Industrial controls - especially “Condition Monitoring” systems - are increasingly supported by sophisticated sensors and data analysis tools. Utilising artificial intelligence (AI) for data classification and analysis, these tools do not simply add intelligence to IoT sensors; they allow engineers to compare machine behavior with highly-tuned prognostic representations. When embedded in remote sensor nodes, AI successfully improves the accuracy and resolution of IoT measurement systems.
AI’s Tools for Artificial Intelligence
The increasing set of hardware and software tools include models, algorithms, software development kits and test suits, which encourage safety and reliability, while also shedding light on the behavior of industrial machinery. With continuous condition monitoring of motors and turbines, systems engineers can recognize possible problems (like bearing wobble) in motorized systems and pinpoint maintenance requirements - long before they become serious.
This doesn’t mean engineers will simply hand over their maintenance decisions to AI engines – there are still problems. One on-going issue plaguing engineers by is “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 rapidly to a change in stimulus. But, does the “local” sensing node deliver enough processing power to properly categorize the data it captures? Equally, 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?
The issue with edge-centric processing is that it has many dimensions, reminds Nalin Balan of Reality Analytics, Inc. (Reality AI), an engineering team specializing in AI-based software for signal analytics. What Balan calls “embeddability” hinges on a number of things, including data dimensionality (a function of how many and what type of sensors are brought to bear), the sample rate of the data converters, the decision window to which the IoT node must respond, and the computational complexity of the signal capture circuits. Reality AI is among the participants at June’s Sensor Expo & Conference.
AI’s Tools for Continuous Condition Monitoring
Industrial applications demanding continuous condition monitoring frequently prioritize capturing rotating, vibrating and repeating movements. Condition monitoring with predictive maintenance accumulates real-time sensor data, with specialized sample rates and sensors – acceleration, vibration, sound, electrical and biometric signals – to recognize signatures of specific events and conditions (see Figure). Reality AI’s tools can be used to generate code for capturing and modeling real-world events and conditions using AI-conditioned signal and sensor inputs.
ACCM Inc.’s chief technology officer — another Sensors Expo presenter —Todd Keitel wraps AI applications in a broad cloak, embracing many specialized technologies. Ultra Wi Band (UWB) positioning, for example, has attracted as many as 150 companies all determined on location finding, Keitel says. Many applications exist in medicine for UWB positioning, including tracking personnel, electronic assets, and surgical navigation. Indoor location positioning has become one of the “Holy Grails” of the mobile technology world.
This information has been sourced, reviewed and adapted from materials provided by Sensors Expo & Conference.
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