As you think through the ways machine learning (ML) can be used to accelerate your business, it can be helpful to see how other companies have done it. Today, I want to share an example of how General Electric (GE) harnessed the power of ML to transform a large part of their business operations.
Predictive maintenance uses ML and other data science algorithms to monitor equipment and detect potential failures before they become critical. The idea is to use a proactive approach so that their customers can reduce unplanned downtime and improve overall efficiency.
Their efforts are primarily focused on two key areas: Early Warning and Prognostics.
Early Warning: Detecting Anomalies
The first step in GE’s predictive maintenance process is the Early Warning phase, which aims to detect anomalies in a system’s operation as early as possible. By identifying potential problems before they cause failures, GE can provide maximum lead time for necessary maintenance.
To accomplish this, they use both supervised and unsupervised ML algorithms. When they have access to ground truth data about the pattern of normal operations, they used supervised algorithms. When they don’t have that ground truth data, they use unsupervised algorithms.
Prognostics: Forecasting the Future
Once the system has detected an anomaly, their forecasting algorithm predicts the remaining useful life of the part. This approach uses more traditional scientific computing that combines physics, statistical analysis and simulation.
This is a common pattern in real-world ML systems. Often, an ML algorithm does some core piece of the heavy lifting and then a chain of other algorithms work together to deliver relevant results to end users.
Lessons for Entrepreneurs
GE’s ML team is well past the proof of concept stage — between internal usage and external customers, they use this tech to manage hundreds of thousands of assets in aerospace, power generation, transportation, oil exploration, and healthcare.
But they didn’t get there overnight. The GE Research team focuses on advancing state of the art, developing new algorithms and running experiments. Meanwhile, teams of engineers scale up data collection and deploy the latest technology in their commercial products.
After hundreds of cycles through the stages of the data science process (POC > MVP > Deployment), they’ve developed IP that impacts countless businesses all over the world.