ENBIS Spring Meeting 2026

Session

Condition Monitoring, Diagnostic & PHM

28 May 2026, 10:00

Granada, Spain

Conveners

Condition Monitoring, Diagnostic & PHM

• Inmaculada Torres Castro (University of Extremadura)

18. TTS R Package: Non-parametric Time-Temperature Superposition for Materials Lifetime Prediction

Prof. Salvador Naya (Universidade da Coruña)

28/05/2026, 10:00

Spring Meeting

We present a novel non-parametric method (MNAT) within materials reliability studies to apply the Time-Temperature Superposition (TTS) principle, enabling prediction of long-term viscoelastic material behavior from short-term accelerated tests—critical for polymers, composites, adhesives, and advanced materials where full-scale durability testing spans decades.
This work presents the TTS R...

20. Data-Driven Condition-Based Maintenance Optimization

Bram de Jonge (University of Groningen)

28/05/2026, 10:20

Spring Meeting

Studies on condition-based maintenance optimization typically consider systems with known deterioration processes. In this talk, we make the often more realistic assumption of an unknown deterioration process and we discuss various approaches for determining when to carry out preventive maintenance based on limited condition data. Contrary to many existing studies that consider a certain...

23. Process Analytical technology by ultrasounds for industrial product control

Dr João Henriques (Basinnov Life Sciences, 1070-325, Lisboa, Portugal)

28/05/2026, 10:40

Spring Meeting

Abstract

Process Analytical Technology (PAT) is a regulatory and scientific framework introduced by the Food and Drug Administration (FDA) to promote innovation and efficiency in pharmaceutical manufacturing by enabling in-line, real-time process monitoring and quality control, consistent with quality by design (QbD) principles, rather than solely through conventional laboratory...

3. A Geometrical Method for High Dimensional On-line Process Monitoring

Prof. Enrique del Castillo (Penn State University)

28/05/2026, 11:00

Spring Meeting

I will review recent work where we take an extrinsic manifold fitting point of view, contrary to a manifold learning (i.e., embedding) approach, to develop an on-line monitoring scheme with a simple geometric interpretation which requires neither decorrelation of process dynamics nor dimensionality reduction. The new monitoring framework for online or ``phase II” SPC monitors deviations from...