****All content for this blog written by Alexander Eroma, head of Intelligence at Octonion****
In today's industrial IoT eco-system, equipment manufacturers are facing different challenges. One of their pains is the lack of information they have about their equipment usage after the sale. Indeed, it is crucial for an Industrial manufacturer to know how the machine is used, how performant it is, and when it is starting to act 'abnormally'. The goal of having this information is to provide to its end customers a qualified recommendation for maintenance and propose better services to fit customer's needs.
In this context, the idea of having Intelligence at the edge, within the machine, and sharing received knowledge from one to many machines, seems obvious. The manufacturer deals with a fleet of equipment and is what we call “Collaborative Learning”.
In Octonion’s Collaborative Learning approach: each industrial equipment is powered by an Arm Cortex M microcontroller (what we define as the edge) and connected to the Cloud.
Figure 1: Octonion’s edge pipeline
Each edge node manages the unsupervised AI model which provides the output used for the collaborative learning. The node is responsible for:
Octonion’s pipeline of unsupervised learning at the edge can be represented as follows:
Figure 2: Octonion’s edge pipeline
The following is a short description of all the steps of the Octonion’s edge Pipeline that are done on the Arm Cortex M microcontroller:
Now let us consider the principles of knowledge sharing across multiple nodes.
When any device recognizes a novel vibration pattern, this pattern is sent to the cloud to contribute to the shared model. The cloud application builds the model and delivers learned patterns to other edge nodes with the following assumptions:
Figure 3. Patterns sharing
One of the key aspects of this collaborative learning approach is the procedure of model correction in the cloud application.
In the real world, despite that each machine is mechanically unique, N similar machines may have N similar vibrational patterns at a global scale for the same machine operational mode or condition, even if vibration patterns are different at the local level. This approach is the rationale for the procedure of model correction.
Figure 4. Model correction procedure
To manage globalization of different local clustering of each machine, a specific procedure of model correction is triggered. The procedure consists in applying an additional clustering to all known patterns from all nodes, so the similar vibration patterns recognized independently on different nodes can be merged into a single shared pattern. The last step is to find out groups of overlapping and tight shared patterns. Once clustering is complete, the allocated clusters replace the vibration patterns. The appropriate model correction patch is then built and sent to all edge nodes.
The Collaborative Learning process enables building a knowledge database for a given type of machine. , By persistently monitoring one machine’s output and having localized unsupervised learning, anomalies are flagged. These flagged anomalies are then pre-classified for the next similar machine. Through this collaborative learning approach, one machine’s anomalies become another machine’s known error state and previous learnings are shared between devices.
Thanks to Intelligence at the machine level, Industrial manufacturers can get a knowledge per equipment. With Collaborative learning on top, manufacturers can bring this Intelligence to a next level with machines being smarter together.
Learn More about Collaborative Learning