To provide a system and a method for fault detection in robotic actuation.SOLUTION: A data driven approach for fault detection in robotic actuation is disclosed. A set of robotic tasks are received and analyzed by a Deep Learning (DL) analytics. The DL analytics includes a stateful (Long Short Term Memory) LSTM. Initially, the stateful LSTM is trained to match a set of activities associated with the robots on the basis of a set of tasks gathered from the robots in a multi-robot environment. The stateful LSTM utilizes a master slave framework based load distribution technique and a probabilistic trellis approach to predict a next activity associated with the robot with a minimum latency and an increased accuracy. Further, the predicted next activity is compared with an actual activity of the robot to thereby identify any faults associated with the robotic actuation.SELECTED DRAWING: Figure 1
SYSTEM AND METHOD FOR FAULT DETECTION IN ROBOTIC ACTUATION