SUNNYVALE, Calif., May 23, 2019 /PRNewswire/ -- SensiML
Corporation, a developer of software tools for creating optimized
embedded IoT AI algorithms, today announced that its Analytics
Toolkit supports Artificial Intelligence (AI) for industrial IoT
predictive maintenance applications. By providing a practical
end-to-end solution, the toolkit makes AI for predictive
maintenance easy to implement without the need for large teams of
data scientists or firmware engineers. Manufacturing and other
industrial companies that implement predictive maintenance via this
toolkit have the potential to dramatically reduce operating costs
while simultaneously increasing employee safety.
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outdated maintenance procedures often resulted in excessive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 all of 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. The data for the model can come from historical
data with examples of critical excursions (failure modes) from
theoretical expectations encoded into a functional algorithm or
start with basic anomaly detection models that become sophisticated
with edge learning over time.
In all cases, 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.
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.
"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,"
said 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."
Availability
The SensiML Analytics Toolkit is
available now. For more information, visit
www.sensiml.com.
About SensiML
SensiML, a subsidiary of QuickLogic (NASDAQ: QUIK), offers
cutting-edge software that enables ultra-low power IoT endpoints
that implement AI to transform raw sensor data into meaningful
insight at the device itself. The company's flagship solution, the
SensiML Analytics Toolkit, provides an end-to-end development
platform spanning data collection, labeling, algorithm and firmware
auto generation, and testing. The SensiML Toolkit supports Arm®
Cortex®-M class and higher microcontroller cores, Intel® x86
instruction set processors, and heterogeneous core QuickLogic SoCs
and QuickAI platforms with FPGA optimizations. For more
information, visit www.sensiml.com.
SensiML and logo are trademarks of SensiML. All other
trademarks are the property of their respective holders and should
be treated as such.
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SOURCE SensiML Corporation