Fault detection and diagnostics analysis of air conditioners using virtual sensors

Kim, Woohyun; Lee, Je-Hyeon

doi:10.1016/j.applthermaleng.2021.116848

Cited by 31 publications

(4 citation statements)

References 28 publications

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“…A fault detection system is a computational process to determine abnormal events, failures, and malfunctions, which can be unsatisfied to people and damage properties [17]. A fundamental principle of fault detection requires knowledge to reason the possible abnormal events.…”

Section: Fault Detectionmentioning

confidence: 99%

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

Sahoh

Kliangkhlao

Kittiphattanabawon

2022

Sensors

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Controlling thermal comfort in the indoor environment demands research because it is fundamental to indicating occupants’ health, wellbeing, and performance in working productivity. A suitable thermal comfort must monitor and balance complex factors from heating, ventilation, air-conditioning systems (HVAC Systems) and outdoor and indoor environments based on advanced technology. It needs engineers and technicians to observe relevant factors on a physical site and to detect problems using their experience to fix them early and prevent them from worsening. However, it is a labor-intensive and time-consuming task, while experts are short on diagnosing and producing proactive plans and actions. This research addresses the limitations by proposing a new Internet of Things (IoT)-driven fault detection system for indoor thermal comfort. We focus on the well-known problem caused by an HVAC system that cannot transfer heat from the indoor to outdoor and needs engineers to diagnose such concerns. The IoT device is developed to observe perceptual information from the physical site as a system input. The prior knowledge from existing research and experts is encoded to help systems detect problems in the manner of human-like intelligence. Three standard categories of machine learning (ML) based on geometry, probability, and logical expression are applied to the system for learning HVAC system problems. The results report that the MLs could improve overall performance based on prior knowledge around 10% compared to perceptual information. Well-designed IoT devices with prior knowledge reduced false positives and false negatives in the predictive process that aids the system to reach satisfactory performance.

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Section: Fault Detectionmentioning

confidence: 99%

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

Sahoh

Kliangkhlao

Kittiphattanabawon

2022

Sensors

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“…This noise has the potential to introduce fluctuations in the feedback signals utilized by the controller, consequently resulting in suboptimal or erratic control performance [9]. To mitigate the effects of measurement noise in monitoring systems, several techniques have been used, such as filtering algorithms [10], signal processing methods [11], and software sensors [12]. Software sensors, commonly referred to as observers (or virtual sensors), have emerged as a viable substitute for addressing the issue of measurement noise and the unavailability of hardware sensors due to system configuration or cost.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Multi-Order High-Gain Observer Design to Estimate Temperature in a Heat Exchange Process

Borja-Jaimes,

Adam-Medina,

García-Morales

et al. 2023

Axioms

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In the present manuscript, we design a fractional multi-order high-gain observer to estimate temperature in a double pipe heat exchange process. For comparison purposes and since we want to prove that when using our novel technique, the estimation is more robust than the classical approach, we design a non-fractional high-gain observer, and then we compare the performance of both observers. We consider three scenarios: The first one considers the estimation of the system states by measuring only one output with no noise added on it and under ideal conditions. Second, we add noise to the measured output and then reconstruct the system states, and, third, in addition to the noise, we increase the gain parameter in both observers (non-fractional and fractional) due to the fact that we want to prove that the robustness changes in this parameter. The results showed that, using our approach, the estimated states can be recovered under noise circumstances in the measured output and under parameter change in the observer, contrary to using classical (non-fractional) observers where the states cannot be recovered. In all our tests, we used the normalized root-mean-square, integral square error, and integral absolute error indices, resulting in a better performance for our approach than that obtained using the classical approach. We concluded that our fractional multi-order high-gain observer is more robust to input noise than the classical high-gain observer.

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“…The results are analyzed and presented with a notification method for air conditioning maintenance that will allow the air conditioner to operate at full efficiency and with sustainable energy savings. The results are comparing the measurement of compressor electric current detection [15]- [17] while operating in abnormal conditions.…”

Section: Introductionmentioning

confidence: 99%

Experimental study of compressor electric current detection for a split-type air conditioner affects energy savings

Saengchandr

Sukontanakarn

Waiyagan

2023

IJECE

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The paper presents an experimental study that aims to measure the compressor electric current of a split-type air conditioner for analyzing the various abnormal condition of the R-32 refrigerant pressure, especially for detecting compressor electric current while occurring dirt in the evaporator coil and condenser coil. The method was to install sensor devices to measure the temperature and humidity of inlet air and outlet air, and the velocity of the air outlet of the evaporator unit. In condenser unit was to measure the electric current compressor and electric power input. All data from sensors send to the Arduino board and using Parallax Data Acquisition (PLX-DAQ) Excel Macro for the record. The results show physical behavior and the changing of compressor electric current according to the abnormal condition of the refrigerant system, blocking of condenser and evaporator coil.

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Fault detection and diagnostics analysis of air conditioners using virtual sensors

Cited by 31 publications

References 28 publications

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

Design and Development of Internet of Things-Driven Fault Detection of Indoor Thermal Comfort: HVAC System Problems Case Study

A Novel Fractional Multi-Order High-Gain Observer Design to Estimate Temperature in a Heat Exchange Process

Experimental study of compressor electric current detection for a split-type air conditioner affects energy savings

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