A 2017 study by IndustryWeek in collaboration with Emerson found that unplanned maintenance results in $50 billion of unnecessary costs to industrial manufacturers every year. The single biggest cause (42%) of unplanned maintenance was equipment failure. The study also found that outdated maintenance procedures often resulted in excessive equipment repair and replacement costs, as well as wasted resources and increased staff exposure to safety risks.
Predictive maintenance approaches can modernize these procedures and reduce or eliminate unplanned downtime and its associated costs. By adding local, networked sensors to equipment, and enabling those sensors to run power-efficient AI algorithms right at the sensor node, engineers can automatically classify observed patterns and compare them against a model with multiple defined states.
SensiML Corporation is enabling this advanced level of predictive maintenance for industrial applications via its new Analytics Toolkit. The toolkit makes AI for predictive maintenance easy to implement without the need for large teams of data scientists or firmware engineers. The SensiML Analytics Toolkit enables the quick and easy creation of embedded predictive classification algorithms which can run in real-time on the local sensor microcontroller. The toolkit supports a broad array of low-power SoCs including those commonly used by sensor devices currently for performing simple digital capture and network communication.
“The SensiML Analytics Toolkit makes it easy for industrial sensor manufacturers and intelligent IoT device manufacturers to integrate predictive maintenance capability into their products without the need for large teams of data scientists and firmware engineers to develop capabilities using costly hand-coded methods,” says Chris Rogers, CEO of SensiML. “Our toolkit can rapidly enable such manufacturers to integrate added intelligence into their products such that customers benefit from much-improved service and maintainability.”
Developers can choose to use the information in existing datasets to generate code or collect new data directly from commonly available SoC evaluation boards directly into the SensiML Data Capture Lab application. The analysis supports both novice and expert users with automation and interfaces that greatly simplify the entire process from data collection to model generation to firmware optimization for a given target architecture.