NI, IBM and SparkCognition have announced a collaboration to increase the efficiency of industrial machinery using a combination of Industrial Internet of Things (IIOT) and machine learning. This new collabration aims to provide software that can predict component failures before they occur, identify suboptimal operating conditions, and assist with root-cause analysis.
As IOT and machine learning technologies are now being applied to a broader selection of application areas it is encouraging the use of compute-intensive technologies, similar to computing technology used in HPC to train the deep neural networks (DNN) used in machine learning.
‘With IIoT technologies driving vast sensitisation 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.
The collaboration hopes to create a ‘Condition Monitoring and Predictive Maintenance Testbed’ which is being designed to deliver an increased level of interoperability among operational technology and informational technology. This is becoming particularly important as organisations 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 this new age of big data, users can take advantage of machine learning to harness value from unstructured 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 or identify suboptimal operating conditions.
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 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.
‘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.’