###### Abstract
Our premise in this study is to develop and deploy an adaptive process scheduling that integrates PM analysis as a safety component in scheduling for an operation mode. First of all, we develop a digital twin of an industrial process, in our case a conveyor belt system run by a servo motor ([[https://github.com/cangorur/servo_motor_simulation](https://github.com/cangorur/servo_motor_simulation)]([https://github.com/cangorur/servo_motor_simulation](https://github.com/cangorur/servo_motor_simulation)), accessed on 30 April 2021), providing massive data to generate a \"fingerprint\" (i.e., a large collection of nominal operational behavior data) for the system that is used both to train for a more accurate PM and to calculate the expected system behavior for a requested operation during runtime. For our implementation of a PM approach, we conduct a comparative analysis to select the best fitting algorithm to our setup since the performance of an anomaly detection algorithm depends on the pattern of the generated data. We implement commonly-used anomaly detection approaches and compare their performances in terms of training time, classification time, and classification accuracy to finally select and integrate one-class support vector machine (OCSVM) approach as the basis of our real-time PM. Our main contribution is a novel PM-based adaptive process scheduling pipeline that incorporates obtained PM analysis as feedback to select a new operation mode for an industrial process that prevents machine breakdowns while keeping the operation running with minimum performance degradation. We believe that this pipeline can complement various industrial processes as high-level decision-making to regulate the process toward a safe operation with maximum efficiency. Our mechanism is a closed-loop system that continuously runs PM analysis and each time an abnormal behavior is predicted, a PID error is calculated to analyze the deviation of current system behavior from its nominal behavior, that is, the expected system behavior at a requested operation. An example would be that the expected power drainage at a requested conveyor speed can be calculated by the digital twin during runtime and our system monitors how much the current drainage deviates from it. A process scheduler then schedules an operation that compensates for this deviation in behavior: the new plan reduces the performance (e.g., the running speed of the conveyor) as close to the original request as possible provided that the power readings are within the allowed safe region of a newly scheduled operation. In this way, the risk of causing further damages to the system is reduced while maximum possible performance is still maintained. As for the scheduling, we are agnostic to the algorithm used. We select a state-of-the-art genetic algorithm (GA) as it is one of the most commonly used meta-heuristic adaptive control techniques [9]. Each chromosome in the GA model describes a different operation mode, in our case a range of nominal speed and power values for the conveyor to safely operate, which are obtained from the fingerprint. The scheduler, i.e., the GA, decides on one of these operation modes according to the optimization criteria, that is, system safety and performance. The pipeline also allows a user to adjust the optimization goals by selecting either a performer mode (more weight on maximum speed) or a protective mode (more weight on minimum power), both of which still optimize to protect the machine's health. To demonstrate the applicability of our pipeline, we implement it on a small-scale conveyor belt as part of a project called CHARIOT ([[https://chariot.gt-arc.com/](https://chariot.gt-arc.com/)]([https://chariot.gt-arc.com/](https://chariot.gt-arc.com/)), accessed on 30 April 2021) [10], which aims at the holistic inter-networking of a heterogeneous set of devices and human actors through unified IoT middleware and data models.