SparkCognition is partnering with NI and IBM to collaborate on the Condition Monitoring and Predictive Maintenance Testbed. The Condition Monitoring and Predictive Maintenance Testbed integrates machine learning algorithms and models to identify machine failures, reduce maintenance costs and keep operations safe. This is very important for Industrial Internet of Things applications. The goal of the collaboration is to deliver an unprecedented level of interoperability among operational technology and informational technology as organizations search for better methods to manage and extend the life of aging assets in heavy machinery, power generation, process manufacturing and a variety of other industrial sectors.
In a new age of Big Analog 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 realize 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 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.
