Collaboration aims to advance IIoT

In a new age of Big Analogue Data solutions, users can take advantage of machine learning to harness value from information. They can collect raw data and derive insights to improve operations, equipment and processes.

Users can also realise huge cost savings and competitive advantages as artificial intelligence-driven prognostics warn of component failures before they occur, identify suboptimal operating conditions and assist with root-cause analysis. 

NI’s open, software-centric platform creates the foundation of the Condition Monitoring and Predictive Maintenance Testbed, which delivers on the opportunities present in machine learning. Customers can apply SparkCognition’s cognitive analytics to proactively avoid unplanned equipment fatigue and failure of critical assets; thus, enhancing system capabilities by gaining advanced insights into equipment health and remediation solutions. These capabilities help increase operational efficiencies and safety, and decrease maintenance costs. 

“With IIoT technologies driving vast sensorisation of industrial equipment, and massive amounts of data being collected on those assets, the collaboration between NI and SparkCognition powers the complex and intelligent processing of information to produce valuable insights,” said Stuart Gillen, director of business development at SparkCognition. 

“We are excited that our platform can acquire the data and extract the features to drive SparkCognition analytics for IIoT solutions,” said Jamie Smith, director of embedded systems at NI. “Combined with existing technologies in the testbed, the addition of SparkCognition presents new ways to help automate the process of turning sensor data into business insight.”  

With this software-defined approach, viewing, managing and refining a broad range of assets stands in direct contrast to the traditional, fixed-functionality methods of the past, which often take too much time, rely on hard-to-find talent and require custom model building for each type of asset.