Data Driven State Machine Model for Industry 4.0 Lifetime Modeling and Identification of Irrigation Control Parameters

The emergence of Industry 4.0 revolution has increased the availability of data from various engineering components providing extensive information on different aspects of the industry. In the context of reliability and efficiency, features extracted from data can be utilized to predict performance degradation and optimizing product service as well as determining remaining useful life. The need for prediction models able to establish correlations between related variables and measured variables becomes obvious. In this work, a data driven approach is developed using a state machine concept to realize a lifetime model able to deal with the variability and uncertainties of industrial data. This concept provides a systematic solution for a system exposed to multi-state degradation by enabling the system to identify appropriate parameters autonomously, according to the state it belongs to. Due to the flexibility and scalability, this model can easily be deployed to various types of industrial data. Impressive findings using data from agricultural experiments validate the adaptability and potential of this approach as an alternative modeling strategy.