The Latest Trends in IIoT Machine Health Monitoring
Sensor nodes are quickly getting smarter. We saw this clearly at Hannover Messe, the world’s biggest event devoted to industrial digital transformation, with more than 6,500 participants engaged in quality discussion on Industry 4.0 developments, including Predictive Maintenance, security and DaaS (Data as a Service).
This gathering and others have given us an excellent opportunity to meet the main players and discuss ideas and technology roadmaps.
World interest in Machine Health solutions based on vibration monitoring is growing, and for good reason. Vibration is the repetitive motion in response to the mechanical excitation from rotating equipment. Vibration monitoring is a dominant sensing technology in machine condition monitoring, since vibration changes occur at the very beginning of a machine failure process, allowing early diagnostics.
As we observed at Hannover Messe, mass deployment of machine condition monitoring is already here, and many more pilots are in progress. Sensor providers offer various sensors and gateways with improved battery life of 3-5 years, yet with relatively large batteries. Big players like Augury, Sensata, Schafler, SKF, and Emerson provide comprehensive solutions that include sensors, communication, predictive maintenance software, and even cloud services of hyperscalers.
Wireless vibration sensor installations will definitely benefit from optimization of power and data volume. Sensor devices based on energy harvesting are a notable trend, and so is data optimization toward the cloud with data transmission technologies like LoRa. LoRa is a very popular technology, especially in the EU, thanks to its low-cost, low power, and long-range communication abilities. However, vibration sensors’ raw data is hardly suitable for transferring by LoRa due to its narrow bandwidth. Hence the clear need for on-sensor data pre-processing that POLYN’s NASP can offer.
We saw a similar readiness to use data-driven solutions at other smaller and more focused trade shows, such as Tire Technology Expo in Hannover and EcoMotion Week in Tel-Aviv. These highlighted some tire industry challenges that we found particularly interesting: How to monitor and predict tire wear, especially on electric vehicles where the tire load is higher. Or how to understand road conditions from the tire vibration signal? Also, did you know that before a tire explodes, it produces a certain smell that can be detected with neural network help?
EcoMotion Week showed that the transportation industry has accepted machine learning with neural network models as the best choice for various applications. Also, the increasing role of software-intensive electronic hardware and semiconductor hardware – in short, AI chips – in the intelligent vehicle was widely discussed.
Whatever modern industry we look at, manufacturers are taking advanced sensors with powerful AI capabilities into consideration. POLYN can certainly add value with our Neuromorphic Analog Signal Processing Tiny AI products offering dramatic power consumption reduction and smart data pre-processing.