Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

Abstract. Modern manufacturing firms should be supported by effective maintenance to become successful in their operations. One of the approaches for improving the performance of maintenance activities is to implement a total productive maintenance (TPM) strategy. Overall equipment effectiveness (OEE) is the key measure of TPM. According to the results of the literature review, the performance elements measured by the OEE tool are not sufficient to describe the effectiveness of TPM implementation. Hence, we aim at developing and evaluating new performance measures oriented towards the quantification of TPM implementation effectiveness under fuzzy environment. For the evaluation of each performance measure, at first, the nominal group technique has been used. Then to determine whether these performance measures are statistically significant, conjoint analysis based experimental design has been applied. In the second step, COmplex PRoportional ASsessment of alternatives with Grey relations (COPRAS-G) and the fuzzy COPRAS method has been developed to evaluate these performance measures in TPM. Proposed fuzzy COPRAS method gives the reassuring results of ranking newly developed performance measures in TPM.Keywords: COPRAS-G, performance measurement, new performance measures, total productive maintenance, conjoint analysis, fuzzy COPRAS. IntroductionTPM is a new concept for maintenance that better optimizes the equipment effectiveness, minimizes breakdowns and encourages operators to autonomous maintenance for day-to-day activities involving total workforce (Andersson 2015). TPM aims to improve equipment effectiveness during the lifetime of the equipment. 664E. Turanoglu Bekar et al. Fuzzy COPRAS method for performance measurement in total productive ... Nakajima (1988) initiated TPM concept in the 1980s, which brought measurable metric named OEE for measuring productivity of individual equipment in a factory. It explains and measures losses of significant sides of manufacturing specifically availability, performance, and quality rate.OEE approach has been starting to be widely used as an important quantitative metric for measurement of productivity in manufacturing operations (Huang et al. 2003). The use of OEE varies from one industry to another, and it is tailored to fit to comply with industries' specific requirements.According to the literature review on performance evaluation in TPM, OEE metric has widely been used as an important performance measure, but it is not adequate to define the effectiveness of TPM. Jeon et al. (2011) also suggested measuring the performance of TPM in terms of efficiency. This has caused to a requirement for a thoroughly described performance measurement system for TPM which is capable of considering different significant elements of productivity in a manufacturing process. Therefore, in this study new performance measures having an impact on TPM are proposed and proposed performance measures are evaluated under fuzzy environment.The innovative side of this study is to develop new per...

People buy insurance to protect themselves against possible financial loss in the future. Health insurance provides protection against the possibility of financial loss due to health care use. A selection among health insurance options is a multiattribute decision making problem including many conflicting criteria. This problem can be better solved using the fuzzy set theory since human decision making is generally based on vague and linguistic data. We propose an integrated methodology composed of fuzzy AHP and fuzzy TOPSIS to select the best health insurance option. The considered option types, Health Savings Account (HSA), Flexible Spending Accounts (FSA), and Health Reimbursement Arrangement (HRA) are evaluated using eight different criteria under fuzziness. A sensitivity analysis is also realized. With competition increasing among health care plans, employees of large firms typically are faced with more opportunities for choice and more complex options than in the past. Most people understand that an insurance choice may be important for them, but their decision making also appears very limited. The dominant model of choice assumes careful examination and weighing of alternatives, but as the number of choices grows, this becomes an increasingly complex and difficult task.The selection of health plans and providers is, in part, an iterative process in which individuals better learn to make informed choices responsive to their needs. The instability in initial choices, however, suggests considerable failure to understand important differences among plans in access, cost, and freedom to select providers or to be reimbursed for services outside the plan. This is particularly true as populations select plans with which they have had little prior experience. Some preferences seem much more central to people's decisions than others. The primary preferences are related to the character of established doctor/patient relationships, cost, and special needs (Mechanic 1989).There are numerous different health insurance plans available in any country today. Because every person has their own unique situation, determining "the best" health insurance plan will vary from person to person. Situations vary and health insurance plans that are right for one person's situation may not be right for someone else's. This is why we propose a methodology for the selection among health insurance options in this paper.Individual choice over health insurance policies may result in risk-based sorting across plans. People are generally unsuccessful in selecting an alternative with more than four criteria. In approaching choice situations, people immediately make efforts to narrow the number of operative choices to a psychologically manageable set. Typically, individuals consider very few alternatives and focus the comparison only on a subset of the many relevant dimensions. People are more likely to select familiar options. Familiarity may be assessed by prior experience with a particular type of health plan arrangement (Mechanic 1989).The...

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