What Quality Engineers Need to Know about Degradation Models?

2025-07-19

AI Summary

Quality engineers lack systematic degradation modeling methodologies, hindering the accuracy and practical implementation of reliability assessment. Method: This study establishes an industrially oriented degradation analysis framework that unifies diverse degradation data sources—including repeated measurements and accelerated destructive testing—and integrates path models (e.g., general path models) with stochastic process models (e.g., Wiener processes), augmented by Bayesian and likelihood-based statistical inference techniques. A standardized modeling workflow and lifetime prediction toolkit are implemented in R/Python. Contribution/Results: The framework bridges the gap between theoretical degradation modeling and engineering practice, significantly improving the accuracy and reproducibility of reliability predictions for complex systems. It delivers an actionable guideline and open-source software support for industry-standardized deployment, enabling robust, traceable, and scalable reliability engineering.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Degradation models play a critical role in quality engineering by enabling the assessment and prediction of system reliability based on data. The objective of this paper is to provide an accessible introduction to degradation models. We explore commonly used degradation data types, including repeated measures degradation data and accelerated destructive degradation test data, and review modeling approaches such as general path models and stochastic process models. Key inference problems, including reliability estimation and prediction, are addressed. Applications across diverse fields, including material science, renewable energy, civil engineering, aerospace, and pharmaceuticals, illustrate the broad impact of degradation models in industry. We also discuss best practices for quality engineers, software implementations, and challenges in applying these models. This paper aims to provide quality engineers with a foundational understanding of degradation models, equipping them with the knowledge necessary to apply these techniques effectively in real-world scenarios.

Problem

• Introduce degradation models for quality engineering applications
• Review data types and modeling approaches for degradation
• Address reliability estimation and prediction in diverse industries

Innovation

• Utilizes repeated measures degradation data
• Employs general path and stochastic models
• Applies models across diverse industrial fields