Dynamic production system identification for smart manufacturing systems. (July 2018)

• Record Type: Journal Article
• Authors: Denno, Peter; Dickerson, Charles; Harding, Jennifer Anne
• Is Part Of: Journal of manufacturing systems. Volume 48(2018)Part C
• Page Start: 192
• Page End: 203
• Publication Date: 2018-07
• Subjects: System identification -- Production systems -- Genetic programming; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2018.04.006
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A novel methodology to produce and maintain a model of system capacities, reliability and interconnection. A machine learning process mining methodology for smart manufacturing. Composition and validation of models essential to production control. Abstract: This paper presents a methodology, called production system identification, to produce a model of a manufacturing system from logs of the system's operation. The model produced is intended to aid in making production scheduling decisions. Production system identification is similar to machine-learning methods of process mining in that they both use logs of operations. However, process mining falls short of addressing important requirements; process mining does not (1) account for infrequent exceptional events that may provide insight into system capabilities and reliability, (2) offer means to validate the model relative to an understanding of causes, and (3) updated the model as the situation on the production floor changes. The paper describes a genetic programming (GP) methodology that uses Petri nets, probabilistic neural nets, and a causal model of production system dynamics to address these shortcomings. A coloured Petri net formalism appropriate to GP is developed and used to interpret the log. Interpreted logs provide a relation between Petri net states and exceptional system states that can be learned by means of novel formulation of probabilistic neural nets (PNNs). A generalized stochastic Petri net and the PNNs are used to validate the GP-generated solutions. The methodology is evaluated with an example based on an automotive assembly system.