Innovative Characteristics

IC01

Predictive maintenance in the production line of one of the most advanced water heaters in the world (UC01)

  • Unit: n/a

  • Market Status: No solution exists mixing predictive maintenance, adaptive maintenance scheduling and bottleneck analysis.

  • Project Objectives: Improve value stream by adding embedded AI maintenance devices; forecasting equipment failure; generating intelligent scheduling of maintenance times; and identifying and predicting bottlenecks.

  • Relative Significance: 15%

  • Achivements:

  • Scientific:

  • Industrial: TBD

IC02

Improvement of maintenance plans of injection molding machines (UC04)

  • Unit: n/a

  • Market Status: Several solutions for preventive maintenance. Very few for predictive with embedded AI.

  • Project Objectives: Improve maintenance plans by adding embedded AI maintenance devices; forecasting equipment failure and intelligent maintenance time scheduling.

  • Relative Significance: 15%

  • Achivements:

  • Scientific:

  • On-going paper writting (applied on [UC04A])

  • Paper [P15] (applied on [UC04B])

  • On-going PhD thesis about “equipment failure forecasting” (applied on [UC01A], [UC04A])

  • On-going PhD thesis about “degradation models” (applied on [UC01B], [UC04B])

  • Industrial: TBD

IC03

Improvement of leakage test reliability of (gas) water heaters (UC02)

  • Unit: n/a

  • Market Status: Similar solutions but not for this particular problem.

  • Project Objectives: Reduce product rejections by validating (false) rejections with external sensors and predicting product rejections.

  • Relative Significance: 15%

  • Achivements:

  • Scientific:

  • Industrial: TBD

IC04

Improvement of quality control tests on equipment subject to external vibrations (UC03)

  • Unit: n/a

  • Market Status: Some solutions but none mixing AI and root cause analysis.

  • Project Objectives: Improve the product testing plans by determining product rejection root causes and forecasting product NOKs.

  • Relative Significance: 15%

  • Achivements:

  • Scientific:

  • Paper [P10] (general RC approach)

  • Paper [P06] (theoretical emthodology)

  • MSc thesis [T01] (applied on [UC03])

  • On-going MSc thesis about “automaic repair recomendations” (applied on [UC03])

  • On-going MSc thesis about “optimal re-ordering of steps in quality control tests” (applied on [UC08])

IC05

Frameworks for Equipment Failure Forecasting (FEFF)

  • Unit: n/a

  • Market Status: We haven’t found any comparable commercial solution.

  • Project Objectives: Produce a decentralized AI solution for predictive maintenance, adapted to the Portuguese context.

  • Relative Significance: 8%

  • Achivements:

  • Scientific:

IC06

Frameworks for Adaptive Maintenance Time Scheduling (FAMTS)

  • Unit: n/a

  • Market Status: Some solutions but with several limitations.

  • Project Objectives: Produce an innovative preventive maintenance solution merging time based, condition based, and risk based techniques.

  • Relative Significance: 8%

  • Achivements:

  • Scientific:

IC07

Frameworks for Bottleneck Identification and Prediction (FBIP)

  • Unit: n/a

  • Market Status: Innovative approach. No commercial solution exists.

  • Project Objectives: Produce a reusable solution for bottleneck detection and forecasting.

  • Relative Significance: 8%

  • Achivements:

  • Scientific:

IC08

Frameworks for Product Rejection Validation and Forecasting (FPRVF)

  • Unit: n/a

  • Market Status: We haven’t found any comparable commercial solution.

  • Project Objectives: Produce a reusable solution for improving quality control process by rejection validation and forecasting.

  • Relative Significance: 8%

  • Achivements:

  • Scientific:

IC09

Frameworks for NOK Prioritization and Root Cause (FNPRC)

  • Unit: n/a

  • Market Status: Innovative approach. No commercial solution exists.

  • Project Objectives: Produce a root cause system merging new techniques in Physics complex networks with telecommunication network techniques.

  • Relative Significance: 8%

  • Achivements:

  • Scientific:

  • Paper [P11] (applied on [UC01])

  • Paper [P10] (general RC approach)

  • Paper [P06] (theoretical methodology)

  • MSc thesis [T01] (applied on [UC03])

  • On-going MSc thesis about “optimal re-ordering of steps in quality control tests” (applied on [UC08])

  • On-going MSc thesis about “image classification of combustion cameras” (applied on [UC06])

  • On-going PhD thesis about “root cause analysis by network methods” (applied on [UC03])