SW
Shalika Walker
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1
Fault detection and diagnosis for heat recovery ventilation using 4S3F method
Impact of diverse sensor configurations
Fault detection and diagnosis (FDD) are crucial to improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems, reducing energy waste, and maintaining indoor comfort. Diagnostic Bayesian Networks (DBNs) present a compelling approach, offering robustness to uncertainty, adaptability to different sensor configurations, and interpretable inference. Existing FDD studies for air handling units (AHUs), however, are often limited to simulation or laboratory settings, seldom consider AHUs with heat recovery wheel (HRW) in operation, and rarely analyze how diagnostic performance changes under diverse sensor configurations. This study defined three practical sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practical survey, developed a corresponding DBN framework, and evaluated its performance on seventeen common faults using real-world data from an AHU in a Dutch office building. Existing FDD studies are often limited to simulation or specific Air Handling Unit (AHU) types with fixed sensor configurations, rarely investigating AHUs with heat recovery wheels, which are common in Europe. This study addresses these gaps by first defining three sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practitioner survey. A DBN-based FDD model was then developed for these configurations using historical data, expert knowledge and subsequently evaluated for its ability to diagnose seventeen common faults in an operational AHU with heat recovery wheel.The DBN correctly diagnosed fifteen, nine, and four faults for these configurations, respectively. The results show that increasing sensor availability improves overall diagnostic performance. However, certain cases demonstrate that additional measurements can also introduce conflicting evidence and reduce diagnostic accuracy. The study suggests that sensor selection must be combined with effective DBN modeling strategies to achieve robust diagnosis. Taken together, the analysis of key sensors and DBN modeling practices provides practical guidance for designing and implementing DBN-based FDD in common European AHU systems under diverse sensor configurations.The results indicate that increasing sensor quantity alone does not improve FDD performance; strategic sensor selection, placement, and effective data processing are also crucial.
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Fault detection and diagnosis (FDD) are crucial to improving the efficiency of heating, ventilation, and air conditioning (HVAC) systems, reducing energy waste, and maintaining indoor comfort. Diagnostic Bayesian Networks (DBNs) present a compelling approach, offering robustness to uncertainty, adaptability to different sensor configurations, and interpretable inference. Existing FDD studies for air handling units (AHUs), however, are often limited to simulation or laboratory settings, seldom consider AHUs with heat recovery wheel (HRW) in operation, and rarely analyze how diagnostic performance changes under diverse sensor configurations. This study defined three practical sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practical survey, developed a corresponding DBN framework, and evaluated its performance on seventeen common faults using real-world data from an AHU in a Dutch office building. Existing FDD studies are often limited to simulation or specific Air Handling Unit (AHU) types with fixed sensor configurations, rarely investigating AHUs with heat recovery wheels, which are common in Europe. This study addresses these gaps by first defining three sensor configurations (Sensor-Rich, Standard, and Limited) based on international guidelines and a practitioner survey. A DBN-based FDD model was then developed for these configurations using historical data, expert knowledge and subsequently evaluated for its ability to diagnose seventeen common faults in an operational AHU with heat recovery wheel.The DBN correctly diagnosed fifteen, nine, and four faults for these configurations, respectively. The results show that increasing sensor availability improves overall diagnostic performance. However, certain cases demonstrate that additional measurements can also introduce conflicting evidence and reduce diagnostic accuracy. The study suggests that sensor selection must be combined with effective DBN modeling strategies to achieve robust diagnosis. Taken together, the analysis of key sensors and DBN modeling practices provides practical guidance for designing and implementing DBN-based FDD in common European AHU systems under diverse sensor configurations.The results indicate that increasing sensor quantity alone does not improve FDD performance; strategic sensor selection, placement, and effective data processing are also crucial.
Digitalization of HVAC piping and instrumentation diagrams (P&IDs) is essential for advancing the intelligent transformation of building systems and the building services industry. This work explores Large Language Models (LLMs) for zero-shot P&ID digitization, focusing on symbol detection. Three LLM-assisted approaches are evaluated. The results show that directly applying LLMs to P&ID digitization is highly challenging. By segmenting P&IDs into local crops and pairing them with the full diagram annotated with bounding boxes to provide global context, the LLM achieves improved symbol recognition. The findings highlight both the promise of LLMs and the need for further refinement to enable reliable HVAC P&ID digitization.
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Digitalization of HVAC piping and instrumentation diagrams (P&IDs) is essential for advancing the intelligent transformation of building systems and the building services industry. This work explores Large Language Models (LLMs) for zero-shot P&ID digitization, focusing on symbol detection. Three LLM-assisted approaches are evaluated. The results show that directly applying LLMs to P&ID digitization is highly challenging. By segmenting P&IDs into local crops and pairing them with the full diagram annotated with bounding boxes to provide global context, the LLM achieves improved symbol recognition. The findings highlight both the promise of LLMs and the need for further refinement to enable reliable HVAC P&ID digitization.
Heat pump and thermal energy storage (HPTES) systems, which are widely utilized in modern buildings for providing domestic hot water, contribute to a large share of household electricity consumption. With the increasing integration of renewable energy sources (RES) into modern power grids, demand-side management (DSM) becomes crucial for balancing power generation and consumption by adjusting end users' power consumption. This paper explores an energy flexible Model Predictive Control (MPC) design for a class of HPTES systems to facilitate demand-side management. The proposed DSM strategy comprises two key components: i) flexibility assessment, and ii) flexibility exploitation. Firstly, for flexibility assessment, a tailored MPC formulation, supplemented by a set of auxiliary linear constraints, is developed to quantitatively assess the flexibility potential inherent in HPTES systems. Subsequently, in flexibility exploitation, the energy flexibility is effectively harnessed in response to feasible demand response (DR) requests, which can be formulated as a standard mixed-integer MPC problem. Numerical experiments, based on a real-world HPTES installation, are conducted to demonstrate the efficacy of the proposed design.
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Heat pump and thermal energy storage (HPTES) systems, which are widely utilized in modern buildings for providing domestic hot water, contribute to a large share of household electricity consumption. With the increasing integration of renewable energy sources (RES) into modern power grids, demand-side management (DSM) becomes crucial for balancing power generation and consumption by adjusting end users' power consumption. This paper explores an energy flexible Model Predictive Control (MPC) design for a class of HPTES systems to facilitate demand-side management. The proposed DSM strategy comprises two key components: i) flexibility assessment, and ii) flexibility exploitation. Firstly, for flexibility assessment, a tailored MPC formulation, supplemented by a set of auxiliary linear constraints, is developed to quantitatively assess the flexibility potential inherent in HPTES systems. Subsequently, in flexibility exploitation, the energy flexibility is effectively harnessed in response to feasible demand response (DR) requests, which can be formulated as a standard mixed-integer MPC problem. Numerical experiments, based on a real-world HPTES installation, are conducted to demonstrate the efficacy of the proposed design.