Sensors Expo 2018: Artificial Intelligence Offers New Resolution to Industrial Applications | Sensors Magazine
Industrial controls — particularly “Condition Monitoring” systems — are increasingly supported by sophisticated sensors and data analysis tools. Using artificial intelligence (AI) for data classification and analysis, these tools do not simply add intelligence to IoT sensors. Rather, they enable engineers to compare machine behavior with highly-tuned predictive models. When embedded in remote sensor nodes, AI effectively improves the accuracy and resolution of IoT measurement systems.
The growing set of hardware and software tools include models, algorithms, software development kits and test suits. They promote safety and reliability, as well as shedding light on the behavior of industrial machinery. With perpetual condition monitoring of motors and turbines, systems engineers can identify potential problems (like bearing wobble) in motorized systems and pinpoint maintenance requirements — long before they become critical.
This doesn’t mean that engineers will simply turn their maintenance decisions over to AI engines. Some problems still remain. An on-going issue that engineers are currently struggling with is called “computing at the edge.” It asks where to position microcontroller intelligence most effectively in the IoT sensor architecture: With the sensors and microcontrollers at the head of the signal-processing chain, the controller can respond quickly to a change in stimulus. But, does the “local” sensing node provide enough processing power to properly classify the data it captures? Conversely, positioning the “intelligence” close to cloud servers enables deeper levels of analysis to be summoned. But this will also increase data communications costs and data transfer latency.