Lauren Michaud DAT 260 December 8, 2023 AI for the Industrial IoT: Predictive Maintenance When looking at the inclusion of AI and how it is being used in industry, one of the use cases I found most interesting is the use of predictive maintenance. In the past, when machinery or equipment broke down, it was these things completely failing and stopping the ability to run that would let people know maintenance needed to be performed. Reactive maintenance like this could be time-consuming and caused a lack in efficiency as there would be down time when the machinery and equipment was not running. In addition, many times this would happen, the maintenance could be more intensive as it was complete fails that would need to be fixed. In an attempt to fix the issues these breakdowns would cause, industries then switched to preventative maintenance, or performing routine checks in hopes to catch problems before they became so bad it interrupted work. While this did help, it still could cause problems since there was extra expenditures with routine maintenance checks. Now, industries are using predictive maintenance, or using AI to predict when machinery and equipment needs to be fixed. With this method, there is no need for the maintenance checks to be done at regular intervals since the algorithms are being used to predict maintenance. One industry that benefits from this is the automotive industry. With the use of robotics to build cars, these machines are much more high tech and have the computing capabilities and sensors to be used to gather data on the performance and health of the robotics. There is a high cost to set up the sensors and programs to collect the data from these sensors, but the reduction of machine
downtime greatly improves the efficiency of production with these robotics to build cars. There have been many cases of automotive production facilities switching to these robotic and automated machines because of the efficiency benefits. The use of these machines and the AI technology for predictive maintenance can benefit other industries as well, especially those that rely on heavy machinery as in the past those could cause a lot of production downtime due to the efforts it would require to repair them. However, with the predictive maintenance, being able to have the data of what might fail in the machine and then go in to do maintenance that would prevent a large breakdown greatly improves production capabilities and efficiency in the industry. I believe we will see more industries switching to the predictive maintenance models, since the increased efficiency has high weight on the success of industry compared to the cost to start up this model. Citation: Kapoor, A. (2019). Hands-On Artificial Intelligence for IoT : Expert Machine Learning and Deep Learning Techniques for Developing Smarter IoT Systems. Packt Publishing Ltd.