhteita valvotaan huoneisiin sijoitetuilla IoT-sensoriyksiköillä. Työn johtopäätöksenä voidaan todeta kunnonvalvontadatan jakaantuvan pääasiassa kahteen muotoon, jotka määrittelevät Fingridin tarpeet kunnonvalvontajärjestelmälle. Jatkuva-aikaisten prosessien valvonta tuottaa suuren määrän jatkuvaa aikasarjadataa, minkä vuoksi tehokkaat työkalut aikasarjadatan esittämiselle ovat tarpeen. Kytkinlaitteiden ohjaukset ja muuntajien öljynäytteet puolestaan tuottavat tapahtumaperusteista dataa, jonka visualisointi muistuttaa datan raportointimenetelmiä. Tarvitaan erilaisia datan esitystapoja ja mukautettuja analysointimenetelmiä. Tapahtumadatan määrä on pieni siitä huolimatta, että sen määrä kasvaa huomattavasti IoT:n myötä

A platform for the development of patient applications in the domain of personalized health

Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases

Performance of control room operators in alarm management

Pipelines transport millions of barrels of petroleum products every day. These systems have significant safety concerns. The BP oil spill in the Gulf of Mexico, while concerned with oil and gas extraction rather than distribution, shares many of the same safety and reliability issues as distribution systems, and demonstrates the significant potential for major disasters in the pipeline industry. In this work, a research study is being conducted to further understanding of the role of operators in the management of alarm systems and to measure the performance of operators in handling abnormal situations like pressure loss, liquid inflow/outflow variation and alarm floods. In an Abnormal Situation Management (ASM) consortium traditional interface study, improving the human machine interaction (HMI) in designing the operator’s user interface resulted in 41% less time for the operators to deal with events like leaks, power failures, equipment malfunction and equipment failures in an unstable plant (Errington, 2005). To evaluate the impact of different alarm rates and interfaces on operator performance, a liquid pipeline simulation experiment of 1 hour was developed and the operators ran the experiment repeatedly at different alarm levels: chronological and categorical displays with the alarm rate of 15 alarms per 10 minutes (chronological display only), 20, 25 and 30 alarms per 10 minutes (the last rate with the categorical display only). Twenty five pipeline and refinery operators participated in this research, and the performance of operators was measured in terms of acknowledgement time, response time and the accuracy of response. Results showed that the operator’s performance in terms of response time was significantly different between 25 and 30 alarm rates. Experiments to compare the response times in both the alarm windows did not show significant difference statistically, but the means were better in categorical display. This study will be useful in developing new standards on operator performance

The explosion at institute: Modeling and analyzing the situation awareness factor

In 2008 a runaway chemical reaction caused an explosion at a methomyl unit in West Virginia, USA, killing two employees, injuring eight people, evacuating more than 40,000 residents adjacent to the facility, disrupting traffic on a nearby highway and causing significant business loss and interruption. Although the accident was formally investigated, the role of the situation awareness (SA) factor, i.e., a correct understanding of the situation, and appropriate models to maintain SA, remain unexplained. This paper extracts details of abnormal situations within the methomyl unit and models them into a situational network using dynamic Bayesian networks. A fuzzy logic system is used to resemble the operator's thinking when confronted with these abnormal situations. The combined situational network and fuzzy logic system make it possible for the operator to assess such situations dynamically to achieve accurate SA. The findings show that the proposed structure provides a useful graphical model that facilitates the inclusion of prior background knowledge and the updating of this knowledge when new information is available from monitoring systems