You must have observed engineers listening for sounds coming from a machine and say, “something doesn’t sound right. Need to check if anything is broken”. Well in that instance, the engineer is foreseeing an issue on the basis of current operating parameters before any break down occurs. That is exactly what predictive maintenance does.
While it is definitely not a new concept, predictive maintenance (PdM) has been boosted to new heights on the advent of Industry 4.0 technological advancements. With the use of IoT sensors and AI/Machine learning technology, several industries and use cases are now benefiting from the various applications of predictive maintenance technologies.
So, what exactly does predictive maintenance mean, and why is it important? Let us find out.
What is predictive maintenance?
How do predictive maintenance technologies work?
Predictive maintenance is straightforward to understand in theory. It gathers data about your assets and extracts information that allows you to calculate when maintenance needs to take place.
But in practice, there are three steps involved:
Data collection
As previously discussed, our aim is to anticipate when equipment may break down. As this relies on gathering quality information in real-time from sensors installed throughout our facilities, the first step should be installing sensors capable of collecting such information about performance and health issues of equipment in real-time.
Sensors measure and collect information based on your monitoring techniques for equipment. You may control vibration, temperature, pressure, noise level or corrosion levels to monitor its proper function – we will explore various predictive maintenance tools in a moment.
Data mining
Accumulating asset information can only benefit you if it can be effectively leveraged. Thanks to IoT sensors and software systems that stream all sensor information directly into a central system or software for analysis.
One such platform is the OmniConnectTM Cloud Platform, that acts as a central repository to store all data collected from IoT and 3rd party devices. This data is then processed and analysed, enabling managers to make insightful decisions. Such predictive maintenance systems with all assets integrated can provide superior results with less surgical precision.
Calculations and Machine Learning
People often think predictive maintenance ends here. However, by simply acting when sensors detect anomalies you are performing condition-based maintenance as we discussed below. What distinguishes predictive maintenance is its key component – algorithm development which provides prognostic predictions.
Condition based monitoring vs. predictive maintenance
We must now do a bit of a parenthesis. As mentioned above, the term predictive maintenance frequently misunderstood by condition-based maintenance. However, there’s a crucial distinction that differentiates the two. While the predictive maintenance schedule is using data collected as well as formulas, the condition-based type of maintenance acts when the parameters are at alarming levels.
To run a high performing predictive maintenance regimen, appropriate use of IoT and condition-based monitoring equipment is essential. Condition monitoring equipment is used to analyze an asset’s performance in real-time. This includes:
- Vibration analysis
- Ultrasonic analysis
- Infrared analysis
- Oil analysis
- Laser-shaft alignment
- Motor circuit analysis
Using IoT, condition monitoring equipment and sensors can connect in real-time and exchange data. This data “talks” to each other, identifying trends and setting performance parameters. When a sensor detects spikes or dips outside of these parameters, it alerts your team. This way the team remains informed of any equipment on the verge of failure.
For example, let’s say that your chiller’s manufacturing guidelines recommend an oil change every 4000 operational hours. The stationed worker dutifully changes the oil once it reached 4000 operational hours, conducting preventative maintenance. This is done regardless of whether such oil change was needed or not.
Predictive maintenance technologies, on the other hand, makes use of IoT, ML and big data analytics. It conducts continuous or periodic monitoring of equipment conditions to see whether an oil change is needed or not.
Using real-time data sourced from the chiller, PdM determines the appropriate duration after which an oil change would be needed. It can even give an heads up when a certain number of miles are left, so resources can be lined up. And alas, you have on your hands saved time and costs on unneeded maintenance and your chiller performing on peak performance.
Benefits of predictive maintenance
1. Lower maintenance costs
Predictive maintenance helps lower maintenance costs by detecting issues before they lead to major breakdowns and replacing parts as soon as necessary.
2. Reducing machine breakdowns
Monitoring systems help avoid unexpected breakdowns, often leading to fewer machine failures within two years of program implementation.
3. Reduced downtime
Reduced Downtime is the benefit of rapid fault detection on the bottom line. Faster issue resolution helps cut back or even eliminate downtime costs altogether.
4. Smaller stock requirements
Predictive maintenance allows companies to order parts as needed, cutting down inventory costs and waste from unnecessary inventory.
5. Longer equipment life
Predictive maintenance extends equipment lifespan and increases return on investment by addressing problems early.
6. Optimal replacement timing
Predictive maintenance helps identify optimal replacement times by calculating mean time between failures (MTBF). This way, equipment replacement occurs at its most cost-effective moment.
7. Improved production
The most comprehensive condition-based systems improve production efficiency by increasing equipment reliability.
8. Enhanced safety
Early warnings regarding equipment issues increase safety, leading to reduced insurance costs and increasing productivity.
9. Repair verification
Tools such as vibration analysis provide reliable means of verifying repairs for any harmful effects. These protect machine health and ensure efficient downtime scheduling.
10. Boosted profits
Predictive maintenance provides a significant return on investment by improving operational efficiencies, mitigating risks and decreasing annual operational costs.
Conclusion
Predictive maintenance has evolved into a cornerstone of efficient industrial management, blending advanced IoT, machine learning, and real-time data analytics to prevent costly machine failures and downtime. With its proactive approach, predictive maintenance optimizes resources, enhances equipment lifespan, and improves safety standards.
As industries increasingly adopt these technologies, they stand to gain not only from significant cost savings but also from more reliable production and streamlined operations. Predictive maintenance truly represents the future of sustainable, high-performing industrial maintenance strategies.
Want to learn how predictive maintenance can transform operational efficiency in your industry? Talk to our experts.
