| Title | On Valuing the Impact of Machine Learning Faults to Cyber-Physical Production Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Cody, Tyler, Adams, Stephen, Beling, Peter, Freeman, Laura |
| Conference Name | 2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS) |
| Keywords | actuator security, composability, Data models, Degradation, fault diagnosis, Hydraulic actuators, machine learning, maintenance, maintenance engineering, Metrics, Probability distribution, Production systems, prognostics and health management, pubcrawl, resilience, Resiliency, Throughput |
| Abstract | Machine learning (ML) has been applied in prognostics and health management (PHM) to monitor and predict the health of industrial machinery. The use of PHM in production systems creates a cyber-physical, omni-layer system. While ML offers statistical improvements over previous methods, and brings statistical models to bear on new systems and PHM tasks, it is susceptible to performance degradation when the behavior of the systems that ML is receiving its inputs from changes. Natural changes such as physical wear and engineered changes such as maintenance and rebuild procedures are catalysts for performance degradation, and are both inherent to production systems. Drawing from data on the impact of maintenance procedures on ML performance in hydraulic actuators, this paper presents a simulation study that investigates how long it takes for ML performance degradation to create a difference in the throughput of serial production system. In particular, this investigation considers the performance of an ML model learned on data collected before a rebuild procedure is conducted on a hydraulic actuator and an ML model transfer learned on data collected after the rebuild procedure. Transfer learning is able to mitigate performance degradation, but there is still a significant impact on throughput. The conclusion is drawn that ML faults can have drastic, non-linear effects on the throughput of production systems. |
| DOI | 10.1109/COINS54846.2022.9854969 |
| Citation Key | cody_valuing_2022 |
On Valuing the Impact of Machine Learning Faults to Cyber-Physical Production Systems