Industrial network communication is highly deterministic as result of availability requirement of control systems in automated industrial production systems. This deterministic character helps with initial step of self-learning anomaly detection systems to detect periodic production cycle in industrial network communication. The methods for frequent episode mining in event sequences fits well to solve the
challenge of production cycle detection for self-learning system. We encode the network communication events to serial and parallel episodes. Methods for discovery of frequent episodes in event sequences are briefly explained. These methods would be further adapted in future to our encoded network communication traffic to extract production cycle comprised of serial and parallel episodes.