Predictive Maintenance Helps Prepare the Way for AI

Joe Weinlick
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Manufacturers that employ predictive maintenance are already one step closer to enjoying the benefits of artificial intelligence. As machine learning progresses and helps firms to become more efficient, these concepts can save manufacturers money on maintenance costs and reduce downtime, creating a less costly and more efficient operation.

How Predictive Maintenance Works

Predictive maintenance gathers data on machines and predicts when workers should take a machine down for maintenance. A computer program examines sensor data, wear and tear on parts, recommendations from the machine's manufacturer and real-time data analysis to anticipate equipment failures. The more data the software collects, the more accurate it becomes.

For example, the machine's sensors notice that the parts it creates slowly change over time. At some point, these changes away from the norm indicate that the machine is not working as efficiently. The computer program must figure out what deviations are enough to warrant maintenance. Knowing this time frame, even a few weeks in advance, can save companies money by preventing complete breakdowns. Manufacturers can prepare for downtime to lessen its effects with predictive maintenance.

One Step Further

Taking this maintenance one step further with artificial intelligence, machines can automatically put in a work order for maintenance teams and order replacement parts. The machines use secondary analytics to figure out optimal times to repair the machine and then determine if there's any secondary maintenance you can perform at the same time. The idea is to use this process to save the most possible money over the long term.

Steps to Take

Outline several steps you can take to prepare for more AI algorithms that go beyond predictive maintenance.

1. Explaining the Process

Everyone must understand why you need this process. Important points include saving money, preventing downtime and reducing waste.

2. Gather Data

For this to work, it's essential to gather data from connected sensors. You also need a maintenance history, equipment usage data and details about the manufacturing execution systems.

3. Analyze the Data

Analyze the information gathered, and interpret it using the numbers on the screen.

4. Enrich the Information

Manipulate the data from multiple sensors to get the big picture. The main goal is to find the optimal point when you should take a machine down for maintenance and then implement maintenance protocols.

5. Get as Much Data as Possible

Once you get better at predicting maintenance, gather as much information as possible about the items that matter most. This makes your computer models more accurate.

6. Share the Data

Share the data with all team members. This helps people envision what should happen throughout the process.

7. Implement the Process

Real-time data gives your maintenance teams the information they need to make decisions. Visual representations of the data make it easier to deploy resources to fix your machines.

Ultimately, your predictive maintenance strategy saves more money when you minimize disruptions and maximize resources. The big hurdle to overcome is funding the technology, but recognize that these AI programs ultimately pay for themselves in cost savings.

 


Photo courtesy of suphakit73 at FreeDigitalPhotos.net

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