Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions
Abstract
:1. Introduction
2. Development of Industrial PHM Solutions
3. Planning Stage
3.1. Subject Matter Expertise Classifications
- Business expertise: Knowledge of the financial aspects of a manufacturing operation. Business experts have an understanding of the production demands that apply to a manufacturing operation and the financial impacts of disruptions. An understanding of the costs (absolute or relative) of false negatives and false positives from a prospective PHM solution is also necessary.
- Reliability expertise: Knowledge of a manufacturing operation’s current reliability strategy. Reliability experts are aware of the initiatives that are currently in place to ensure that a manufacturing operation remains online, including scheduled preventative maintenance procedures and protocols for administering reactive maintenance.
- Equipment expertise: Knowledge of the machine(s) that a prospective PHM solution will monitor. Equipment experts have an understanding of the mechanical and electrical components that make up the machine and an awareness of its potential faults and failure modes.
- Data expertise: Knowledge of a manufacturing operation’s data collection capabilities. Data experts are aware of the signals that are available to be used in a PHM solution and understand how system health problems can manifest themselves in these signals. They are also familiar with the data reporting and storage infrastructure that will be used to implement the PHM solution and can provide insight into how data collection and processing can be improved to better support PHM solutions.
3.2. Planning Methodology
4. Design Stage
4.1. PHM Solution Architecture
4.2. Data Quality
4.2.1. Pre-Processing Historical Data
: An occurrence associated with an unwanted situation within an manufacturing system that must be resolved through maintenance
: Transition of a system from an online state to an offline state for the purpose of conducting a repair procedure
: Transition of a system from an offline state to online state after a repair procedure has been completed
4.2.2. Combined Training Datasets
4.3. Modeling Time-Series Degradation Trends
5. Case Study PHM Solution Development
5.1. Planning
5.2. Requirements and Analysis
5.3. Design
5.3.1. Context Adaptation
5.3.2. Feature Generation
5.3.3. Health Modeling
5.3.4. Output Processing
6. Case Study Insights
6.1. Planning
6.2. Design
6.3. Data Quality
6.4. Modeling Time-Series Degradation Trends
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Toothman, M.; Braun, B.; Bury, S.J.; Moyne, J.; Tilbury, D.M.; Ye, Y.; Barton, K. Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions. Sensors 2023, 23, 4009. https://doi.org/10.3390/s23084009
Toothman M, Braun B, Bury SJ, Moyne J, Tilbury DM, Ye Y, Barton K. Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions. Sensors. 2023; 23(8):4009. https://doi.org/10.3390/s23084009
Chicago/Turabian StyleToothman, Maxwell, Birgit Braun, Scott J. Bury, James Moyne, Dawn M. Tilbury, Yixin Ye, and Kira Barton. 2023. "Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions" Sensors 23, no. 8: 4009. https://doi.org/10.3390/s23084009
APA StyleToothman, M., Braun, B., Bury, S. J., Moyne, J., Tilbury, D. M., Ye, Y., & Barton, K. (2023). Overcoming Challenges Associated with Developing Industrial Prognostics and Health Management Solutions. Sensors, 23(8), 4009. https://doi.org/10.3390/s23084009