Search results

As the world approaches a new millennium, more and more industrial and manufacturing processes are being computerized and rapid retrieval and use of necessary information is vital if an organization is to remain competitive. The present work develops a computerized decision support system to assist in maintenance planning. The system design and analysis, and the decision support system design and development are all developed in an object‐oriented environment. The support system is driven by maintenance performance indices. Both object‐oriented databases and relational databases are used, for transient and permanent entities respectively. A knowledge base with if‐then rules is developed for fault diagnosis and repair. The methodology was applied to a powder coating plant to develop its maintenance decision support system.

The purpose of this paper is to explore the applicability of the Decision Making Grid (DMG) and its usefulness, in practice, in the maintenance of rolling stock in the railway industry. The Class 319 fleet operated by First Capital Connect (FCC) is used to demonstrate the application of the DMG.

The level of use of the data recorded in the CMMS, in the decision making process for reviewing, and updating the maintenance policy, was established through discussions with senior management at FCC. Available decision support systems were then researched, with emphasis on the DMG concept. The advantages of using the DMG and its application in the real world using data for the Class 319 fleet were then explored and are discussed in this paper.

This paper discusses the value in applying the DMG concept in the decision‐making process for prioritising systems and the work that should be done to ensure the maintenance policy takes into account the performance of the units of rolling stock against the most important criteria for FCC. Through the research carried out, it was established that the existing CMMS already records the data required for the application of the DMG, although in itself, the CMMS does not have any decision support capabilities.

Although a number of different CMMSs are used in the railway industry, few or none of these are capable of providing decision support for maintenance. This paper explores the use of the DMG concept to demonstrate the use of data recorded in the CMMS to develop a more effective maintenance policy and to determine exactly which maintenance activities need to be carried out in order to remedy the worst performing systems in terms of the most important criteria as identified by the business.

The aim with this paper is to describe current IT practices within maintenance in Swedish industry, and to outline the future possible developments.

The study is performed as a web‐based questionnaire survey consisting of 71 participants. A systems perspective is applied for capturing the most relevant aspects of IT utilisation.

The IT practices are characterised by high use of business‐specific IT solutions, low use of company‐wide IT solutions, low IT intensity and the client‐server architecture is dominating. The purchase decision and ownership of IT is to high extent tied to the maintenance function. Moreover, IT systems are apprehended as beneficial and connected to real needs. The findings imply a decentralised IT governance form and a mainly vertical (functional) IT support.

The socio‐technical approach suggested in this paper helps us to identify which areas to study, and also shows the tight interrelationship between different factors, layers and systems.

The study results could be used for benchmarking purposes or to highlight state‐of‐the‐art of IT utilisation in maintenance, and thereby set the topic on the corporate agenda.

Studies describing IT utilisation within maintenance in the form of case studies and surveys exist, but they mainly focus on one aspect. This study approached the problem from a socio‐technical perspective, covering several aspects connected to IT utilisation.

The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

The purpose of this paper is to identify the productivity improvement potentials from maintenance planning practices in manufacturing companies. In particular, the paper aims at understanding the connection between machine criticality assessment and maintenance prioritization in industrial practice, as well as providing the improvement potentials.

An explanatory mixed method research design was used in this study. Data from literature analysis, a web-based questionnaire survey, and semi-structured interviews were gathered and triangulated. Additionally, simulation experimentation was used to evaluate the productivity potential.

The connection between machine criticality and maintenance prioritization is assessed in an industrial set-up. The empirical findings show that maintenance prioritization is not based on machine criticality, as criticality assessment is non-factual, static, and lacks system view. It is with respect to these finding that the ways to increase system productivity and future directions are charted.

In addition to the empirical results showing productivity improvement potentials, the paper emphasizes on the need for a systems view for solving maintenance problems, i.e. solving maintenance problems for the whole factory. This contribution is equally important for both industry and academics, as the maintenance organization needs to solve this problem with the help of the right decision support.

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.

We propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.

We found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.

This approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.

This approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.

Sustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.

This is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

The purpose of this paper is to present the development of a maintenance engineering policy in the context of a decision support model based on a production machine process perspective.

The structure of the policy is called the maintenance decision support (MDS) model, which consists of three steps: initial setup, deterioration monitoring, and decision making. A detailed presentation of each step of the proposed model together with a real case example from the pulp manufacturing industry proves the applicability of the model.

Validation of the proposed MDS model is as follows. In Task 1 of Step 1, the cutting, sealing, and perforating line processes are classified as critical machining processes. The analysis of Task 2 of Step 1 found that cutting knife, bearing, and motor are classified as the components that most possibly contribute to the cutting appearance quality. In Task 3 of Step 1, it was found that the cutting knife is classified as a maintenance-significant component with non-repairable and single-component type characteristics. The result of Step 2 suggested that at the 29th hour of operating time, the decision of do-something was suggested. In the following step (Step 3), for the case of the cutting knife, which has been classified as a non-repairable type component, the decision to perform preventive replacement of cutting knife is recommended to be carried out at the 29th hour of operating time.

The uniqueness of this model is that it systematically considers different machinery component(s) characteristics, including single- and multiple-component cases, repairable and non-repairable types, and functional or/and physical failure types, to make maintenance decisions.

The proposed MDS model provides a systematic guideline for identifying, evaluating, and monitoring, which makes maintenance-related decisions. Three significant maintenance decisions can be determined based on the proposed MDS model, which includes an appropriate time-to-perform maintenance, correct maintenance actions to be performed, and the right component required for maintenance (for multi-component cases).

One of the vital elements in considering the production machine process perspective toward the development of the MDS model is the need to use product output/quality characteristics for machine deterioration-monitoring and decision-making processes.

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.

The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.

The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.

The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.

Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

This paper aims to identify the different system approach that is equipped with decision making processes. Presently, most maintenance organisations are still implementing conventional methods rather than fully integrated information and communication technology (ICT) to manage the information database on maintenance of residential building. The significant factor to select an ICT is much more advantageous than just a way to improve interfirm communication and cooperation on maintenance management processes and be able to perform the task needed without stressing the budget. ICT could be a pillar of fundamental importance for the implementation of an effective and efficient maintenance management on residential building and facility.

This paper presents a review of recent publications on the topic regarding residential maintenance systems, which also takes into consideration the heritage structures, due to their same maintenance requirements and processes.

The findings reveal the need for ICT tools and techniques specific to the needs of reducing poor service delivery, inadequate financial support, poor maintenance plan and maintenance backlogs.

The paper concludes with a comprehensive research framework of ICT-based system as the basis for further progress in the development of the residential maintenance schemes of system.

This paper aims to synthesize and categorize the published literatures related to maintenance strategies formulation, selection and implementation in various industries. The purpose of this paper is to develop a conceptual framework based on literature review for formulation of maintenance strategies, selection and the implementation of selected strategies. Further, to study on impact of maintenance strategies implementation in maintenance function.

A literature review has been carried out to identify the existing frameworks related to maintenance strategies formulation, selection of maintenance strategy and implementation of maintenance strategy in the industry. Literature support for all the conceptual constructs referred in the framework has been discussed to establish a logical sequence.

A conceptual framework for maintenance strategies formulation, selection and implementation and its impact in maintenance function has been developed. Further, constructs and sub-constructs which form the basis for maintenance strategies formulation, selection and implementation have been identified from the literatures. In addition, propositions have also been formulated to support the conceptual framework and these propositions provide the logical relationship among the maintenance strategies formulation, selection among the formulated strategies and the implementation of these strategies.

The conceptual framework developed in this paper for maintenance strategy formulation and selection is yet to be empirically tested. The proposed framework can be tested in various industries.

Literature study on maintenance strategy formulation and selection has so far been very limited. Maintenance strategy selection is a critical decision-making problem for the maintenance managers working in the process plant, manufacturing companies, etc. The conceptual framework proposed in this paper will help maintenance managers to asses, formulate, select suitable maintenance strategy and implement for their organization.

The paper provides comprehensive study on maintenance strategy problem which will be useful to researchers, maintenance managers and other professionals in various industries such as process industry, manufacturing industry, etc., to understand maintenance strategy selection problem and implementation of maintenance strategy.