Maintenance action recommendation is a critical procedure in industrial engineering, as it can ensure that industrial equipment and machinery operate at peak performance levels, prevent unexpected equipment failures, minimize downtime for higher overall equipment effectiveness (OEE), reduce maintenance expenses, prolong equipment lifespan, provide safety environments for works, and enhance quality control and energy efficiency. This module will analyze a maintenance action recommendation data set by applying the Naive Bayes, Hybrid Quantum Naive Bayes, and Quantum machine learning model (QML). We will set parameters such as qubit numbers to implement this model.
Learning objectives: After completing this module, students will be able to
(i) Describe the maintenance action recommendation and its types
(ii) Explain the importance of maintenance action recommendation
(iii) Apply the knowledge learned in this module to analyze and recommend maintenance actions using various quantum machine learning models