How to Use Predictive Maintenance Machine Learning

  • 7/16/2020
  • Read Time : 4 min
predictive maintenance machine learning

Predictive maintenance is changing the way that machines are being looked after. This internet of things (IoT)-enabled technology is enabling machines to indicate when they might break down, prior to the event. By reducing machine downtime, you can save money, meet demand, and keep your customers happy.

What is predictive maintenance?

Predictive maintenance is the process of predicting a machine’s performance, status, and real-time health by using condition-monitoring sensors. With predictive maintenance, machines are equipped with sensors that are connected to IoT-enabled software that gives users updates and notifications. The IoT-enabled software will absorb vast amounts of data. This data can then be run through various algorithms that can be used to accurately predict when a future breakdown or outage may occur. This form of machine learning allows predictive maintenance to prevent running unnecessary maintenance checks, further reducing waste in terms of time and effort. 

Predictive maintenance is not to be confused with preventative maintenance. Preventative maintenance is the process of scheduling maintenance checks in accordance with usage limits or a present schedule. Although this method reduces machine downtime, it is still not an absolute way of knowing whether the machine is likely to break down or not.

What are the benefits of predictive maintenance?

Predictive maintenance is ultimately about increasing the interval between any maintenance procedures. This has many benefits for your business, including: 

  • Reduction in maintenance costs. The reduced manpower required as a result of less service technician time being used can result in tremendous savings for your business. 
  • Reduction in machine failures. Predictive maintenance has the ability to reduce unexpected machine failures by about 90%. This ultimately results in fewer workplace injuries, less money spent on repair, and products available on time, generating more happy customers.
  • Reduced stock of spare parts. Unused spare parts take up unnecessary space and electricity for storage. By knowing when a machine is likely to need replacement in advance, you’re able to order in the parts you need when you need them.
  • Increased service life of parts. Predictive maintenance can increase the lifecycle of an industrial service plant by up to 30%. A side benefit of predictive maintenance is that the data collected through your machinery can allow you to calculate the mean time between the maintenance that is required. This means you can replace the machinery at a time that is cost-effective for you.
  • Improved worker safety: Unexpected malfunctions can lead to high electrical charges and toxic chemicals being spilled. This can cause unnecessary injury to employees.

How to get started with predictive maintenance

The initial investment in IoT-enabled predictive maintenance will be justified by your return on investment. But how and where is the best place to start?

Start small

When implementing large-scale organizational change, you need to start small to ensure adoption among employees. Focus on rolling out a successful predictive maintenance pilot program first. 

Identify which manufacturing components you want to start with

Some components or pieces of machinery do not require predictive maintenance. As an alternative, lower cost processes may be more cost effective. Similarly, you shouldn’t start with a major, critical piece of machinery. By the time you have learned from your pilot program, you should have better processes in place to run the program. 

Identify the resources you require

Before implementing a predictive maintenance machine learning system in your workplace, you should determine which resources you will require. Calculate your required usage of: 

  • Labor: The amount of man hours needed to start and run your program.
  • Facilities: Where will your predictive maintenance tasks occur in your workplace?
  • Technology: You will need to determine which data collectors, sensors, and internet cameras you’ll need for the operation. You may need temperature sensors, pressure sensors, chemical sensors, vibration sensors, motion detectors, and location tracking. These will need to be connected to data analysis software.

Begin data harvesting

Now that you have your technology and manpower, and have identified your pilot asset, you can start to collect some preliminary data. The most common ways of collecting your data will be: 

  • Electro mechanical systems: Your data collectors and analysis software will record and predict the vibrational energy of your components.
  • Thermography: This monitors heat to determine untoward machine activity. Heat on a surface may indicate friction.
  • Lubrication and wear: The data you gain on your lubrication levels can inform you when to perform maintenance on that piece of machinery.

Develop predictive algorithms

Now that your production line is starting to collect data on itself, you can start to analyze the data. Formulas, algorithms, and machine learning tools can be used to translate this data into actionable maintenance timelines. The benefit of using machine learning predictive maintenance is that the data collected can be used to be better informed for future product design. 

Establish a continuous improvement process

In line with a manufacturing strategy, you must be constantly improving your machine learning predictive maintenance process. Make it part of your process to continually determine if there might be more useful data to collect, or if your algorithms could be giving more accurate results. The benefit of machine learning is that after you’ve collected your first data set, the predictive maintenance model will continue to improve over time. 

Predictive maintenance for modern manufacturing

Predictive maintenance needs to be at the heart of any modern manufacturing business. Lowering inefficiency and maximizing value will give any manufacturer the competitive edge they need in today’s industrial landscape. Predictive maintenance machine learning delivers just that. Find out how you can start your digital transformation with PTC.

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Tags:
  • ThingWorx
  • Aerospace and Defense
  • Automotive
  • Electronics and High-Tech
  • Industrial Equipment
  • Life Sciences
  • Oil and Gas
  • Retail and Consumer Products
  • Digital Transformation
  • Predictive Analytics

About the Author

Prema Srinivasan, Digital Content Marketing Manager

As a Digital Content Marketing Manager, I bring the latest technology stories to the forefront. I'm passionate about engaging readers and empowering decision makers with relevant, up-to-date content.