On Valuing the Impact of Machine Learning Faults to Cyber-Physical Production Systems

Tyler Cody, Stephen Adams, P. Beling, Laura Freeman
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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.
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评估机器学习故障对信息物理生产系统的影响
机器学习(ML)已被应用于预测和健康管理(PHM),以监测和预测工业机械的健康状况。在生产系统中使用PHM创建了一个网络物理的全层系统。虽然ML提供了对以前方法的统计改进,并将统计模型用于新系统和PHM任务,但当ML接收其输入的系统的行为发生变化时,它很容易受到性能下降的影响。自然变化(如物理磨损)和工程变化(如维护和重建程序)是性能下降的催化剂,并且都是生产系统固有的。根据维护程序对液压执行器中ML性能影响的数据,本文提出了一项模拟研究,该研究调查了ML性能下降需要多长时间才能在串行生产系统的吞吐量中产生差异。特别地,本研究考虑了在液压执行器上进行重建过程之前收集的数据上学习的ML模型的性能,以及在重建过程之后收集的数据上学习的ML模型转移。迁移学习能够减轻性能下降,但对吞吐量仍然有重大影响。得出的结论是,机器学习故障会对生产系统的吞吐量产生剧烈的非线性影响。
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