Review of predictive maintenance algorithms applied to HVAC systems

Es-sakali, Niima; Cherkaoui, Moha; Mghazli, Mohamed Oualid; Naimi, Zakaria

doi:10.1016/j.egyr.2022.07.130

Cited by 50 publications

(8 citation statements)

References 36 publications

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“…PdM arose due to the shortcomings of reactive and preventive maintenance [5]. The PdM strategy is based on monitoring the equipment with the help of measurement systems capable of evaluating its condition during operation, permanently or in a certain period.…”

Section: Introductionmentioning

confidence: 99%

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A Specialty Literature Review of the Predictive Maintenance Systems

ANDRIOAIA,

GAITAN

2024

JESR

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Within the industrial standard 4.0, predictive maintenance has an essential role in production activities, by increasing equipment uptime and decreasing maintenance costs. Predictive maintenance monitors assets through sensors for optimal planning of maintenance operations to keep assets functional. In this paper, the authors perform an analysis of predictive maintenance system, proposed in the specialized literature, highlighting their component elements.

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Section: Introductionmentioning

confidence: 99%

“…PdM systems whose decision-making system uses machine learning allow detecting the defect long before the functional failure occurs [5,7]. According to the report prepared by the company IoT Analytics, the PdM systems market will grow from 4.77B $ to 23.53 B $ during the period 2019 -2024 [8].…”

Section: Introductionmentioning

confidence: 99%

A Specialty Literature Review of the Predictive Maintenance Systems

ANDRIOAIA,

GAITAN

2024

JESR

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“…The building sector is considered among the most sectors that consume energy [1]- [5]. The biggest part of the energy used in buildings is consumed by heating, ventilation, and air conditioning systems (HVAC) [6]- [8]. Although these systems are energy-consuming, they are essential in providing thermal comfort in buildings [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Building energy efficiency improvement using multi-objective optimization for heating and cooling VRF thermostat setpoints

et al. 2023

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The variable refrigerant flow system is one of the best heating, ventilation, and air conditioning systems (HVAC) thanks to its ability to provide thermal comfort inside buildings. But, at the same time, these systems are considered one of the most energy-consuming systems in the building sector. Thus, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. Although many researchers have studied the optimization of the building energy performance considering heating or cooling needs, using air handling units, radiant floor heating, and direct expansion valves, few studies have considered the use of multi-objective optimization using only the thermostat setpoints of VRF systems for both cooling and heating needs. Thus, the main aim of this study is to conduct a sensitivity analysis and a multi-objective optimization strategy for a residential building containing a variable refrigerant flow system, to evaluate the effect of the building performance on energy consumption and improve the building energy efficiency. The numerical model was based on the EnergyPlus, jEPlus, and jEPlus+EA simulation engines. The approach used in this paper has allowed us to reach significant quantitative energy saving by varying the cooling and heating setpoints and scheduling scenarios. It should be stressed that this approach could be applied to several HVAC systems to reduce energy-building consumption.

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“…Specifically, the energy consumption of heating, ventilation, and air-conditioning (HVAC) systems in buildings constitutes 38% of this expenditure [2]. Moreover, the upkeep of HVAC systems makes up over 65% of the annual costs associated with building facilities management [3]. Proper maintenance of HVAC systems can curtail these expenses, enhance HVAC system availability, and thereby elevate human comfort in enclosed spaces.…”

Section: Introductionmentioning

confidence: 99%

“…IoT-enabled HVAC systems can greatly benefit from data-driven predictive maintenance. There are two prominent data-driven strategies for the predictive maintenance of HVAC systems: fault detection and diagnostics (FDD) and health prognostics (HP) [3].…”

Section: Introductionmentioning

confidence: 99%

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

Tian,

Gomez-Rosero,

Capretz

2023

Energies

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Buildings’ heating, ventilation, and air-conditioning (HVAC) systems account for significant global energy use. Proper maintenance can minimize their environmental footprint and enhance the quality of the indoor environment. The adoption of Internet of Things (IoT) sensors integrated into HVAC systems has paved the way for predictive maintenance (PdM) grounded in real-time operational metrics. However, HVAC systems without such sensors cannot leverage the advantages of current data-driven PdM techniques. This work introduces a novel data-driven framework, the health prognostics classification with autoencoders (HPC-AE), designed specifically for PdM. It utilizes solely HVAC power consumption and outside temperature readings for its operations, both of which are readily obtainable. The primary objective of the HPC-AE framework is to facilitate PdM through a health prognostic approach. The HPC-AE framework utilizes an autoencoder for feature enrichment and then applies an artificial neural network to classify the daily health condition of an HVAC system. A multi-objective evaluation metric is employed to ensure optimal performance of the autoencoder within this framework. This metric evaluates the autoencoder’s proficiency in reducing reconstruction discrepancies in standard data conditions and its capability to differentiate between standard and degraded data scenarios. The HPC-AE framework is validated in two HVAC fault scenarios, including a clogged air filter and air duct leakage. The experimental results show that compared to methods used in similar studies, HPC-AE exhibits a 5.7% and 2.1% increase in the F1 score for the clogged air filter and duct leakage scenarios.

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Review of predictive maintenance algorithms applied to HVAC systems

Cited by 50 publications

References 36 publications

A Specialty Literature Review of the Predictive Maintenance Systems

A Specialty Literature Review of the Predictive Maintenance Systems

Building energy efficiency improvement using multi-objective optimization for heating and cooling VRF thermostat setpoints

Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems

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