Search results

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 introduction of ever more complicated machinery and associated operating procedures is placing an increasing strain on the maintenance function. The options available to a company engaged in the manufacture of a range of sheet metal products, for which a flexible manufacturing facility has been installed, are examined. The introduction of this system has caused the company to examine its maintenance procedures, which are based on a traditional breakdown and repair approach, and has prompted an investigation into the possibility of implementing a condition based approach to maintenance. This investigation is presented here, together with a review of currently available options, and an outline of the planned move towards condition based maintenance.

This paper seeks to study maintenance policies on a plant‐wide level. It focuses on the effectiveness of condition‐based maintenance (CBM). It highlights the role of the production context and the importance of using appropriate metrics to assess CBM.

A simulation model was developed to explore the effects of production context using traditional performance indicators (costs and availability of each piece of equipment) and a more comprehensive metric (line efficiency).

The results showed that CBM has the best performance among other PM policies in loosely coupled processes. By contrast, in tightly coupled processes, CBM has a negative effect on the production line efficiency because it increases equipments' blockage and starvation states.

The simulation model was developed to reflect the reality. Nevertheless, some assumptions have been used to develop the conceptual and computerized model, which can be explored further in future research.

The idea of this paper originates from empirical findings of fellow researchers. The findings in this paper provide a better understanding of how CBM affects key performance indicators in different production contexts and therefore help managers to appropriately execute CBM programmes.

This study focuses on CBM from a new angle. The majority of the literatures on condition‐based maintenance either discusses pure technical issues, or focusses on single equipment only. In this research, the effectiveness of CBM for two processes is studied and CBM is compared with block and age‐based replacement policies using a comprehensive performance indicator.

The purpose of this paper is to describe a flexible architecture concept to provide a ubiquitous computing framework where condition‐based maintenance (CBM) data and information can be easily accessed, and maintenance decisions may be performed wherever required.

The architecture is based on a platform of intelligent web services, and logically structured decision layers based on the open systems architecture for CBM definition, from condition monitoring to decision support, and provides automated extraction of results. The application of web services is demonstrated, first related with an original vision on the adoption of mobile devices for dynamic maintenance management solutions, and then with a more conventional web‐based predictive maintenance management system.

Scenarios for the upgrade of existing preventive maintenance practices towards condition‐based strategies are sufficiently different to require customised solutions. A web‐based platform can work with such differences in a cost‐effective way.

Further research can expand the actual platform by embedding new complementary web services.

Condition‐based strategies can provide clear savings in many maintenance activities. The platform described will provide the necessary flexibility to industrial users to manage the volume of data and information needed in CBM.

The platform is flexible enough to provide intelligent processing “on‐demand” and ubiquitously, with a three‐level configuration of web services, agents and interfaces that facilitates interoperability with existing legacy systems. Finally, the platform can grow according to the needs of the user (e.g. new information, increased knowledge on the process, new measurements, etc.).

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.

Points out that increasing pressure to prolong the useful life of a building without compromising the objectives of maintenance has led to an increasing interest in methods of integrated maintenance management. As an alternative to budget‐driven maintenance strategies, develops a new approach to selecting an appropriate maintenance strategy which relies on determining the consequences of failure of every item in the building, and determining a suitable strategy for each one. Analyses the relative advantages and disadvantages of corrective, preventive and condition‐based strategies. Advocates a novel, systematic approach to the management of building maintenance. Suggests this method will help maintenance engineers and managers to reduce the cost of maintenance while preserving the safety, health and satisfaction of the user.

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.

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 analyze the effects of condition-based maintenance based on unscheduled maintenance delays that were caused by ATA chapter 21 (air conditioning). The goal is to show the introduction of condition monitoring in aircraft systems.

The research was done using the Airbus In-Service database to analyze the delay causes, delay length and to check if they are easy to detect via condition monitoring or not. These results were then combined with delay costs.

Analysis shows that about 80 percent of the maintenance actions that cause departure delays can be prevented when additional sensors are introduced. With already existing sensors it is possible to avoid about 20 percent of the delay causing maintenance actions.

The research is limited on the data of the Airbus in-service database and on ATA chapter 21 (air conditioning).

The research shows that delays can be prevented by using existing sensors in the air conditioning system for condition monitoring. More delays can be prevented by installing new sensors.

The research focuses on the effect of the air conditioning system of an aircraft on the delay effects and the impact of condition monitoring on delays.