Quantum Assisted Predictive Maintenance and Scheduling System for Industry 4.0

Abstract: Predictive Maintenance is one of the primary applications of Industry 4.0 for detecting potential equipment failures in advance of unplanned outages. With the increasing number of Industrial Internet of Thing(IoT) sensors available today, there is now the ability to access multi-modal data from machines that can be used to monitor conditions and predict failures. Traditional methods have been limited in their ability to capture the complex relationship between the different sensor measurements and make decisions regarding maintenance based upon limited resources. A quantum- assisted framework for integrating Machine Failure Prediction and Maintenance Scheduling. A Hybrid Quantum Neural Network (HQNN) will be utilized to analyze the different types of industrial sensor data and determine the likelihood of failure. The quantum component of our model will use a parameterized quantum circuit to represent the analyzed sensor data as quantum states; then measure these quantum states to produce a classical representation of the probability of failure. Once we have determined this probability of failure, it will be combined with the priority of the machine and its maintenance needs to create a maintenance scheduling problem. A QAOA-based method will be developed to develop a multi-shift maintenance schedule while considering all available resources.

Keywords: Industry 4.0, Predictive Maintenance, Hybrid Quantum Neural Network (HQNN), Parameterized Quantum Circuit (PQC), CNOT Ring Entanglement, QAOA, QUBO, Shift Scheduling