Details
| Original language | English |
|---|---|
| Pages (from-to) | 60131-60147 |
| Number of pages | 17 |
| Journal | IEEE ACCESS |
| Volume | 13 |
| Early online date | 31 Mar 2025 |
| Publication status | Published - 10 Apr 2025 |
Abstract
A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations.
Keywords
- Fleet knowledge, health estimation, health prediction, industrial application, transfer learning
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Materials Science(all)
- General Materials Science
- Engineering(all)
- General Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: IEEE ACCESS, Vol. 13, 10.04.2025, p. 60131-60147.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Cross-Method Overview of Fleet-Based Machine Health Estimation and Prediction
T2 - A Practical Guide for Industrial Applications
AU - Yan, Xuqian
AU - Woelke, Janis
AU - Bensmann, Boris
AU - Eckert, Christoph
AU - Hanke-Rauschenbach, Richard
AU - Niebe, Astrid
N1 - Publisher Copyright: © 2013 IEEE.
PY - 2025/4/10
Y1 - 2025/4/10
N2 - A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations.
AB - A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations.
KW - Fleet knowledge
KW - health estimation
KW - health prediction
KW - industrial application
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=105003087981&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2025.3556251
DO - 10.1109/ACCESS.2025.3556251
M3 - Review article
AN - SCOPUS:105003087981
VL - 13
SP - 60131
EP - 60147
JO - IEEE ACCESS
JF - IEEE ACCESS
SN - 2169-3536
ER -