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.

How can systems engineering inform the methods of programme evaluation in health professions education?

CONTEXT

We evaluate programmes in health professions education (HPE) to determine their effectiveness and value. Programme evaluation has evolved from use of reductionist frameworks to those addressing the complex interactions between programme factors. Researchers in HPE have recently suggested a 'holistic programme evaluation' aiming to better describe and understand the implications of 'emergent processes and outcomes'.

FRAMEWORK

We propose a programme evaluation framework informed by principles and tools from systems engineering. Systems engineers conceptualise complexity and emergent elements in unique ways that may complement and extend contemporary programme evaluations in HPE. We demonstrate how the abstract decomposition space (ADS), an engineering knowledge elicitation tool, provides the foundation for a systems engineering informed programme evaluation designed to capture both planned and emergent programme elements.

METHODS

We translate the ADS tool to use education-oriented language, and describe how evaluators can use it to create a programme-specific ADS through iterative refinement. We provide a conceptualisation of emergent elements and an equation that evaluators can use to identify the emergent elements in their programme. Using our framework, evaluators can analyse programmes not as isolated units with planned processes and planned outcomes, but as unfolding, complex interactive systems that will exhibit emergent processes and emergent outcomes. Subsequent analysis of these emergent elements will inform the evaluator as they seek to optimise and improve the programme.

CONCLUSION

Our proposed systems engineering informed programme evaluation framework provides principles and tools for analysing the implications of planned and emergent elements, as well as their potential interactions. We acknowledge that our framework is preliminary and will require application and constant refinement. We suggest that our framework will also advance our understanding of the construct of 'emergence' in HPE research.

Understanding diagnostic error: looking beyond diagnostic accuracy

Whether a diagnosis is correct or incorrect is often used to determine diagnostic performance despite there being no valid measure of diagnostic accuracy. In this paper we draw on our experience of conducting research on diagnostic error and discuss some of the challenges that a focus on accuracy brings to this field of research. In particular, we discuss whether diagnostic accuracy can be captured and what diagnostic accuracy does and does not tell us about diagnostic judgement. We draw on these points to argue that a focus on diagnostic accuracy may limit progress in this field and suggest that research which tries to understand more about the factors that influence decision making during the diagnostic process may be more useful in helping to improve diagnostic performance.

The capability of static and dynamic features to distinguish competent from genuinely expert practitioners in pediatric diagnosis

OBJECTIVE

The authors describe the development of a new, more objective method of distinguishing experienced competent nonexpert from expert practitioners within pediatric intensive care.

BACKGROUND

Expert performance involves the acquisition and use of refined feature-event associations (cues) in the operational environment. Competent non-experts, although experienced, possess rudimentary cue associations in memory. Thus, they cannot respond as efficiently or as reliably as their expert counterparts, particularly when key diagnostic information is unavailable, such as that provided by dynamic cues.

METHOD

This study involved the application of four distinct tasks in which the use of relevant cues could be expected to increase both the accuracy and the efficiency of diagnostic performance. These tasks included both static and dynamic stimuli that were varied systematically. A total of 50 experienced pediatric intensive staff took part in the study.

RESULTS

The sample clustered into two levels across the tasks: Participants who performed at a consistently high level throughout the four tasks were labeled experts, and participants who performed at a lower level throughout the tasks were labeled competent nonexperts. The groups differed in their responses to the diagnostic scenarios presented in two of the tasks and their ability to maintain performance in the absence of dynamic features.

CONCLUSION

Experienced pediatricians can be decomposed into two groups on the basis of their capacity to acquire and use cues; these groups differ in their diagnostic accuracy and in their ability to maintain performance in the absence of dynamic features.

APPLICATION

The tasks may be used to identify practitioners who are failing to acquire expertise at a rate consistent with their experience, position, or training. This information may be used to guide targeted training efforts.

Extending hierarchical task analysis to identify cognitive demands and information design requirements

While hierarchical task analysis (HTA) is well established as a general task analysis method, there appears a need to make more explicit both the cognitive elements of a task and design requirements that arise from an analysis. One way of achieving this is to make use of extensions to the standard HTA. The aim of the current study is to evaluate the use of two such extensions--the sub-goal template (SGT) and the skills-rules-knowledge (SRK) framework--to analyse the cognitive activity that takes place during the planning and delivery of anaesthesia. In quantitative terms, the two methods were found to have relatively poor inter-rater reliability; however, qualitative evidence suggests that the two methods were nevertheless of value in generating insights about anaesthetists' information handling and cognitive performance. Implications for the use of an extended HTA to analyse work systems are discussed.

What makes diagnosis hard?

This essay uses a case study to explore some of the reasons why understanding failures associated with diagnosis seems to have lagged behind the attention and understanding directed at failures in other aspects of healthcare (e.g., wrong patient/procedure, medication misadministration, etc). The goal is not to pose an alternative to many of the current ideas about problems in diagnosis, but rather to enrich the discussions.

Knowledge representation and sharing using visual semantic modeling for diagnostic medical image databases

Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval have been studied and applied to assist physicians in diagnoses. Unfortunately, most of the studies lack the flexibility of sharing both explicit and tacit knowledge involved in the decision making process, while adapting to each individual's opinion. In this paper, we propose a knowledge repository and exchange framework for diagnostic image databases called "evolutionary system for semantic exchange of information in collaborative environments" (Essence). This framework uses semantic methods to describe visual abnormalities, and offers a solution for tacit knowledge elicitation and exchange in the medical domain. Also, our approach provides a computational and visual mechanism for associating synonymous semantics of visual abnormalities. We conducted several experiments to demonstrate the system's capability of matching synonym terms, and the benefit of using tacit knowledge in improving the meaningfulness of semantic queries.

Using simulated investigations for accident investigation studies

Regardless of the actual causes of particular accidents, it is the causes identified by the analyst that determine what responses are made, and how safety is managed in industry. Past authors have suggested that investigation might be biased, but studies were limited by the lack of similarity to real-world investigation tasks in which investigators must decide what information to acquire as well as analyse and interpret it. A technique was developed to use simulated investigations rather than attribution judgements about causation. Three studies are described, using simulated investigation to compare elicited knowledge and hypotheses among safety specialists, to compare investigations using job aids with unaided investigations, and to teach students about investigation bias and comprehensiveness. The method was well accepted by participants and shows flexibility for a range of uses, although it may have limitations.