6 research outputs found
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Distributed diagnostics, prognostics and maintenance planning: Realizing industry 4.0
In this paper, a novel distributed yet integrated approach for diagnostics and prognostics is presented. An experimental study is conducted to validate the performance. Results showed that distributed prognostics give better performance in leaser computational time. Also, the proposed approach helps in making the results of the machine learning techniques comprehensible and more accurate. These results will be handy in arriving at predictive maintenance schedule considering the criticality of the system, the dependency of the components, available maintenance resources and confidence level in the results of the prognostic.Royal Academy of Engineering London, UK (IAPP 18-19/31
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Product quality driven auto-prognostics: Low-cost digital solution for SMEs
Setting out existing prognostics solutions in small and medium enterprises (SMEs) is accompanied by challenges. These include employing expensive sensors, acquisition systems; and attending geometric limitations. Additionally, these solutions call for a specialist to take on feature engineering, machine learning algorithm selection, etc. Presented in this paper is a low-cost digital solution (intelligently integrate cost-cutting off-the-shelf technologies) for SMEs via product quality driven auto-prognostics. First, we develop upon existing solutions by addressing their drawbacks viz. cost, geometric limitations via a new product quality-centered condition monitoring strategy. Every SME must investigate the quality of their products, and therefore the authors believe this to be a low-cost solution. Next, the proposed solution integrates automated machine learning via Auto-WEKA, an off-the-shelf open-source technology. Lastly, the practical advantages of the proposed solution over the existing sensor-based solution were investigated via a case study. Results depict that this low-cost prognostics solution is vital for maintenance planning in SMEs.Royal Academy of Engineering London, UK (IAPP 18-19/31)
Dynamic optimization of process quality control and maintenance planning
In this paper, we propose a novel methodology for dynamic optimization of process quality control and maintenance planning while considering the real-Time health state of the system. First, by investigating the relationship between product quality and tool degradation, a new tool condition monitoring (TCM) system for instantaneous diagnostic and prognostic is proposed. Subsequently, the existing process quality control policy is enhanced to become dynamic and extended to deal with machine deterioration with time. This is done via the proposed residual-life based evaluation and multistate magnitude of process shift schemes. Furthermore, the maintenance planning model is modified to capture real-Time remaining life information. These models are integrated and built in conjunction with developed TCM system. As a result, the designed dynamic integrated model can evolve itself to re-evaluate the optimal values for the design parameters used in the entire lifecycle of the manufacturing process. Finally, an experimental case study is implemented to demonstrate the practical feasibility of the developedmethodology.An extensive performance investigation revealed substantial economic benefits over conventional independent approach. This is further complimented with systematic sensitivity analysis. Moreover, we attempt to present potential implications and guidelines for various industrial scenarios to expand the model's robustness and relevance in industrial environment