Systems and Methods for Advance Anomaly Detection in a Discrete Manufacturing Process with a Task Performed by a Human-Robot Team

A method for a process control system for detecting an anomaly in a discrete manufacturing (DMP) with human-robot teams executing at least one task within the process, comprising steps of:

• receiving test signals that includes DMP signals from DMP sensors that includes robot operational signals, and human worker (HW) signals from HW sensors including a time series of measurements of tasks completed by the HW;
• predicting a sequence of events from the DMP signals and an expected human-robot team completion time, and determine whether the predicted sequence of events in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted sequence of events from the DMP signals is inconsistent with the behavior, then an alarm is to be signaled;
• and
• extracting from the HW signals, a task completion time, a task name, measurements relating to a state of the HW and a next predicted sequenced task, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, such that outputs of the HW model, the HRI model, or both, are inputted into the DMP model to determine a classification of an anomaly or no anomaly;
• updating the HRI model with the robot operation signals, the HW signals and the classified anomaly, then determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and the classified anomaly;
• and
• outputting the control action of the robot to change a robot action, or output the type of the anomaly alarm to a management system of the DMP, based on the updated HRI model and the classified anomaly, wherein the steps are implemented by a hardware processor connected to a memory.

A non-transitory computer readable storage medium embodied thereon a program executable by a computer for performing a method for a process control system for detecting an anomaly in a discrete manufacturing process (DMP) with human-robot teams executing at least one task within the process, comprising steps of:

• receiving test signals that includes DMP signals from DMP sensors that includes robot operational signals, and human worker (HW) signals from HW sensors;
• predicting a sequence of events from the DMP signals and an expected human-robot team completion time, and determine whether the predicted sequence of events in the DMP signals is inconsistent with a behavior of operation of the DMP described in a DMP model, and if the predicted sequence of events from the DMP signals is inconsistent with the behavior, then an alarm is to be signaled;
• and
• extracting from the HW signals, a task completion time, measurements relating to a state of the HW and a next predicted sequenced task, and input into a Human Performance (HP) model, to obtain a state of the HW based on previously learned boundaries of the state of the HW, the state of the HW is then inputted into a Human-Robot Interaction (HRI) model, such that outputs of the HW model, the HRI model, or both, are inputted into the DMP model to determine a classification of anomaly or no anomaly;
• updating the HRI model with the robot operation signals, the HW signals and the classified anomaly, then determine a control action of a robot interacting with the HW or a type of an anomaly alarm using the updated HRI model and the classified anomaly;
• and
• outputting the control action of the robot to change a robot action, or output the type of the anomaly alarm to a management system of the DMP, based on the updated HRI model and the classified anomaly, wherein the steps are implemented by a hardware processor connected to a memory.