Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344
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In this article a generic fault detection and diagnosis (FDD) method for demand controlled ventilation (DCV) systems is presented. By automated fault detection both indoor air quality (IAQ) and energy performance are strongly increased. This method is derived from a reference architecture based on a network with 3 generic types of faults (component, control and model faults) and 4 generic types of symptoms (balance, energy performance, operational state and additional symptoms). This 4S3F architecture, originally set up for energy performance diagnosis of thermal energy plants is applied on the control of IAQ by variable air volume (VAV) systems. The proposed method, using diagnosis Bayesian networks (DBNs), overcomes problems encountered in current FDD methods for VAV systems, problems which inhibits in practice their wide application. Unambiguous fault diagnosis stays difficult, most methods are very system specific, and finally, methods are implemented at a very late stage, while an implementation during the design of the HVAC system and its control is needed. The IAQ 4S3F method, which solves these problems, is demonstrated for a common VAV system with demand controlled ventilation in an office with the use of a whole year hourly historic Building Management System (BMS) data and showed it applicability successfully. Next to this, the influence of prior and conditional probabilities on the diagnosis is studied. Link to the formal publication via its DOI https://doi.org/10.1016/j.buildenv.2019.106632
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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The Heating Ventilation and Air Conditioning (HVAC) sector is responsible for a large part of the total worldwide energy consumption, a significant part of which is caused by incorrect operation of controls and maintenance. HVAC systems are becoming increasingly complex, especially due to multi-commodity energy sources, and as a result, the chance of failures in systems and controls will increase. Therefore, systems that diagnose energy performance are of paramount importance. However, despite much research on Fault Detection and Diagnosis (FDD) methods for HVAC systems, they are rarely applied. One major reason is that proposed methods are different from the approaches taken by HVAC designers who employ process and instrumentation diagrams (P&IDs). This led to the following main research question: Which FDD architecture is suitable for HVAC systems in general to support the set up and implementation of FDD methods, including energy performance diagnosis? First, an energy performance FDD architecture based on information embedded in P&IDs was elaborated. The new FDD method, called the 4S3F method, combines systems theory with data analysis. In the 4S3F method, the detection and diagnosis phases are separated. The symptoms and faults are classified into 4 types of symptoms (deviations from balance equations, operating states (OS) and energy performance (EP), and additional information) and 3 types of faults (component, control and model faults). Second, the 4S3F method has been tested in four case studies. In the first case study, the symptom detection part was tested using historical Building Management System (BMS) data for a whole year: the combined heat and power plant of the THUAS (The Hague University of Applied Sciences) building in Delft, including an aquifer thermal energy storage (ATES) system, a heat pump, a gas boiler and hot and cold water hydronic systems. This case study showed that balance, EP and OS symptoms can be extracted from the P&ID and the presence of symptoms detected. In the second case study, a proof of principle of the fault diagnosis part of the 4S3F method was successfully performed on the same HVAC system extracting possible component and control faults from the P&ID. A Bayesian Network diagnostic, which mimics the way of diagnosis by HVAC engineers, was applied to identify the probability of all possible faults by interpreting the symptoms. The diagnostic Bayesian network (DBN) was set up in accordance with the P&ID, i.e., with the same structure. Energy savings from fault corrections were estimated to be up to 25% of the primary energy consumption, while the HVAC system was initially considered to have an excellent performance. In the third case study, a demand-driven ventilation system (DCV) was analysed. The analysis showed that the 4S3F method works also to identify faults on an air ventilation system.
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The purpose of this article is the presentation of a multidimensional guideline for the diagnosis of anxiety and anxiety-related behavior problems in people with intellectual disability (ID), with a substantial role for the nurse in this diagnostic process. DESIGN AND METHODS: The guideline is illustrated by a case report of a woman with ID with severe problems. FINDINGS: It appears that a multidimensional diagnostic approach involving multidisciplinary team efforts can result in a more accurate diagnosis and improved subsequent treatment. PRACTICE IMPLICATIONS: Nurses should be engaged in the diagnostic process because of their ability to make direct observations and to actively participate in carrying out all parts of the guideline.
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Objectives: The aim of this study was to determine how diagnosing and coding of malnutrition in an internal medicine ward setting influences potential hospital reimbursement. Methods: Patients admitted to the internal medicine ward of Centro Hospitalar do Médio Ave between April 24 and May 22, 2018 were screened by Nutritional Risk Screening 2002, and patients classified as at “risk for malnutrition” were assessed by the Patient-Generated Subjective Global Assessment (PG-SGA). For each patient, medical coders simulated coding, taking into account the malnutrition diagnosis by PG-SGA, and compared it with the real coding as retrieved from the medical records. For the coding, the Diagnosis-Related Group and Severity of Illness were determined, allowing the calculation of hospitalization cost (HC) according to Portuguese Ministerial Directive number 207/2017. The increase of HC in this subsample was extrapolated to the number of patients admitted during 2018, to obtain the estimated unreported annual HC. Results: Of the 71% (92/129) participants having malnutrition risk according to Nutritional Risk Screening 2002, 86% were malnourished. Including malnutrition diagnosis in the coding of malnourished patients increased the level of Severity of Illness in 39% of cases and increased HC for this subsample, resulting in €52 000. Extrapolating for the annual HC, total HC reached €1.3 million. Conclusions: Identifying malnourished patients and including this highly prevalent diagnosis in medical records allows malnutrition coding and consequent increase of HC. This can improve the potential hospital reimbursement, which could contribute to the quality of patient care and economic sustainability of hospitals.
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ObjectiveAlthough regular physical activity is an effective secondary prevention strategy for patients with a chronic disease, it is unclear whether patients change their daily physical activity after being diagnosed. Therefore, the aims of this study were to (1) describe changes in levels of physical activity in middle-aged women before and after diagnosis with a chronic disease (heart disease, diabetes, asthma, breast cancer, arthritis, depression); and to (2) examine whether diagnosis with a chronic disease affects levels of physical activity in these women.MethodsData from 5 surveys (1998–2010) of the Australian Longitudinal Study on Women's Health (ALSWH) were used. Participants (N = 4840, born 1946–1951) completed surveys every three years, with questions about diseases and leisure time physical activity. The main outcome measure was physical activity, categorized as: nil/sedentary, low active, moderately active, highly active.ResultsAt each survey approximately half the middle-aged women did not meet the recommended level of physical activity. Between consecutive surveys, 41%–46% of the women did not change, 24%–30% decreased, and 24%–31% increased their physical activity level. These proportions of change were similar directly after diagnosis with a chronic disease, and in the years before or after diagnosis. Generalized estimating equations showed that there was no statistically significant effect of diagnosis with a chronic disease on levels of physical activity in women.ConclusionDespite the importance of physical activity for the management of chronic diseases, most women did not increase their physical activity after diagnosis. This illustrates a need for tailored interventions to enhance physical activity in newly diagnosed patients.
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Rationale: Diagnosis of sarcopenia in older adults is essential for early treatment in clinical practice. Bio-electrical impedance analysis (BIA) may be a valid means to assess appendicular lean mass (ALM) in older adults, but limited evidence is available. Therefore, we aim to evaluate the validity of BIA to assess ALM in older adults.Methods: In 215 community dwelling older adults (age ≥ 55 years), ALM was measured by BIA (Tanita MC-780; 8-points) and compared with dual-energy X-ray absorptiometry (DXA, Hologic Discovery A) as reference. Validity for assessing absolute values of ALM was evaluated by: 1) bias (mean difference), 2) percentage of accurate predictions (within 5% of DXA values), 3) individual error (root mean squared error (RMSE), mean absolute deviation), 4) limits of agreement (Bland-Altman analysis). For diagnosis of low ALM, the lowest quintile of ALM by DXA was used (below 21.4 kg for males and 15.4 for females). Sensitivity and specificity of detecting low ALM by BIA were assessed.Results: Mean age of the subjects was 71.9 ± 6.4, with a BMI of 25.8 ± 4.2 kg/m2, and 70% were females. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 0.2 kg. The percentage accurate predictions was 54% with RMSE 1.6 kg and limits of agreements −3.0 to +1.8 kg. Sensitivity was 79%, indicating that 79% of subjects with low ALM according to DXA also had low ALM with the BIA. Specificity was 90%, indicating that 90% of subjects with ‘no low’ ALM according to DXA also had ‘no low’ ALM with the BIA.Conclusions: This comparison showed a poor validity of BIA to assess absolute values of ALM, but a reasonable sensitivity and specificity to diagnose a low level of ALM in community-dwelling older adults in clinical practice.Disclosure of interest: None declared.
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