Authors:

Austin H Gibson

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About Paper:

The dominant paradigm in manufacturing in the late 20th century was lean manufacturing, which emphasized minimizing costs through the elimination of waste. Since then, the invention and advancement of the Internet of Things (IoT), Big Data and Data Analytics, and Artificial Intelligence (AI) models have laid the foundation for the rise in Industry 4.0, where data generated in real-time is continually assessed by AI models for minimizing costs and maximizing operation efficiency. With modern technologies, the condition of machines can be continually assessed to determine the ideal time to perform maintenance on them, which is called predictive maintenance. The effective application of predictive maintenance can improve equipment longevity, reduce time wasted in needless inspections, and minimize needless expenditures by proactively repairing machines before they break. Sound and temperature data of three vacuum pumps in Birck Nanotechnology Center at Purdue were recorded over four years with low-cost sensors attached to them. The data was curated into an organized dataset and analyzed to build a model to predict maintenance-related outcomes of the vacuum pumps. Furthermore, deep learning models for vacuum pump health monitoring will be developed to support decision-making and reduce maintenance costs. Keywords: Artificial Intelligence; Predictive Maintenance; Machine Learning; Data Analytics; Internet of Things

Source:

Purdue University / 2025

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Co-authors:

Austin H Gibson