FEATURE
Implementing AI into hardware designs
4 Key steps engineers can take when deploying artificial intelligence.BY CHRIS CATTERTON, DIRECTOR OF SOLUTION ENGINEERING, ONE TECH INC. Artificial intelligence (AI) is becoming increasingly used within our personal or consumer devices, such as smartphones, digital assistants, even automobiles. For example, Gartner predicts that by 2022, 80 percent of smartphones shipped will have on-device AI capabilities, up from 10 percent in 2017. These capabilities include features such as ‘digital me’s’ that make the smartphones an extension of the user, user authentication, natural-language processing and more. But it’s the more ‘durable’ home goods that may receive a bigger boost from AI. While consumers expect their smartphone to last two to three years before they upgrade, they expect appliances such as washers and dryers,
refrigerators and dishwashers to last seven to 10 years—or more. It’s here where the asset performance management (APM) side of AI pays big dividends. APM systems use AI to monitor device health, improving the reliability of physical assets while minimizing risk and reducing costs. According to Arc Advisory Group, APM solutions typically include “condition monitoring, predictive maintenance, asset integrity management, reliability-centered maintenance, and often involves technologies such as asset health data collection, visualization, and analytics.”
Raw sensor values
On all consumer appliances, energy draw is a good indicator of machine health—when energy use is trending upwards, it’s
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trending toward failure. AI systems can listen to bearings, belts, pulleys and other components to get more specific data on what’s failing—before it fails. Engineers have several paths they can take when it comes to deploying AI on this type of consumer equipment and each has its own sets of benefits and challenges. A key benefit of deploying and specifically training AI locally on the asset, is that the raw sensor values that are generated from the equipment can be processed locally, and only transmitted to the cloud when a sign of failure or anomaly is detected with that asset. Without diagnostics powered by AI, owners of such goods may experience a shorter asset lifecycle, increased maintenance costs and unexpected failures.
For simplicity’s sake, let’s walk through the end-to-end process for installing AI on one type of device—a washing machine.
1) Installing AI
a. Direct install on the device. Adding AI to a washing machine could be as simple as downloading an applet using firmware over the air (FOTA) or another means. Many carriers use lightweight M2M (LWM2M), allowing the AI package to be uploaded to the LWM2M server and having it spider out to all applicable devices to reside on. Another possible implementation of AI on this consumer appliance would be building AI into the MCU that is embedded within the asset itself. When the machine is powered on for the first time, the machine learning engine goes through a training period to form a behavioral profile for the asset. Once sufficient data has passed through the machine learning engine, alerts/ notifications are sent out when signs of failure and anomalies are detected. These alerts can then be passed to the asset OEM for further analysis. EPT.CA
Photo: Timplaru Ovidiu / Getty Image
ARTIFICIAL INTELLIGENCE
