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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.).

To develop and test a model and software‐based support system for better understanding of the interactions between man‐machine‐maintenance‐economy (MMME), and enable cost‐effective decisions.

The study is based on published knowledge and experience within maintenance, maintenance organization and production, and a case study.

Development of a model describing interactions between man‐machine‐maintenance‐economy interactions and MMME software module. MMME test shows its ability to identify, quantify, assess and follow up losses in production time which is necessary when planning effective maintenance actions.

In the paper the focus is to quantify production time losses in order to identify the root causes of the problem. The case study is performed at a manufacturing plant for truck engines.

A systematic approach of how to quantify and evaluate losses in production time in order to identify problems and problem areas within the production. This approach is discussed and motivated with the aim of achieving more cost‐effective decisions in maintenance.

The model and software application developed enables a structured way of analyzing production time losses in order to find cost‐effective solutions to the problems. The model is very flexible enabling it to be customized for a wide spectrum of branches.

The purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.

A literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.

The study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.

The findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

Several previous studies have described the use of maintenance management (MM) at polytechnic institutions. However, a major drawback of those studies is that they did not reveal the actual support for MM as practiced using the conventional method. To describe the full situation at polytechnics, the gaps in the conventional method must be determined. Thus, the purpose of this study is to analyse the gaps in the conventional method for actual MM as practiced at Malaysian polytechnics.

The existence of these gaps must be explored further to reach a better understanding of the utilisation of the conventional method for MM. Primary data were gathered through interviews to develop the proposed system. The major conclusion drawn from the interview results is that there is a lack of comprehensive MM systems that integrate the operation and maintenance processes of facilities management and software programming to guide the decision-making processes. The interview results also reveal irregularities within the MM database used by Malaysian polytechnics related to e-complaints, as well as the fact that the database is unsystematic.

The findings show the need for sophisticated MM at polytechnics.

The identified gaps will guide future improvements to develop MM systems that meet the solution needs.

The purpose of this paper is to presents the current and prospective state of affairs when it comes to the information and communication technologies (ICTs) in condition monitoring (CM) and maintenance, especially for the e-maintenance approach.

The author presents some of the standards for the domain of interest, such as the open system architecture condition-based maintenance. In addition, the e-maintenance approach is gone through as well as such ICTs as, for instance, the emergent web technologies, the service-oriented architecture (SOA), the web services and the Web 2.0 are analysed.

The findings highlight the need for a clearer understanding of the characteristics of different ICTs, such as Web 2.0 technologies, Cloud computing, agent technologies, etc., to be able to use them in an optimal manner for various purposes in the e-maintenance applications. In addition, the standardisation of the emergent ICTs different aspects is shown to be an important factor for the development of different phases of software as well as for its overall acceptance.

The given work presents the current and emergent ICTs for the domain of interest and provides the discussion and various issues connected to these ICTs.

The author provides practical implications of the different ICTs mentioned in the paper, i.e. benefits and possibilities as well as risks when those technologies are implemented for CM and maintenance, especially for the e-maintenance.

The paper provides insight into various current and prospective ICTs for the domain of interest that provides important knowledge for different employees with the objective of a purchase, users of the system, such as technicians, maintenance engineers as well as developers of these systems. Consequently, the paper provides knowledge of different characteristics of the current and prospective technologies, which is important to take into account in order to be able not only to use them in an optimal manner, but also to understand possible constraints if they are used in the system and in applications in the domain.

The purpose of this study is to ensure productive, robust and sustainable production systems by enabling future investments in maintenance. This study aims to provide a deeper understanding of the investment process and thereby facilitate future maintenance-related investments. The objectives are to describe the investment process, map the decision support and roles involved and identify factors influencing the process.

The study was designed as a multiple-case study, with three industrial cases of maintenance-related investments. A structured coding procedure was used to analyse the empirical data from the cases.

This paper provides a deeper understanding of the process of maintenance-related investments. Eleven factors influencing the investment process could be identified, three of which were seen in all three cases. These three factors are: fact-based decision-support, internal integration and foresight.

Investments in modern maintenance are needed to ensure productive, robust and sustainable production in the future. However, it is a challenge in manufacturing industry to justify maintenance-related investments. This challenge may be solved by developing a decision-support system, or a structured work procedure, that considers the findings of this study.

From this study, an extended view of the relation between quantifying effects of maintenance and maintenance-related investment is proposed, including surrounding factors influencing the investment process. The factors were identified using a structured and transparent coding procedure which is rarely used in maintenance research.

OVER the last few years, corrosion monitoring has gained a much higher profile in a wide variety of industries, including among others the aerospace and petrochemical sectors.

This study aims to investigate about the use of artificial intelligence (AI) (man machine relationship) regarding organizational behavior. The aim of this research paper is to analyze whether the current AI is used also to replace man in “creative” activities.

This study is based on a qualitative and explorative approach. It is made a review of the literature with “Scopus” and “Web of Science” databases. The research fields are AI, organizational behavior, man-machine relationship and creativity.

Analyzing whether the intensive use of AI in organizational behavior can replace human work in creative activities.

The connection of AI with creative activities within the organization is only just beginning. For this reason, other sources, like Harvard Business Review, public reports and professional papers found on the internet have been considered. The most important limitation of this paper is that all the results presented here do not concern a single case study.

In this paper, there are some examples that can show the use of AI in creative activities; however, this does not complete the situation facing companies in any sector because the AI technologies used within enterprises are constantly evolving. It is possible to continue to do research in this field.

The paper is meaningful because highlights the development of AI toward creative activities typically of human resources. It is also interesting because it analyzes the exploratory use of AI in increasingly human work, generating positive and negative externalities.

Condition‐based maintenance (CBM) has increasingly drawn attention in industry because of its many benefits. The CBM problem is a kind of state‐dependent scheduling problem, and is very hard to solve within the conventional Markov decision process framework. The purpose of this paper is to present an intelligent CBM scheduling model for which incremental decision tree learning as an evolutionary system identification model and dynamic programming as a control model are developed.

To fully exploit the merits of CBM, this paper models CBM scheduling as a state‐dependent, sequential decision‐making problem. The objective function is formulated as the minimization of the total maintenance cost. Instead of interpreting the problem within the widely used Markovian framework, this paper proposes an intelligent maintenance scheduling approach that integrates an incremental decision tree learning method and deterministic dynamic programming techniques.

Although the intelligent maintenance scheduling approach proposed in this paper does not guarantee an optimal scheduling policy from a mathematical viewpoint, it is verified through a simulation‐based experiment that the intelligent maintenance scheduler is capable of providing a good scheduling policy that can be used in practice.

This paper presents an intelligent maintenance scheduler. As a system identification model, we devise a new incremental decision tree learning method by which interaction patterns among attributes and machine condition are disclosed in an evolutionary manner. A deterministic dynamic programming technique is then applied to select the best safe state in terms of the total maintenance cost.