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.

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.

Most daily tasks require exerting static grip strength which can be challenging for the elderly as their strength diminishes with age. Moreover, normative static grip strength data are important in ergonomics and clinical settings. The goal of this study is to present the gender, age-specific, hand-specific, and body-mass-index-specific handgrip strength reference of Saudi males and females in order to describe the population’s occupational demand and to compare them with the international standards. The secondary objective is to investigate the effects of gender, age group, hand area, and body mass index on the grip strength. A sample of 297 (146 male and 151 female) volunteers aged between 18 and 70 with different occupations participated in the study. Grip strength data were collected using a Jamar dynamometer with standard test position, protocol, and instructions. The mean maximum voluntary grip strength values for males were 38.71 kg and 22.01 kg, respectively. There was a curvilinear relationship of grip strength to age; significant differences between genders, hand area, and some age groups; and a correlation to hand dimensions depending on the gender.

Predictive Maintenance 4.0 (PdM 4.0) showed a highly positive impact on chilled water system (CWS) maintenance. This research followed the recommendations of a systematic literature review (SLR), which was performed on PdM 4.0 applications for CWS at commercial buildings. Per the SLR, and to start making an excellent PdM 4.0 program, the faults and their frequencies must be identified. Therefore, this research constructed an industry survey, which went through a pilot study, and then shared it with 761 maintenance officers in different commercial buildings. The first goal of this survey is to verify the faults reported by SLR, explore more faults, and suggest a managerial solution for each fault. The second goal is to determine the minimum and maximum frequencies of faults occurrence, while the third goal is to verify selected operational parameters, in which their data can be used in smart buildings applications. A total of 304 responses are considered in this study, which identified additional faults and provided faults solutions for all CWS components. Based on the survey outcomes, justifiable frequencies are proposed, which can be used in creating the dataset of any machine learning model, and then to control the CWS performance.

Predictive maintenance shows significant impact in managing and controlling the utility systems of commercial buildings. Here in this paper, the predictive maintenance that is in line with Industry 4.0/ Quality 4.0 is applied. The chilled water system is highlighted in this research and the main goal is to reach out the most occurred faults in one of the chilled water system, which is cooling tower. An engineering management framework was used through three parts, set up, machine learning, and quality control. Having said machine learning, decision tree algorithm is used in this research and prediction model showed high prediction accuracy. During an empirical period, the most occurred faults were malfunctioning blowdown system, over current, and low water basin level. Keywords Faults, Predictive Maintenance 4.0, Commercial Buildings, Chilled Water System, and Cooling Towers

Chilled water system can be considered as one of the critical systems in commercial buildings. One of its major components is addressed here, which is chiller system. The related faults of the said system normally make negative impact on its performance and accordingly reducing its life cycle. Addressing these faults by finding them while operating the buildings is recommended to make an efficient maintenance program. So, in this research, a quality engineering mechanism to address or to find these faults is proposed. Two tailed t-test is used as part of the proposed methodology. A case study of a building that contains five chillers is presented. During a particular period of time, the performance of one of those chillers has been found substantial for inspection. The proposed method has shown a significant outcome for the users at the said building from engineering management point of view.