As artificial intelligence (AI) continues to advance processes and tasks throughout a variety of sectors, the manufacturing industry has applied this technology in machine learning, which focuses on developing algorithms and statistical models that computer systems can use to perform specific tasks without explicit instructions. Instead of being programmed to perform a specific task, machine learning algorithms are trained on large amounts of data to learn patterns and make predictions or decisions based on that data.

One way in which machine learning can be applied is with predictive maintenance or equipment failure predictions. Equipment failure prediction using machine learning involves using historical data from machines to train models that can predict when a machine is likely to fail in the future. Here’s a general overview of how it works; relevant data is collected from sensors, logs, and other sources. This data typically includes information about the machine’s operating conditions, performance, and any past failures. Features are then extracted from the raw data that can be used to train the machine learning model. These features could include things like temperature, pressure, vibration, and other indicators of the machine’s health.

Once complete, a machine learning model is trained on the historical data to learn the patterns that indicate an impending failure. Different machine learning algorithms can be used for this task, such as decision trees, random forests, or neural networks. The model is trained and can be used to make predictions on new data. By feeding real-time data from the machines into the model, it can predict when a machine is likely to fail, allowing for proactive maintenance to be performed.

Based on the predictions, maintenance schedules can be optimized to minimize downtime and reduce costs. This process presents cost savings that can be seen in multiple ways, including:

1. Reduced Downtime: Equipment failures can lead to significant downtime, which can be expensive in terms of lost production. Predicting failures allows for scheduled maintenance, minimizing unplanned downtime.
2. Optimized Inventory Management: With predictive maintenance, manufacturers can plan for specific parts and components that may need replacement, reducing the need for large, costly inventories of spare parts.
3. Extended Equipment Life: Timely maintenance and repairs can extend the life of equipment, delaying the need for expensive replacements.
4. Improved Safety: Predictive maintenance can help identify potential safety hazards, reducing the risk of accidents and associated costs.
5. Energy Efficiency: Well-maintained equipment tends to operate more efficiently, reducing energy consumption and related costs.

Topics, like AI and technological advancements within the industry, will be explored through the educational sessions at IFE 2024. If you would like to share your knowledge on this subject or any additional industry insights, we invite you to submit a speaker proposal here. Our Call for Speakers will remain open until Friday, April 12, 2024.