297. Preventive maintenance decisions through maintenance optimization models: a case study
University of Minho, Portugal
Technology has always been a key driver of change in industry. The deployment of new technologies is determinant to enhance manufacturing systems availability, improve cost-effectiveness, and deliver better and more innovative products and services. Inevitably, maintenance, is influenced by those rapid technological changes. Those modifications strengthen today’s competitive environment, forcing enterprises to adopt practices and methods to optimize maintenance decisions and achieve maintenance excellence. One of the main challenges in the maintenance field is to achieve a high degree of control over maintenance activities. In this context, Computerized Maintenance Management Systems (CMMS) arise as a fundamental tool to support the maintenance strategies. Those systems process data to provide information that supports maintenance activities, including preventive maintenance decisions.
In industry, one of the most common preventive maintenance problems is to determine the best preventive intervention time of productive equipment, by minimizing or maximizing some interest criteria. Traditionally, the preventive replacement of parts or maintenance interventions are defined through the technician’s experience or equipment manufacturer recommendations, without the search for an optimal solution. Scientific approaches allow decision making to be based on facts acquired through real data analysis. In this case, maintenance interventions can be performed at predetermined intervals based on failure time analysis. This process includes failure analysis and the use of mathematical models to determine the optimal decisions in relation to an objective.
This paper reports the implementation of a procedure to support the planning of preventive interventions to be integrated in a CMMS of an automotive company. More specifically, it provides a basis to get a CMMS function that allows to obtain the optimal periodicity or preventive interventions, considering costs. In this paper, the procedure is discussed considering the necessary data and its proper organization and the critical factors for its implementation.
The crucial stages of the development method involve the analysis and reorganization of failures records in the existent CMMS adopting a failure tree structure to facilitate reliability study. In addition, all necessary steps to the reliability study must be defined to determine the optimal periodicity of preventive maintenance interventions. Finally, the method validation will be done to be later integrated in the information system.
The analysis of procedure implementation is based on failure data from a critical item in the manufacturing company. The developed procedure will contribute to improve equipment maintenance decisions and also to support maintenance activities, including, maintenance actions scheduling and spare parts management.
258. Research on Reliability Modeling of CNC System Based on Association Rule Mining
Beihang University, China, People's Republic of
CNC system is the control center of CNC machine tools, and failure positions and failure causes of the CNC system are varied. CNC system itself manufacturing, assembly problems or performance degradation of components caused by failure, called associated failure; failure was caused by external factors such as maintenance reasons, improper installation, misoperation, which was known as non-associated failure and need eliminating in the counting process of reliability modeling. In order to improve the accuracy and validity of the reliability modeling and evaluation, the failure correlation factor is introduced into the reliability modeling of CNC system. The size of failure correlation factor can describe clearly the inter-dependent relationship between failure positions and failure causes, but the relevant literature about how to obtain fault correlation factor is less. This paper considers the data mining technology, sets up the failure time data set, analyzes failure positions and failure causes, uses Apriori algorithm to search the frequent itemsets, and obtains the association rules with minimum confidence. The failure correlation factor is obtained by the confidence degree between the failure positions and failure causes obtained by the association rules. In the process of CNC system reliability modeling, the failure correlation factor is introduced into the model, improving the reliability evaluation accuracy of the modified model for CNC system.
213. Effect of coefficient of thermal expansion (CTE) mismatch of solder joint materials in photovoltaic (PV) modules operating in elevated temperature climate on the joint’s damage
1University of Wolverhampton, United Kingdom; 2Department of Mechanical, Aerospace and Civil Engineering, School of Science and Engineering, Teesside University, Middlesbrough, Tees Valley, TS1 3BA, UK; 3Mechanical Engineering Department, Faculty of Engineering, University of Benin, Nigeria
With failure of solder joints (SJs) in photovoltaic (PV) modules constituting over 40% of the total module failures, investigation of SJ’s reliability factors is critical. This study employs the Garofalo creep model in ANSYS Finite Element Modelling (FEM) to simulate solder joint damage. Accumulated creep strain energy density is used to quantify damage. PV modules consisting of interconnections formed from different material combinations (silver, copper, aluminum, zinc, tin and brass) are subjected to induced temperature cycles ranging from -40 °C to +85 °C. Results show that zinc-solder-silver joint having the highest CTE mismatch of 19.6 ppm exhibits the greatest damage while silver-solder-silver with no mismatch possesses the least damage
110. A conceptual framework of knowledge conciliation to decision making support in RCM deployment
1Pontifical Catholic University of Parana, Brazil; 2Federal Institute of Parana, Telêmaco Borba, 84269-090, Brazil
This paper proposes a conceptual framework that conciliates tacit and explicit information from the maintenance function, generating a new knowledge base used in analyzing and improving decisions in deploying a customized RCM (Reliability Centered Maintenance) model. The transformation of raw information into formal knowledge must generate personalized records in a single database, being available for the RCM deployment phases. By identifying trends and applying Process Mining techniques, hidden patterns and relationships can be uncovered. MCDM/A (Multi Criteria Decision Making/Analysis) methods support the decisions in the stages of RCM implementations. Improving maintenance strategies is an important approach in increasing system reliability and reducing costs.