Industrial Genomics: A Novel Approach to System Behaviour Discovery

Abstract

This paper explores a deeper discovery of the concept of industrial genomics which proposes a technique for registering and relating events causing an observable and definable system state and its transfer to another observable state. These industrial genomes are information quanta captured through the digital process to align and represent a chain of activities or processes. They outline the cause-and-effect relationships between events, forming patterns or pathways that ultimately lead to specific outcomes, such as the presence of defects in a product or a machine breakdown. Constructing industrial genomics necessitates understanding the observed or latent parameters of the system's state and how it changes over discrete time intervals. The concept of the proposed industrial genomes, when applied to manufacturing processes, provides a systematic and holistic approach to process optimization, predictive maintenance, and quality control. It has the potential to transform traditional manufacturing processes into smart, efficient, and reliable systems. It could be categorised as a unique method for machine learning.