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
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
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.
Rotating machinery, such as centrifugal pumps, turbines, bearings, and other critical systems, is the backbone of various industrial processes. Their failures can lead to significant maintenance costs and downtime. To ensure their continuous operation, we propose a fault diagnosis and monitoring framework that leverages the innovative use of acoustic sensors for early fault detection, especially in components less accessible for traditional vibration-based monitoring strategies. The main objective of the proposed project is to develop a fault diagnosis and monitoring framework for rotating machinery, including the fusion of acoustic sensors and physics-based models. By combining real-time monitoring data from acoustic sensors with an understanding of first principles, the framework will enable maintenance practitioners to identify and categorize different failure modes such as wear, fatigue, cavitation, reduced flow, bearing damage, impeller damage, misalignment, etc. In the initial phase, the focus will be on centrifugal pumps using the existing test set-up at the University of Twente. Sorama specializes in acoustic sensors to locate noise sources and will provide acoustic cameras to capture sound patterns related to pump deterioration during various operating conditions. These acoustic signals will then be correlated with the different failure modes and mechanisms that will be described by physics-based models, such as wear, fatigue, cavitation, corrosion, etc. Furthermore, a recently published data set by the Dynamics Based Maintenance research group that includes vibration analysis data and motor current analysis data of various fault scenarios, such as mentioned above, will be used as validation. The anticipated outcome of this project is a versatile framework for a physics-informed acoustic monitoring system. This system is designed to enhance early fault detection significantly, reducing maintenance costs and downtime across a broad spectrum of industrial applications, from centrifugal pumps to turbines, bearings, and beyond.
