Evaluation of a Predictive Maintenance Framework for Industrial Batch Processes : A Feasibility Study at Seco Tools AB

Abstract: Predictive maintenance is a topic that has been researched and theorized for decades. With the advent of Industry 4.0 and greater technological capabilities in the form of advanced AI, the concept of predicting the need for maintenance in a system or its components is quickly becoming more of a reality for complex processes. The possibility of estimating remaining useful lifetimes would help businesses with maintenance scheduling to avoid unnecessary maintenance actions, but also process failures. Predicting when maintenance is needed would ensure system or component reparation or replacement before they are degraded to the point of negative product quality impact and production losses. While there are many studies on predictive maintenance and how it can be implemented and used in continuous processes, the research on complex batch processes is minimal. Therefore, this thesis aims to construct a framework based on literature for implementing predictive maintenance in batch processes. Parts of the framework are then applied and validated on a complex batch process in the form of sintering at Seco Tools AB. Recommendations are given on how to implement predictive maintenance and what is required in the company’s specific case based on the sintering process’ agreement with the framework. The framework consists of two main parts with several underlying requirements: Data collection and pre-processing and Predictive models. Evaluating the sintering process based on these requirements reveals that many parts of the framework are already in place or possible to implement, while other areas are lacking. There is a need for data cleaning and data related to component health and issues, while the amount of specific parameter data on temperatures, pressures, and similar variables is large. It is possible to predict these parameters accurately through building, training, and validating linear regression models. These predictions can be used as inputs in future models to predict the Remaining Useful Life (RUL) of components or the entire system. Due to the inherent complexity of the sintering process and similar industrial manufacturing processes, which involve numerous interdependent variables affecting product quality and component health, it is imperative to develop machine learning models and neural networks for future predictive maintenance algorithms. Moreover, as highlighted in this thesis, the attainment of predictive maintenance in an industrial environment necessitates prioritizing augmented data collection on component conditions, investing in hardware to bolster computational power, and acquiring the essential expertise to design and implement tailored predictive maintenance algorithms for dedicated manufacturing processes.