Asset reliability and process professionals face an enormous challenge to tame the tsunami of data and turn it into useful information.
With the advent of cheaper IIoT devices, sensor data is growing 50 times faster than business data, with not more than 2 percent of it being used! By 2025, the number of devices will double to 21 billion. Data increasingly comes from multiple sources, including high-frequency vibration, time series, inspection imagery, video, semi-structured and unstructured events, and logs. For sure, the human mind cannot cope fast enough with this data unless supported by Intelligent systems.
While AI has become an essential component of making sense of the data, AI alone is not sufficient to determine cause and actions without raising false positives. A must going forward is the ability to create models that harness AI's processing power and the subject matter expert’s deep knowledge and understanding of operating nuances.
In this presentation, we will discuss the principles and methodologies and explore real-world case examples showing how 3rd gen AI methods combining high-frequency vibration and process data can be effectively used for optimum asset condition monitoring and process productivity.
“R.A.I.” the Reliability.aiTMChatbot
You can ask "R.A.I." anything about maintenance, reliability, and asset management.