Author
Laura Swanson
Bio: Laura Swanson is an academic researcher from Southern Illinois University Edwardsville. The author has contributed to research in topics: Proactive maintenance & Predictive maintenance. The author has an hindex of 4, co-authored 4 publications receiving 735 citations.
Papers
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TL;DR: In order to achieve world-class performance, more and more companies are replacing their reactive, fire-fighting strategies for maintenance with proactive strategies like preventive and predictive maintenance and aggressive strategies like total productive maintenance as discussed by the authors.
605 citations
TL;DR: Galbraith's information-processing model is applied to study how the maintenance function applies different strategies to cope with the environmental complexity and shows that maintenance responds to the complexity of its environment with the use of computerized maintenance management systems, preventive and predictive maintenance systems, coordination and increased workforce size.
100 citations
TL;DR: In this article, the authors report the results of a study of the relationship between the characteristics of production technology and maintenance practices, based on the responses from a survey of plant managers and maintenance managers.
83 citations
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9 citations
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TL;DR: A framework for classifying business process-modelling techniques according to their purpose is proposed and discussed, and a process model can provide a comprehensive understanding of a process.
918 citations
TL;DR: In this paper, the authors present an overview of TPM implementation practices adopted by the manufacturing organizations and highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementations.
Abstract: Purpose – The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation.Design/methodology/approach – The paper systematically categorizes the published literature and then analyzes and reviews it methodically.Findings – The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here.Practical implications – The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a larg...
521 citations
TL;DR: In this paper, a review of the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitioners is presented, where important issues in maintenance management range from various optimization models, maintenance techniques, scheduling and information systems.
Abstract: Purpose – The purpose of this paper is to review the literature on maintenance management and suggest possible gaps from the point of view of researchers and practitionersDesign/methodology/approach – The paper systematically categorizes the published literature and then analyzes and reviews it methodicallyFindings – The paper finds that important issues in maintenance management range from various optimization models, maintenance techniques, scheduling, and information systems etc Within each category, gaps have been identified A new shift in maintenance paradigm is also highlightedPractical implications – Literature on classification of maintenance management has so far been very limited This paper reviews a large number of papers in this field and suggests a classification in to various areas and sub areas Subsequently, various emerging trends in the field of maintenance management are identified to help researchers specifying gaps in the literature and direct research efforts suitablyOriginali
512 citations
TL;DR: The fuzzy AHP method proposed in this paper is a simple and effective tool for tackling the uncertainty and imprecision associated with MCDM problems, which might prove beneficial for plant maintenance managers to define the optimum maintenance strategy for each piece of equipment.
468 citations
TL;DR: Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR, and experimental results have also shown thatRFs can be more accurate than support vector regression (SVR) without a hidden layer.
Abstract: Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closedform mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR. [DOI: 10.1115/1.4036350]
367 citations