Condition Based Maintenance | L2L

What is Condition Based Maintenance?

As a manufacturer, you’re an asset whisperer. Maybe you’ve been monitoring the condition of those assets for decades now — but as you’ve likely noticed, recent developments have set the stage for new equipment and reliability needs. Digital manufacturing is here, and it’s all thanks to technologies like the Internet of Things, cloud computing, virtual reality (VR), augmented reality (AR), machine connectivity and wearable devices.

That’s great, sure — but it also means you need to figure out how to prevent entirely new types of failure. As equipment itself evolves, so do the methods for monitoring and maintaining different asset types. 

That’s why condition based maintenance (a.k.a., condition based monitoring) is such a big deal. It empowers factories to employ machine learning and remote sensor monitoring to reap the benefits of predictive maintenance. 

Long story short, condition based monitoring allows businesses to scale up their operations with greater simplicity and ease — and today, we’re going to prove it.

What is condition based monitoring?

A condition based monitoring program is a form of real-time monitoring. It uses a combination of software technology and connected machine sensors to assess and predict when maintenance needs to occur, which means you can kick guesswork to the curb.

With condition based maintenance on your side, the advantages are real:

  • Reduction in production and maintenance costs.
  • Reduction in repair time.
  • Reduction in spare parts costs.
  • Reduction in unplanned downtime.
  • Increase in machine health and asset availability.
  • Increase in production.
  • Extension of asset life.
  • Optimization of maintenance work.
  • Elimination of significant operational waste.

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Condition based monitoring requires a program to collect machine data during operation. (Hint: This works a whole lot better if all your devices are linked with machine connectivity solutions.) Data capture is a job for machine sensors, which detect variations in performance or production.

These variations are then able to indicate imminent failure, and the program will flag the problem prior to failure. Next, the program tells a maintenance technician when and where to proceed, which maintenance task is recommended and how to prevent a potential failure. This helps you:

  • Eliminate unexpected downtime.
  • Boost productivity and quality.
  • Cut service costs.
  • Minimize shrinkage.
  • Simplify stock, plant and equipment tracking on-site and in the field.

Say goodbye to costly, time-consuming repairs and equipment failure prevention. Condition based maintenance gives you the connected data you need to make sure your maintenance staff do the right thing at the right time, which offers huge advantages over the break-fix method (sometimes called reactive maintenance).



As a form of predictive maintenance, condition based maintenance is very intuitive. It naturally builds on the success of the previous fix and incorporates Machine Learning to get smarter every time. 

And the best part is that factors like the complexity of the asset or the presence of actual individuals are no big deal once the system is properly installed. Through methods like ultrasound, thermography or remote monitoring services, data can keep flowing in to trigger the processes that enable a fix. 

Don’t take our word for it

We said we were going to prove that condition based maintenance can do all these things, and hopefully, you’ve seen the logic behind this claim. However, if you still need some convincing, check in on our friends at Sonoco Metal Packaging. After just nine months of condition based maintenance, this manufacturer estimates they saved more than $50,000. That’s a pretty impressive return on investment for just one maintenance strategy.

Hint: This could be you. All you need to do is check out our Smart Manufacturing Platform.

Types of condition based monitoring: Periodic vs. continuous monitoring

There are two types of condition based maintenance: periodic and continuous. If you want to boost your asset monitoring and maintenance game, you’ll need to know the difference.


1. Periodic monitoring

Periodic monitoring is considered a reactive approach, where you fix your machines only when broken, or when you have a planned downtime. This calls for managers to schedule maintenance, but not with any sort of real data.


These are at Level I and Level II of equipment efficiency. What does that mean? Well, check out this handy chart:

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One of the major benefits of periodic monitoring is the low cost barrier to entry: All you need is basic diagnostic functions. However, there’s also some bad news. 

It still only provides a limited window into your production machinery. That means reliability could go down and equipment failure could go up, even with a solid maintenance strategy in place.

Luckily, there’s another option — and that’s continuous monitoring.

2. Continuous monitoring

Continuous monitoring works — you guessed it — continuously. It’s a method of data collection that occurs in an ongoing manner, and it’s best performed through a connected worker platform enabling humans and equipment to speak the same language. That means continuous condition based monitoring requires a truly integrative approach, and that’s why it falls under the predictive maintenance umbrella.  

It also pushes you into Levels III and IV above: proactive maintenance and predictive maintenance. 

In continuous monitoring, machines and situations are counter to the factors of periodic monitoring:

  • Machines run 24 hours a day, which makes them the linchpins of many operations. 
  • They’re not always replaceable and are crucial to the operation.
  • If any critical asset were to fail, there would be serious consequences — including risks to personnel safety.
  • They’re already expensive to maintain, so replacement could be catastrophic.

Predictive monitoring relies on advanced analytics and sensory data to analyze asset reliability, which means you can run more efficiently and with fewer errors. 

It’s all made possible with tools like L2L Connect, which enables condition based maintenance through real-time machine connectivity. If you’re looking for the train headed straight toward reduced equipment failure, this is your ticket.

What Types of Condition Based Maintenance Do Manufacturers Work With? 

Just like a living thing, an asset has a lifecycle. Also like a living thing, an asset’s lifecycle can be extended through proper monitoring (much like you would head to the doctor for your regular checkup) and maintenance (like you might take vitamins or treat an illness).

Predictive maintenance provides this lifecycle extension in a much more responsive way. The data collected through shop floor connectivity and condition monitoring can help you see red flags and dispatch the maintenance team at the perfect time.

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The key factor here is time. If a predictive maintenance program can identify an abnormality and flag it with a technician or manager, any “fix” is actually a matter of maintenance rather than repairs.

As an asset’s life progresses, this trend continues. Technicians and managers can use multiple methods of maintenance, including:

  • Route-Based Monitoring: A technician records data intermittently with a handheld device. This is a preliminary test to figure out if there’s a need for further analysis.
  • Portable Machine Diagnostics: Portable machinery helps monitor the health of machines located in diverse areas. The health of the machine is monitored using sensors, which are attached to the machine permanently, then read by the portable device.
  • Online Machine Monitoring: Through this method, the process of machine monitoring moves online and occurs continuously, as the machine runs in real time. An embedded device captures and analyzes the data, then transmits it to the main server for further analysis and scheduling.
  • Factory Assurance Test: This method is less about maintenance and more about tracking failure. Managers will verify that the finished product meets its design and quality specs, determining possible failure modes.
  • Online Machine Protection: This condition based monitoring method is closely related to online machine monitoring. Here, the machine actively runs, and its limits settings are turned on and off remotely to control the machine during crises.
  • Remote Monitoring Services: Rather than having in-house teams dedicated to analyzing the incoming data, some manufacturing firms will choose to outsource their monitoring to independent consultants or external businesses. The benefit of this is that the entire operation of protection, monitoring, maintenance, repair, analysis and replacement is offloaded onto the contractor and can significantly free up the manufacturing business’s human resources.
  • Ultrasound Remote Monitoring: Ultrasound monitoring is both a method and a tool. Like vibration measurements, ultrasound waves will help technicians look for patterns and then identify inconsistencies with running assets. Use cases include leak detection, electrical inspection and rotating equipment.

Get connected with condition monitoring

We can’t tell you that you must use condition based maintenance. It’s your plant, after all.

What we can do is offer a word to the wise: Don’t overlook this maintenance strategy unless you’re giving up on reliability and accepting potential failure.

Condition monitoring can lead to increased efficiency, greater responsiveness, optimized assets, reduced downtime and waste reduction on all fronts. Of course, if you want these benefits to show up in every maintenance task across the plant floor, you need powerful, next-gen maintenance management software. It has to include machine and worker connectivity, complete integration, tools for big data analysis and more.

Basically, it has to be L2L.

Connected factories are the future because that’s what our present demands. See how real-time data can transform your plant floor with a CloudDispatch demo

 

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What makes L2L so unique is the fact that the product was developed by real manufacturing users. People that truly understand the day-to-day issues and concerns that drive the production floor.

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Kevin Bryan

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