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With the shift of manufacture to mainland China, the utility and building services industries in Hong Kong have become dominant. To ensure adherence to proper routine operations and the provision of quality services, the equipment of these sectors must be maintained in good condition. The results of a recently conducted survey indicate that equipment failure‐driven and time‐based maintenance are most commonly used in Hong Kong. Only a few companies use condition‐based preventive maintenance. This paper presents an overview of maintenance practice in Hong Kong, and introduces the novel concept of intelligent predictive maintenance. In this maintenance system, the seriousness of the damage that is caused by faults in equipment can be determined, and the remnant life of the defective equipment can be predicted. Moreover, the system can automatically schedule maintenance activities in an efficient manner. With such abilities in equipment prognosis and automatic maintenance scheduling, the “fire‐fighting” situations that often occur in failure‐driven and time‐based maintenance can be avoided. Hence, any waste of resources and loss of production that are due to the mismanagement of maintenance can be substantially reduced.

The purpose of this paper is to properly plan the preventive maintenance schedule for multiple elevators and optimize the number of maintenance workers.The total amount of the maintenance cost consisting of the labor cost, the part cost, and the quality cost (the loss evaluated in terms of cost, to be incurred when an elevator breaks down) is to be minimized.The method is presented of setting up the optimal preventive maintenance schedule on a long‐term basis by rescheduling the contents of schedule dynamically and flexibly in accordance with the ever‐changing maintenance conditions, taking the possibility of the future occurrence of failure into consideration. From numerical experiments, the validity of the proposal procedure for planning the preventive maintenance schedule and the effectiveness of considering the possibility of the future occurrence of failure in planning the schedule are shown, and the optimal number of maintenance workers can be decided.

The purpose of this paper is to present the findings of a recent study conducted with the objective of addressing the problem of failure of baggage carts in the high-speed baggage tunnel at Heathrow Terminal 5 by the development of an innovative condition-based maintenance (CBM) system designed to meet the requirements of 21st century airport systems and Industry 4.0.

An empirical experimental approach to this action research was taken to install a vibration condition monitoring pilot test in the north tunnel at Terminal 5. Vibration data were collected over a 6-month period and analysed to find the threshold of good quality tyres and those with worn bearings that needed replacement. The results were compared with existing measures to demonstrate that vibration monitoring could be used as a predictive model for CBM.

The findings demonstrated a clear trend of increasing vibration velocity with age and use of the baggage cart wheels caused by wheel mass unbalanced inertia that was transmitted to the tracks as vibration. As a result, preventative maintenance is essential to ensure the smooth running of airport baggage. This research demonstrates that a healthy wheel produces vibration of under 60 mm/s whereas a damaged wheel measures up to 100 mm/s peak to peak velocity and this can be used in real-time condition monitoring to prevent baggage cart failure. It can also run as an autonomous system linked to AI and Industry 4.0 airport logic.

Whilst vibration monitoring has been used to measure movement in static structures such as bridges and used in rotating machinery such as railway wheels (Tondon and Choudhury, 1999); this is unique as it is the first time it has been applied on a stationary structure (tracks) carrying high-speed rotating machinery (baggage cart wheels). This technique has been patented and proven in the pilot study and is in the process of being rolled out to all Heathrow terminal connection tunnels. It has implications for all other airports worldwide and, with new economic sensors, to other applications that rely on moving conveyor belts.

Degradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.

A progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.

A comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.

The proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

The purpose of this paper is to demonstrate how Internet of Things (IoT) technology can enable highly distributed elevator equipment servicing by using remote-monitoring technology to facilitate a shift from traditional corrective maintenance (CM) and time-based maintenance (TBM) to more predictive, condition-based maintenance (CBM) in order to achieve various benefits.

Literature review indicates that CBM has advantages over conventional CM and TBM from a theoretical perspective, but it depends on continuous monitoring enhancement via advanced IoT technology. An in-depth case study was carried out to provide practical evidence that IoT enables elevator firms to achieve CBM.

From a theoretical perspective, the CBM of elevators makes business sense. The challenges lie in data collection, data analysis and decision making in real-world business contexts. The main findings of this study suggest that CBM can be commercialized via IoT in the case of elevators and would improve the safety and reliability of equipment. It would, thus, make sense from technological, process and economic perspectives.

Our longitudinal real-world case study demonstrates a practical way of making the CBM of elevators widespread. Integrating IoT and other advanced technology would improve the safety and reliability of elevator equipment, prolong its useful life, minimize inconvenience and business interruptions due to equipment downtime and reduce or eliminate major repairs, thus greatly reducing maintenance costs.

The main contribution of this paper lies in the empirical demonstration of the benefits and challenges of CBM via IoT relative to conventional CM and TBM in the case of elevators. The authors believe that this study is timely and will be valuable to firms working on similar research or commercialization strategies.

The great need for an optimum preventive maintenance strategy coupled with the fast‐developing condition‐monitoring techniques has given rise to the invention of relevant condition predictor (RCP)‐based maintenance approach. The main purpose of this approach is to prevent the failures due to gradual deterioration of mechanical items in order to improve system reliability and availability. This is done by monitoring relevant condition predictors of constituent maintenance significant items of the system, taking into account the availability and cost‐effectiveness of the monitoring techniques. A comprehensive review of all constituent items is carried out and a systematic approach is used to decide an optimum maintenance policy for each corresponding group of items. An optimum time to the examination of relevant condition predictors is derived mathematically with required reliability as the optimisation criterion in order to implement the RCP‐based maintenance activities.

The purpose of this paper is to propose the best method of maintenance for electrical equipment of a building and especially elevators in order to achieve various benefits.

Literature review shows that there are various methods such as corrective maintenance, condition-based maintenance, time-based maintenance and reliability-centered maintenance (RCM) for the maintenance of electrical equipment of a building. Selecting the most appropriate maintenance method, especially in large buildings such as commercial ones, is a multiple-criteria decision-making problem. To solve this problem, the fuzzy analytic hierarchy process is used in the present study. It considers the uncertainties of the experts' judgments to provide an optimized maintenance method.

Theoretically, the RCM method will take a lot of time and cost. But in prioritizing the methods, RCM gained more points with regard to safety criteria and value added. The main finding of this study suggests that RCM can be used in the case of elevators and would improve the safety and reliability of buildings.

The authors believe that this study will be valuable because one of the problems in building maintenance complexes is the lack of knowledge about the best method to prevent the risks of elevators. The proposed method has compared with other methods in various sub-criteria, and the results have been fully analyzed.

This paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.

A literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.

Each maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.

The literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.

The novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.

The purpose of this paper is to develop a fuzzy analytic network process (FANP) model to select the maintenance policy of an acid manufacturing company.

Four maintenance strategies of Corrective Maintenance (CM), Time-Based Maintenance (TBM), Condition-Based Maintenance (CBM) and Shutdown Maintenance (SM) are investigated to be considered for seven equipment of the case study. These equipment are almost new and include boiler, molten sulfur ponds, cooling towers, absorption tower, converter, sulfur fuel furnace and heat exchanger. Chang’s extended analysis has been employed to deal with fuzzy data and analyze the fuzzy decision matrices. The proposed approach is applied to a sulfuric acid production plant and the suitable maintenance policy is found for all seven equipment of the company.

Based on the obtained results, the CBM policy is appropriate for high-risk (cooling tower) and high added value equipment (absorption tower). In addition, TBM is selected for boiler and converters while SM is selected for molten sulfur ponds. Finally, high-cost, low-risk and low added value equipment (sulfur fuel furnace and heat exchanger) are more appropriate with CM policy.

This research presents a novel idea to consider cost, risk and added value in the context of maintenance policy selection. From the methodological and theoretical features, this research offers new insights in this area since, to the best of the authors’ knowledge, no comparable study has been conducted before.

Elevator manufacturers are responsible for securing the safety and maintaining the performance, cleanliness and cosmetic appearance of the elevators which they sell.The purpose of this paper is to develop a cost‐optimized after‐sales strategy for the maintenance of an elevator part. First, in order to find an optimum maintenance method, the replacement probability and the inspection probability of the part are theoretically derived using each of the three maintenance methods: Time Based Maintenance (TBM), Condition Based Maintenance (CBM), and Breakdown Maintenance (BM). Then, the approach to plan a cost‐optimized maintenance method is presented and the relationship between the failure characteristics of the part and the cost‐optimized maintenance method is discussed.The results of using the proposed approach are compared with those obtained by Online Quality Engineering (Taguchi methods). Finally, from numerical experiments, it is shown that this approach can provide a more cost‐effective and better maintenance method than the method provided by Online Quality Engineering.