We gathered some real applications of predictive maintenance with machine learning in different environments.
Oil pumps maintenance
Halliburton needed to monitor thousands of oil pumps scattered across the United States. Most of them were in areas with no mobile coverage, and even when they had, prices were prohibitive. We partnered with them to implement a monitoring system for their oil pumps while overcoming a common challenge when implementing predictive maintenance in real life: limited connectivity.
The most traditional way to apply predictive maintenance would have been to use a central server. But on these conditions, it would be extremely costly because we would need to transfer all the data to the server, run all diagnosis there, and send it back to take action. So this was not an option.
We devised a solution using these IoT devices attached to the already existing equipment, to do the heavy processing and only alert when something was off. This approach brought all the benefits of predictive maintenance while keeping the internet bill low.
Historically edge devices have lacked computing power to process large amounts of data. This is no longer true as edge devices are becoming more powerful and cheaper (check our comparison on multiple devices running bigger models).
Machine learning on the edge is a great cost-effective way to implement real-time predictive maintenance, even in extremely distributed cases. These devices can not only monitor and alert about pump status but also take remote actions to fix or prevent issues.
Satellites’ terminals monitoring (and maintenance)
We teamed up with SES to build a Medium Earth Orbit (MEO) monitoring solution for the gateways and their customers’ terminals. As a network provider, the most significant risk to SES’s business is outages. The main goal of the project was to predict weather-related outages in a matter of minutes since ~70% of the outages they had were due to weather conditions that interfered between the ground antenna, terminal or gateway, and the satellite signal,
At Tryolabs, we developed an offline-on-site solution in this case as well. For SES, the moment their equipment started to malfunction due to weather conditions, it was too late, and the client’s internet connection was already lost.
To avoid the outage altogether, the monitoring system can now switch to a Geostationary satellite when weather conditions worsen. While this is more expensive and slower, it is not affected by weather fluctuations. With this solution in place, they can now provide a proactive customer’s Service Level Agreement (SLA) management, detecting the outage in real-time or even anticipate it.
Once the above monitoring solution is fully operational, the next logical step was to extend the solution to provide predictive maintenance to the customer’s terminals as part of the SES service. This will avoid long-term outages, prevent expensive parts’ replacement when unnecessary, and reduce the manual work of visiting and monitoring these terminals.
Thermal imagery + machine learning in electrical substations
In this case, thermal images were processed using machine learning to do real-time detection of anomalies in electrical tools to prevent power-equipment failure for power stations located in Chongqing, China.
The team tackling this project realized that multiple reasons could raise the internal temperature of electrical instruments, which end up triggering unexpected problems and potential damage to power systems. They concluded that early detection of these temperature anomalies could prevent larger damage since it can improve inspection speed and effectiveness. In summary, the problem at hand was to conclude if it is possible to detect temperature anomalies fast in order to act quickly, save costs, and prevent a whole system breakdown.
The solution implemented consisted of deploying thermal infrared cameras across ten power substations to monitor temperatures. Once they were able to capture 150 thermal pictures using 300 hotspots, they implemented a multi-layered perceptron (MLP) to classify the thermal conditions into “defect” and “non-defect”.
Per the paper results, after fine-tuning the MLP model, the team was able to achieve an 84% accuracy, which was good enough for them to demonstrate the ROI of deploying this predictive maintenance system.