Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Systematic Literature Review

Abstract

Predictive maintenance plays an important role in managing commercial buildings. This article provides a systematic review of the literature on predictive maintenance applications of chilled water systems that are in line with Industry 4.0/Quality 4.0. The review is based on answering two research questions about understanding the mechanism of identifying the system’s faults during its operation and exploring the methods that were used to predict these faults. The research gaps are explained in this article and are related to three parts, which are faults description and handling, data collection and frequency, and the coverage of the proposed maintenance programs. This article suggests performing a mixed method study to try to fill in the aforementioned gaps.

Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment: An Overview of the Special Issue

“…Almobarek et al [9] performed a systematic review of the literature on predictive maintenance applications of chilled water systems (CWS) and focused on two aspects: (1) the identification of operational faults and (2) the methods to better predict them. The authors covered chillers, cooling towers, circulating pumps, and terminal units and pinpointed the lack of studies tackling the entire CWS (rather than focusing on specific components); they also suggested that more attention should be given to cooling towers and pumps.…”

“…Almobarek et al [10] conducted an industry survey to complement the aforementioned systematic review outputs [9]. This survey targets the identification and frequencies of more operational faults and fault solutions for chilled water systems.…”

“…Finally, the closing article by Almobarek et al [11] addressed the need for maintenance programs raised in the aforementioned system review [9] to implement predictive maintenance in existing buildings. They proposed a methodological framework to describe the requirements (drawings, measuring devices, and operational data), develop machine learning algorithms, and conduct quality control (ensure proper operation and correct faults).…”

A Critical Perspective on Current Research Trends in Building Operation: Pressing Challenges and Promising Opportunities

“…Previous initiatives have tackled similar topics, but the focus was rather on specific applications: metadata schemas [18], data analytics [19], FDD [20], model-based predictive controls (MPC) [9,[21][22][23], reinforcement learning [24], occupant-centric controls (OCC) [25,26], predictive maintenance [27], peak load management [28], building energy flexibility [29], strategies for building energy management systems (including MPC, demand side management, optimization, and FDD) [8], and data-driven building operations (with a focus on metadata, FDD, OCC, key performance indicators, virtual energy meters, and load disaggregation) [30]. Some other work emphasized the perspectives, challenges, and opportunities but also concentrated on specific aspects: industry engagement [31], data requirements for MPC [32], reinforcement learning [33], and OCC [34].…”

Enhanced Dual Convolutional Neural Network Model Using Explainable Artificial Intelligence of Fault Prioritization for Industrial 4.0

“…However, the great majority of deep learning and machine learning systems for identifying control automation do not provide more detailed information about the patterns and their turning points (even when these patterns are seen on control charts). This information is required to undertake a realistic analysis of assignable causes, which in turn expedites the execution of suitable remedial measures [ 8 , 9 ]. To help quality control staff members locate the origins of deviations and take the appropriate preventative or corrective measures, this study aims to suggest the development of a defect detection system that utilizes hybrid convolutional neural networks.…”