Maintenance costs and equipment downtime have a significant impact on the profitability of energy and manufacturing companies. In light of this, no wonder that one of the biggest challenges in these industries is asset usage optimization.
Let’s consider wind farms. Wind farms are usually located in isolated areas, away from the cities. Some of them are even placed in the sea! It is often too expensive to deploy a permanent maintenance department at these locations. Thus, even when minor faults occur (e.g. power outage), technicians must travel all the way to that location to fix the failure.
That’s one of the reasons why periodic maintenance (performed at set time intervals) is very costly and time-consuming.
Lack of proper estimation of the failure probability causes technical inspections to be performed more often than needed. And even with frequent inspections, costly equipment failures still occur.
How to deal with that? The answer is Predictive Maintenance, using Microsoft Azure.
In this article, we’re going to cover the topic of Predictive Maintenance and its application in the energy industry. We’ll use the example of wind farms, but this is applicable for any other business, struggling to optimize the utilization of its assets.
Traditionally, there were two approaches:
Corrective Maintenance is the simplest solution, which minimizes the number of inspections. However, in case of failure, this approach results in long downtime and high replacement costs. Additionally, the long wait time increases the risk that the defective part will also damage others.
Preventive Maintenance (commonly used in the energy industry) significantly reduces the number of uncontrolled failures, but it comes at a high cost of frequent inspections and the replacement of parts that might still be working.
What if we only inspected and replaced parts when it is really needed? Apply the just-in-time strategy for maintenance? With IoT and Artificial Intelligence, it has become possible.
To allow accurate monitoring of their condition, wind turbines have a lot of IoT sensors, detecting the energy flow, wind speed, wind direction, rotation speed, temperatures, humidity, vibrations, stresses, and lubrication pressures.
This amount of sensor data enables not only environmental conditions monitoring but also powerful analytical opportunities.
By analyzing sensor data in real-time, you can predict the probability of equipment failure and act only when this probability is significant. This is what Predictive Maintenance is all about.
Predictive Maintenance provides two major benefits:
Now, let’s see what the Azure-based architecture for Predictive Maintenance looks like. Again, this is the case for wind farms, but it will apply to any other device equipped with IoT sensors.
Below is the explanation of each step in the process.
Below are the three examples of machine learning models worth considering when implementing Predictive Maintenance. Our experience shows that combining different models gives the best results.
Digital Twins model:
Cross Population model:
First, you should define the objective in business terms. We have to remember that predicting the probability of failure isn’t our real goal, but rather a component of a solution that will enable us to optimize the maintenance process.
It’s wise to consider how our solution will be used – what actions need to be taken after failure prediction. Considering the action plan will give us additional requirements (e.g., if the travel time to the wind farm is 30 minutes, we need to detect the failure at least 30 minutes earlier).
We should also think about how predictions performance will be measured. Usually, the recall performance (percentage of predicted failures) is more important than precision (percentage of correctly predicted failures).
In other words, we can accept a certain number of false positives to detect more failures. It is a good practice to perform certain business case calculations to determine the minimum performance of the algorithm (the golden mean between precision and recall) needed to reach the business objectives. This approach will give us our success criteria.
Once we have a well-defined business goal and success criteria, we can follow the machine learning project process:
So, how much can you save on it? Almost 80% of the cost.
Below are the calculations for the wind farm consisting of 100 wind turbines with the comparison of costs for Corrective, Preventive, and Predictive Maintenance.
Compared to Preventive Maintenance (often used in the energy sector, as we mentioned before), Predictive Maintenance generates 79% fewer costs due to the reduction of cost of inspections, downtime, and parts replacement.
What’s next? Can we go even further than Predictive Maintenance?
Yes, we can! With Prescriptive Maintenance. In this approach, we don’t only predict failure events, but also recommend actions to take (understanding the root cause of failure and prevention much earlier than in Predictive Maintenance). But this is much more complicated and I could cover it in another article.
If you have questions or would like to implement this approach at your organization, just let me know!
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