###### Abstract  
Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process. p 2021 25  
Since PM is a relatively new application field, how to incorporate predicted machinery states (prognostics) into online decision processes is a rather less discovered area of research. In general, maintenance planning consists of scheduling maintenance intervention based on the state of machinery and carrying out operational decisions to retain or restore a system to an acceptable operating condition [4]. Our research into the online analysis of PM has shown that little to no focus has been given to the latter, that is, the scheduling of operational decisions to adapt better to the changing states of machinery--we refer to this as _adaptive process scheduling_. Many approaches into prognostics only schedule maintenance through simple control measures like condition-based maintenance (CBM) [5], which may also be categorized as preventive solutions [6]. With the advances in PM, recent studies offer prognostic health management (PHM) applications that run online prognostics to predict the remaining useful life (RUL) of a machine to better schedule maintenance intervention based on fault predictions [6; 7; 8]. Even though these approaches protect the machines and greatly reduce breakdowns, we believe that they need to be complemented with operational decisions to adapt to the predicted conditions for a more efficient process, until a maintenance intervention is issued. The advantages of taking PM-based operational decisions can be, for example, delaying a scheduled maintenance intervention by retaining an acceptable operating condition, autonomously recovering when some transient abnormal behavior disappears by itself (e.g., if it is due to a short-lived external disturbance), and finding a new operating condition that may avoid the abnormal behavior and possibly cancel the scheduled intervention. In general, many maintenance interventions and long downtimes could be reduced by such an adaptive process scheduling that optimizes an operation mode. Nevertheless, the conventional process scheduling mechanisms provide dynamic solutions toward maintaining the desired performance, which then usually disregard a machine's health and maintenance actions. We state that a holistic approach to process scheduling should be developed so that an industrial process jointly optimizes for a more efficient and safe mode of operation until a maintenance intervention is issued.