Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework

Abstract

Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university.

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

“…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).…”

“…Ongoing efforts to improve models for indoor air temperature [3] and building energy loads [4] are and will remain essential to strengthening confidence in data-driven solutions. These solutions could be applied at various scales [1], from setpoint tracking [5] to the adjustment of building indoor conditions [6] and the optimization of heating and cooling system operations [6][7][8]10,11], with different levels of complexity, such as model-based controls [6,8], predictive control [7], and predictive maintenance [10,11].…”

Remote Fault Diagnosis System Based on the Pattern Recognition and Cloud

“…Many of the popular remote diagnosis and decision-making troubleshooter has been applied already for the smart troubleshooter to diagnose and makes the corrected decision to keep the monitored components performed in a healthy condition. For example, Almobarek et al 2023 built a framework based on the industry survey study outcomes and divided it into three parts: setup, machine learning, and quality control. Ali et al (2020) propose a cloud computing platform consisting of three layers of techniques forming a Cyber-Physical system.…”

Towards AI-Based Condition Monitoring and Predictive Maintenance for Water Smart Pipes: The SANDMAN Approach

“…The evaluation of the proposed solution was achieved on a real-world water transmission network. In Almobarek et al (2023), a methodological framework for a predictive maintenance program for commercial buildings is proposed. The solution is developed for chilled water systems (CWS) and includes three parts, the setup, machine learning, and quality control.…”