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What is predictive maintenance?
Predictive maintenance is when companies use augmented analytics and machine learning that assess the state of machinery based on available sensor data, suggesting maintenance operations that can be performed with minimum disruption on business.
Predictive maintenance is a form of proactive maintenance, meaning you act on something before it occurs. This is the opposite of reactive maintenance, which only reacts when a problem has already happened. By implementing predictive maintenance, a proactive strategy, you can save up to 30% on costs by avoiding disruptions during the processing phase.
With the application of the Internet of Things (IoT), live data from machines and sensors is captured. Predictive maintenance algorithms then assess the data and anticipate future failures. Based on this information, companies can do maintenance before an actual error occurs, avoiding unexpected downtime caused by, for example, the delivery time of a critical component.
With the evolution of IoT, predictive maintenance is seen as one of the drivers behind Industry 4.0, bringing us one step closer to real-time data insights and analytics.
Predictive maintenance vs. preventive maintenance
Predictive maintenance is when companies use augmented analytics and machine learning that assess the state of machinery based on available sensor data, suggesting maintenance operations that can be performed with minimum disruption on business.
Predictive maintenance is a form of proactive maintenance, meaning you act on something before it occurs. This is the opposite of reactive maintenance, which only reacts when a problem has already happened. By implementing predictive maintenance, a proactive strategy, you can save up to 30% on costs by avoiding disruptions during the processing phase.
With the application of the Internet of Things (IoT), live data from machines and sensors is captured. Predictive maintenance algorithms then assess the data and anticipate future failures. Based on this information, companies can do maintenance before an actual error occurs, avoiding unexpected downtime caused by, for example, the delivery time of a critical component.
With the evolution of IoT, predictive maintenance is seen as one of the drivers behind Industry 4.0, bringing us one step closer to real-time data insights and analytics.
Preventive maintenance is based on average component or subsystem life expectancy statistics. It is work that is scheduled based on calendar time, asset runtime, or some other time period. Thus it is not based on real-time insights, and unexpected downtime could still occur due to statistical deviations.
Predictive maintenance, in contrast, is based on real-time data that is analyzed and used to forecast failures or corrective maintenance. It is scheduled as-needed, based on gathered real-time conditions of assets. It holds an advantage over preventive maintenance because it only gets triggered when needed, eliminating the chances of working upon a problem when there is none.
Goals of predictive maintenance
Minimizing machine downtime and reducing costs are the main goals of predictive maintenance. By maximizing asset uptime and minimizing unexpected errors, machine reliability will improve. By knowing when your assets need maintenance support, you can eliminate the time you might waste on unnecessary maintenance checks. Eventually, this will reduce long-term maintenance costs and maximize production hours, reflecting positively on net proceeds.
Data collection
The first step in predictive maintenance is collecting data from all of your data sources. As IoT is getting more advanced, there are many different levels available for you to collect machine data from, including control systems, operational systems, network routers, and embedded sensors. All of these sources are of value for predictive maintenance.
Equipment condition monitoring
After collecting data from multiple sources, a process called equipment condition monitoring starts. It checks the status of the machines with periodic and continuous monitoring.
Equipment condition monitoring gives you three insights:
First, it gives you an advance warning that some of your assets will break down.
Second, it indicates the expected failure mode. This is especially beneficial because knowing where the problem is located inside the machine will allow you to eliminate time spent on inspecting the asset to determine the problem, which often takes up most of the time spent on repairing assets. This also allows the pre-ordering of right replacement components.
Lastly, it gives you a prediction about how much time you have to fix it before the asset actually breaks down and causes production downtime.
Equipment condition monitoring is based on variables like vibrations, temperatures, pressure, oil levels, and generated noise. If any of these deviates from normal, the algorithm can then determine a potential impending failure mode.
Data analytics
The key to using predictive maintenance to save on maintenance costs lies in the ability to analyze available data. The data collected previously is analyzed using predictive algorithms that identify trends with the aim of detecting when an asset will require repair, servicing, or replacement.
These algorithms follow a set of predetermined rules or even machine learning that compare the asset’s current behavior against its expected behavior. Deviations are an indication of gradual deterioration that will lead to asset failure. Service technicians can then intervene as required, with a timetable of their choice, to avoid breakdowns.
Benefits of predictive maintenance
By reducing the downtime thanks to predictive maintenance, the corporate will be able to maximize production hours. Every industry has an established machine breakdown rate usually expressed in terms of percentage. A robust predictive maintenance program can direct and focus repairs to the proper fail point or automate adjustments.
Further, by introducing a more focused response to equipment failure, those breakdown numbers can be reduced, saving money for the entire operation through increased efficiency and maintaining high-quality production output.
Selecting the right platform to analyze the data and providing actionable real-time maintenance will be needed to improve interoperability between different IoT technologies.
With such a platform in place, the image of the mechanic shuffling his toolbox toward a breakdown with little or no knowledge of what they will find can be replaced by focused workflow guidelines based on real-time information. This eliminates unnecessary time spent on looking for the cause of the problem, making this a much more efficient process.
Successful preventive maintenance will have a positive impact on your margins. Not only does it have a positive effect on your eventual net profit, but also on return-on-investment (ROI).
To summarize, implementing predictive maintenance will:
- Reduce maintenance costs.
- Reduce breakdown time.
- Increase production hours.
- Eliminate time spent on looking for the problem.
- Have a positive effect on margins.
Investing in predictive maintenance tools will result in tangible benefits for your company, earning back the initial investment needed to set up such a system.
