2015
DOI: 10.1007/s00170-015-7981-6
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A predictive maintenance approach based on real-time internal parameter monitoring

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Cited by 38 publications
(16 citation statements)
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“…For the first task, usually techniques from the fields of forecasting [5] and prognostics [6], [7] are applied, which can either rely on process parameter settings (static case) or process values recorded over time (dynamic case). Supervision of process parameters were proposed in [8] (there for a phone camera lens injection molding machine) and in [9] (there for plastic injection moulding). Both approaches rely on linear models and static mappings which do not integrate the time component (for respecting varying delays in input variables) and which cannot be (self-)adapted over time to address significant system dynamics properly.…”
Section: Motivation and State-of-the-artmentioning
confidence: 99%
“…For the first task, usually techniques from the fields of forecasting [5] and prognostics [6], [7] are applied, which can either rely on process parameter settings (static case) or process values recorded over time (dynamic case). Supervision of process parameters were proposed in [8] (there for a phone camera lens injection molding machine) and in [9] (there for plastic injection moulding). Both approaches rely on linear models and static mappings which do not integrate the time component (for respecting varying delays in input variables) and which cannot be (self-)adapted over time to address significant system dynamics properly.…”
Section: Motivation and State-of-the-artmentioning
confidence: 99%
“…Therefore, the practice of PdM strategies can result in an increase in global competitiveness through the elimination of unplanned maintenance operations, since PdM techniques help to identify and to determine the conditions of assets before they fail [22]. Further, PdM helps to monitor the condition of selected physical parameters within an operating machine by performing periodic or continuous real-time data collection [22] [23]. Thus, the collected data can be used to discover, with the help of PdM models, current or future deterioration of components and machines [25] [37].…”
Section: E Condition-based Maintenance (Cbm)mentioning
confidence: 99%
“…Within such architectures/ frameworks, the main idea of CBM is to detect the degradation level of machine components by collecting periodic or continuous real-time data with sensor technologies whereas. CBM strategies may involve Predictive Maintenance (PdM), wherein the current state of monitored equipment, along with historical data and relevant domain knowledge and models is employed to predict through statistical or machine learning models trends, behaviour patterns and correlations [11] [22] [23], and anticipate pending failures in advance to enhance the decision-making process for the maintenance activity [24]- [26]. PdM is becoming more and more a crucial approach among modern smart manufacturing industries, since global competitiveness is becoming everyday more challenging.…”
Section: Introductionmentioning
confidence: 99%
“…• Availability improvement -Predictive Maintenance [12], [13], [14] -Prognostics and Health Management [13], [15] • Performance improvement -Predictive Production Planning [16] -Predictive Manufacturing Control [17] • Quality improvement -Predictive Quality Control [18] -Control Chart Pattern Recognition [19], [20] These concepts provide theoretical foundations and require additional specifications for direct applicability. For this purpose, we propose a specified process model focusing on improving availability, since the costs for keeping a high availability represent 15 -60 % of total costs in manufacturing [21].…”
Section: Kdd In Manufacturingmentioning
confidence: 99%