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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.

Condition‐based maintenance is the diagnosis of component failure or a prognosis of a component's time to failure. The aim of this paper is twofold: a summary of the main assumptions regarding condition‐based maintenance found in the literature into eight postulates, and a comparison of the postulates against industrial practice. The postulates were formulated regarding the technical system, the managerial system and workforce knowledge.

The postulates were examined in a multiple case study of five large firms in the process industry.

The results indicate that some postulates were supported with empirical findings. Limited or no support was found for postulates concerning the application of prognostic activities, use of dedicated software, use of procedures, use of training, and the active management of domain‐related knowledge availability.

Practitioners can use the eight postulates as key elements in the management of condition‐based maintenance technology, and for the comparison of their current condition‐based maintenance practices with what literature generally proposes.

Other researchers have reported on condition‐based maintenance, but most publications focus on applied mathematics and new monitoring and simulation models. Only limited attention was paid to industrial practice so far. The study is one of the first in‐depth empirical studies into actual condition‐based maintenance practice.

The aim of this paper is to develop a contribution to knowledge that adds to the empirical evidence of predictive condition-based maintenance by demonstrating how the availability and reliability of current assets can be improved without costly capital investment, resulting in overall system performance improvements

The empirical, experimental approach, technical action research (TAR), was designed to study a major Middle Eastern airport baggage handling operation. A predictive condition-based maintenance prototype station was installed to monitor the condition of a highly complex system of static and moving assets.

The research provides evidence that the performance frontier for airport baggage handling systems can be improved using automated dynamic monitoring of the vibration and digital image data on baggage trays as they pass a service station. The introduction of low-end innovation, which combines advanced technology and low-cost hardware, reduced asset failures in this complex, high-speed operating environment.

The originality derives from the application of existing hardware with the combination of edge and cloud computing software through architectural innovation, resulting in adaptations to an existing baggage handling system within the context of a time-critical logistics system.

The focus of preventive maintenance (PM) programmes in industry is shifting from a pure statistical basis to online condition monitoring. Examines the shortcomings of statistical‐based PM which are contributing to this shift, and the potential benefits of and current research issues within condition‐based PM. Notes that statistics and quality control techniques will continue to play a critical role in this evolution.

When induction motors are considered, there is no specific cost model for net savings per year due to condition-based maintenance (CBM) covering various parameters such as downtime, energy, quality, etc. The purpose of this paper is to develop a cost model for the financial viability of the implementation of CBM for induction motors.

A literature review has been carried out to identify the existing failure modes of motor, available condition monitoring techniques, the usefulness of CBM and different maintenance models available. Then, a cost model considering all parameters has been proposed.

A cost model has been proposed for the maintenance of induction motors. Method for the economic evaluation of the model has also been suggested in the paper. The application of the model has been illustrated through a case study of a steel plant, which suggests that investment in the condition monitoring of induction motors increases the net profit of the organization.

The proposed model is specifically designed for induction motors. All the motors under consideration are assumed to be of the same specifications, and fault in any motor is supposed to have the same effect on quality, cost, criticality, etc., of the operation and end product.

This paper will help the maintenance manager in decision making when maintenance action has to be carried out for a given motor under CBM for the better utilization of the equipment and resources. This paper also shows how to compute ROI on CBM investment.

The paper provides a cost model for the economic evaluation of implementing CBM for induction motors which will be useful to researchers and maintenance managers in effective decision making and maintenance planning. The methodology and the cost models are the original contribution of the authors.

The purpose of this paper is to develop a comprehensive approach to efficiently integrate maintenance and operation by combining process and condition monitoring data with performance measures.

Intelligent stress, condition and health indicators have been developed for control and condition monitoring by combining generalised moments and norms with efficient nonlinear scaling. The data analysis resulting nonlinear scaling functions can also be used to handle performance measures used for management. The generalised norms provide limits for an advanced statistical process control.

The data‐driven analysis methodology demonstrates that management‐oriented indicators can be presented in the same scale as intelligent condition and stress indices. Control, condition monitoring, maintenance and performance monitoring are represented as interactive feedback loops.

Performance analysis can be based on real‐time information by using various stress, condition and health indices as inputs. Similar approaches can be used for outputs: quality indices, harmonised indices, key performance indicators, process capability indices and overall equipment effectiveness. Since consistent linguistic explanations based on nonlinear scaling are available for all these indices, the analysis can be further deepened with LE modelling. Efficient monitoring with intelligent indices provides a good basis for control and condition‐based maintenance and performance monitoring.

The paper extends the nonlinear scaling methodology and linguistic equations to intelligent performance measures. The methodology provides a consistent way to also represent all information with linguistic terms.

Facilities managers have a host of skills to sustain the functionality of complex buildings, often not provided by them directly, but by the team of specialists they draw upon to effectively plan for the future, whether the resource be money, space or technology. Building intelligence presents a challenge in terms of understanding a wholly new approach to the building management. This paper asks if the intelligent building of today meets the needs of the facilities management team. Does it enable them to manage their asset more effectively? New technologies are converging that will enable a radically new approach to maintenance, enabling remote smart sensing or remote condition based monitoring (CBM). Some of the design and economic issues that arise from this radically new approach to managing built assets are highlighted and the possibilities for a maintenance environment, where wires, power cables and data loggers become a thing of the past, is described.

The purpose of this paper is to develop a preventive maintenance policy with continuous monitoring for a circular consecutive-k-out-of-n: F systems. A preventive maintenance policy is developed based on the system critical condition which is related to the number of working components in the minimal cut sets of the system. If there is at least one minimal cut set which consists of only one working component, the system is maintained preventively (PM) after a certain time interval and the failed components are replaced with the new ones to prevent the system failures. If the system fails prior to the preventive maintenance, the system is correctively maintained (CM) immediately by replacing the failed components.

The mathematical function of the expected cost rate for the proposed maintenance policy is derived. The costs of PM, CM, and replacement per component are considered. The optimal maintenance parameter, which is the PM interval, is obtained by enumeration, and the numerical studies are shown with various system and cost parameters. The performance of the proposed policy is evaluated by comparing its expected cost rate to those of the no-PM and age-PM policies. The percentage of cost increase from the no-PM and age-PM policies to the proposed PM policy is calculated and this value can represents how important the continuous monitoring in this policy.

The proposed policy outperforms other policies. When the cost of CM is high and the cost of PM is low, the proposed PM policy is more suitable.

The system consists of identical components and the component failure times follow an exponential distribution. Continuous monitoring is considered, which means that the component states can be known at any time. Three cost parameters, cost of PM, CM, and replacement per component, are considered.

This paper shows a maintenance problem for circular consecutive-k-out-of-n: F systems. Many studies on this system type focused on the reliability estimation or system design problem. Previous study with this policy (Endharta and Yun, 2015) has been developed for linear systems, although the study used a simulation approach to estimate the expected cost rate. Also, Endharta et al. (2016) considered a similar method for the different types of system, which is linear consecutive-k-out-of-n: F system.

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 system designed to meet the requirements of twenty-first 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 tires 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 condition-based maintenance.

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 world-wide and, with new economic sensors, to other applications that rely on moving conveyor belts.

The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.