scholarly journals Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning

2021 ◽  
Author(s):  
Simon Zhai ◽  
Benedikt Gehring ◽  
Gunther Reinhart
Keyword(s):  
Deep Learning ◽  
Production Scheduling ◽  
Predictive Maintenance ◽  
Integrated Production ◽  
Specific Health

scholarly journals Predictive maintenance integrated production scheduling by applying deep generative prognostics models: approach, formulation and solution

2021 ◽  
Author(s):  
Simon Zhai ◽  
Meltem Göksu Kandemir ◽  
Gunther Reinhart
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Health Management ◽  
Mathematical Problem ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Full Potential ◽  
Integrated Production ◽  
Operational Conditions ◽  
Simulated Condition

AbstractTo harness the full potential of predictive maintenance (PdM), PdM information has to be used to optimally plan production and maintenance actions. Hence, operation-specific modelling of degradation, i.e. predictions of the health condition under time-varying operational conditions, has to be realized. By utilizing operation-specific degradation information, maintenance and production can be planned with regard to each other and thus, predictive maintenance integrated production scheduling (PdM-IPS) is enabled. This publication proposes a novel PdM-IPS approach consisting of two interacting modules: an operation-specific Prognostics and Health Management (PHM) module and an integrated production scheduling and maintenance planning (IPSMP) module. Specifically, the mathematical problem of the IPSMP module based on an extended version of the maintenance integrated flexible job shop problem is formulated. A two-stage genetic algorithm to efficiently solve this problem is designed and subsequently applied to simulated condition monitoring, as well as real industrial data. Results indicate that the approach is able to find feasible high quality PdM integrated production schedules.

Computer Integrated Production Scheduling

2019 ◽  
pp. 713-760
Author(s):  
Jacek Blazewicz ◽  
Klaus H. Ecker ◽  
Erwin Pesch ◽  
Günter Schmidt ◽  
Malgorzata Sterna ◽  
...  
Keyword(s):  
Production Scheduling ◽  
Integrated Production

scholarly journals Review—Deep Learning Methods for Sensor Based Predictive Maintenance and Future Perspectives for Electrochemical Sensors

2020 ◽  
Vol 167 (3) ◽  
pp. 037552 ◽  
Author(s):  
Srikanth Namuduri ◽  
Barath Narayanan Narayanan ◽  
Venkata Salini Priyamvada Davuluru ◽  
Lamar Burton ◽  
Shekhar Bhansali
Keyword(s):  
Deep Learning ◽  
Electrochemical Sensors ◽  
Predictive Maintenance ◽  
Future Perspectives ◽  
Learning Methods

scholarly journals Risk-based integrated production scheduling and electricity procurement for continuous power-intensive processes

2016 ◽  
Vol 86 ◽  
pp. 90-105 ◽  
Author(s):  
Qi Zhang ◽  
Jochen L. Cremer ◽  
Ignacio E. Grossmann ◽  
Arul Sundaramoorthy ◽  
Jose M. Pinto
Keyword(s):  
Production Scheduling ◽  
Integrated Production ◽  
Continuous Power
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