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.
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
INTRODUCTION: Mechanical Insufflation-Exsufflation (MI-E) is used as an airway clearance intervention in primary care (home ventilation), long-term care (prolonged rehabilitation after intensive care, neuromuscular diseases, and spinal cord injury), and increasingly in acute care in intensive care units (ICU).AIM: We sought to develop in-depth understanding of factors influencing decision-making processes of health care professionals regarding initiation, escalation, de-escalation, and discontinuation of MI-E for invasively ventilated patients including perceived barriers and facilitators to use.METHODS: We conducted focus groups (3 in the Netherlands; 1 with participants from four European countries) with clinicians representing the ICU interprofessional team and with variable experience of MI-E. The semi-structured interview guide was informed by the Theoretical Domains Framework (TDF). Two researchers independently coded data for directed content analysis using codes developed from the TDF.RESULTS: A purposive sample of 35 health care professionals participated. Experience varied from infrequent to several years of frequent MI-E use in different patient populations. We identified four main themes: (1) knowledge; (2) beliefs; (3) clinical decision-making; and (4) future adoption.CONCLUSION: Interprofessional knowledge and expertise of MI-E in invasively ventilated patients is limited due to minimal available evidence and adoption. Participants believed MI-E a potentially useful intervention for airway clearance and inclusion in weaning protocols when more evidence is available.RELEVANCE TO CLINICAL PRACTICE: This focus group study provides an overview of current practice, knowledge and expertise, and barriers and facilitators to using MI-E in mechanically ventilated patients. From these data, it is evident there is a need to develop further clinical expertise and evidence of efficacy to further understand the role of MI-E as an airway clearance technique for ventilated patients.
