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November 08, 2019
We’ve been monitoring the condition of our assets for decades now–and it’s not a new practice, especially in manufacturing plants.
But recent developments have set the stage for today, where the Internet of Things, cloud computing, big data, tablets, VR/AR, AI, additive printing, and wearable devices are intersecting with businesses around the world. These mark the move toward digital manufacturing.
Each epoch brings a greater number of changes, and they’re happening at an accelerated rate. This rapidity is simply a feature of technology–the more you have, the more you get.
Yet, there’s now a similar shift needed in the maintenance of these technological advancements. As equipment itself evolves, so do the methods for monitoring and maintaining these assets.
That’s why condition based maintenance (a.k.a., condition based monitoring) is so innovative. It empowers factories to employ machine learning and remote monitoring to reap the benefits of predictive maintenance.
Ultimately, condition based monitoring allows businesses to scale up their operations with greater simplicity and ease.
A condition based monitoring program is a form of real-time monitoring that uses a combination of software technology and connected sensors to assess and predict when maintenance needs to occur.
Through predictive maintenance, you can optimize multiple factors, reduce the overall cost of production, and eliminate significant areas of operational waste.
These benefits narrow down into several, visible, and felt effects–not least of which is to improve customer satisfaction at the very end of the chain.
Condition based monitoring requires a program to collect data from a machine (or several in conjunction) while it’s operating. Sensors are responsible for collecting data and detecting changes in the machine.
These changes are then able to predict imminent failure, and the program will flag the problem early on. The next steps generally look like this:
This process short-circuits what used to be time-consuming and costly repairs and preventing them a much simpler occurrence you can plan for.
Thus, condition based maintenance has a clear advantage over the break-fix method.
Condition based maintenance has real-world applications where results consistently cut service costs, improve up-time, cut shrinkage, and help to track assets like stock, plant, and equipment both on-site and in the field.
As a form of predictive maintenance, condition based maintenance is very intuitive. It naturally builds on the success of the previous fix and gets smarter every time.
And the best part is that factors like the complexity of the asset or the presence of actual individuals are not a major factor once the system is properly installed. Through methods like ultrasound or remote monitoring services, data can keep flowing in to trigger the processes that enable a fix.
There are two types of condition based maintenance. The first is periodic, and the second is continuous.
Let’s examine periodic first, and then compare it to its counterpart.
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 1 and Level 2 of equipment efficiency.
One of the major factors in favor of periodic monitoring is the low cost barrier to entry–all you need is basic diagnostic functions. Yet, it still only provides a limited window into your production machinery.
Continuous monitoring works exactly like it sounds: continuously. It’s a method of data collection that occurs in an ongoing manner. It requires a truly integrative approach, and that’s why it comes under predictive maintenance.
It also pushes you into levels three and four above—proactive and predictive maintenance.
In continuous monitoring, machines and situations are counter to the factors of periodic monitoring:
Predictive monitoring relies on advanced analytics and sensory data to assess machine reliability, which means you can run more efficiently and with less errors.
The life of assets goes through a predictable series of stages. Predictive maintenance extends the life of these assets in a much more responsive way. The data collected through specific methods of condition based maintenance can help machines make decisions about warning signs.
The key factor here is time. If a predictive maintenance program can zone in on and bring the defect to the attention of a technician or manager, then this lead time means that any “fix” is actually a matter of maintenance, rather than repairs.
As the life of the assets progresses, this trend continues. Technicians and managers can use multiple methods of maintenance, including:
With condition based maintenance on your side, the advantages are real, and can show results such as:
It would be wise not to ignore condition based monitoring, which as shown above, can lead to increased efficiency, greater responsiveness, optimized assets, reduced downtime, and a reduction in “waste” on all fronts.
Implementing the benefits of condition based maintenance relies heavily on powerful, next-gen maintenance management software, designed to integrate these functions and bring large data sets together for visual analysis.
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.
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.