How Edge IoT Platform Increases Efficiency, Availability and Productivity
In 4 years,
more than 30% of businesses and organizations will include edge computing in their cloud deployments to address bandwidth
bottlenecks, reduce latency, and process data for decision support in real
time. Edge computing accomplishes this by bringing the businesses’
computational processes closer to the data sources, increasing the speed of
these actions. Additionally, even if a single node is unreachable, the service
should still be accessible to users. In this way, edge computing promises to
deliver Internet of Things (IoT) reliably and speed while taking more care of
security, data privacy.
What’s more, 69% of organizations say
that prioritizing edge-based analytics will improve their ability to meet IoT
objectives for specific use cases.
Industries
including Manufacturing, Water & Wastewater, Utilities and Building, are
implementing hybrid strategies to enable real-time analytics such as Machine
Anomaly detection and diagnostics, Quality Analytics, Energy Analytics, and OEE
(Overall Equipment Effectiveness). First, anomaly detection on the edge
leverages Machine Learning to monitor machine health, detect anomalous data
from sensors, and reduce the time it takes to get critical information.
Advanced notice of anomalous machine behavior gives maintenance employees to
prevent breakdowns before they occur, saving the business time, money, and
resources.
Additionally,
running quality analytics on the edge enable faster decision making , which is
important for many industries. This type
of data analysis on the edge is important for businesses which use real-time
data to improve productivity, require solutions that scale over time, or reside
in a fast-paced environment full of unexpected changes. Edge computing gives
you access to analytics and actionable insight on edge, right where the data is
generated.
Also, energy
analytics on the edge has allowed utility companies to get real-time data at
remote energy production facilities such as wind turbine farms or solar farms.
It is not practical for remote equipment at these locations to quickly transmit
data to and from the cloud, slowing the data analytics process. However, if
data is quickly processed on edge computing devices, employees have access to
real-time data which reflects the current state of energy production.
Lastly,
Overall Equipment Effectiveness (OEE) measures how well a manufacturing
operation is utilized compared to its full potential, measuring the percentage
of manufacturing time that is truly productive. This includes measuring the
speed at which the parts are produced (the performance), the quality of the
parts which are being manufactured (the quality), and the number of
interruptions to the manufacturing process (the availability). A perfect score
of 100% indicates that all the manufactured parts are good, they were produced
at maximum speed, and they were produced without interruption. Measuring these
aspects is a best practice for any manufacturing operation. Bringing OEE onto
the edge allows businesses to measure their Key Performance Indicators (KPIs)
easily and pivot their business with agility.
Edge-enabled
machines provide the data to give you insight and foresight into manufacturing
or the utility floor near your asset; you can take preventative corrective
action, even when the opportunity to prevent problems is very small.
CENTRALIZED CLOUD ANALYTICS STUMBLE
IN CRITICAL MANUFACTURING AREAS
Many
enterprises have adopted cloud first strategies. They have married their
workflows to cloud platforms to connect low cost, elastic global infrastructure
with rich device data. Initially, this approach allowed these organizations to
accelerate deployment of connected products and industrial internet efforts.
However, as they scale their digital transformation efforts, cloud-only approaches
limit growth because of delays to transmit data from devices to the cloud and
to transmit analytics from the cloud back to devices. IoT use cases on the
manufacturing floor often have unique, real-time data analysis needs. It is not
always practical, economical, or even lawful to move, store, and analyze IoT
data on a core cloud infrastructure. Many manufacturing professionals recognize
these limitations. They cite security concerns, the high costs of repeatedly
accessing data, reduced data accessibility, and the subsequently reduced
ability to make real-time decisions as the top downfalls of analyzing IoT data
in the cloud. The solution to these latency issues is to continue to scale businesses
using edge computing.
Edge
computing solutions, which converge hardware and software into increasingly
smaller devices which run smarter analytics onboard, enable real-time decisions
and insights. Momentum for edge IoT solution deployment is increasing at a
faster rate in the manufacturing, utility, and building use cases.
Edge
computing often incorporates Machine Learning (ML) and Artificial Intelligence
(AI) technologies. These techniques make the calculations performed on the edge
even more efficient. That way, the system does not require the help of human
operators to identify data irregularities which may point to a potential
problem developing with a machine or system. The AI can flag anomalies in an
actionable way so that machine breakdown can be prevented. Another use case for
AI on the edge is the detection of defective parts in a manufacturing
operation. This technology can be used to guide part inspectors or to identify
patterns which may lead to the production of defective parts.
However, the
accuracy of the models which AI uses for these purposes may degrade over
time—this is where Machine Learning (ML) becomes important. ML is incorporated
into the process to create a closed loop in which the computer contains
supervisory programming which observes the accuracy of the AI model over time
by analyzing data drifts within the AI model.
All these
technologies are available through CIMCON’s versatile iEdge 360 Edge Computing Platform. This system is designed to
integrate both wireless and wired sensors into the IoT network. The iEdge 360
platform compiles, validates, quality-checks, and processes the data. It
efficiently uses bandwidth to store and forward data, creating a sensor data
lake. The data collected is also used to nimbly detect anomalies in machine
operation on the edge.
CIMCON iEdge 360 Edge Computing
Platform enables multiple use cases:
The platform
gives users insights into machine operation and process data which would
otherwise be unavailable. This includes automated KPI calculation, derived
statistical data, and long-term trend analysis. This gives operators the
process visibility they need for situational awareness, energy analytics, and
real-time detection of anomalies. This helps you stay on top of your
operational goals, efficiency objectives, and machine health status in a simple
package which keeps your business running smoothly,
When an
anomaly is detected, the iEdge 360 platform provides machine operators with the
tools to determine the cause. Drill-down widgets and rule-based alerts couple
with Machine Learning technology to enable easy machine diagnostics. Key
Performance Indicator (KPI) calculations and machine fault mode diagnosis take
the raw data collected by the system and turn it into actionable intelligence.
Rather than allowing you to get lost in the sea of big data, the iEdge 360
platform pinpoints the important nuggets of information and presents it to you
in an easy-to-understand manner. This allows operators to quickly fix the issue
and get critical processes running again. Overall, these features reduce
operational downtime, repair costs, and labor costs while increasing energy
efficiency and production output.
In addition
to the other actionable, useful features, CIMCON’s iEdge 360 Edge Computing
Platform contains built-in video analytics capabilities. A plug and play
architecture is included out of the box which makes including video analytics
into your system simple. AI and ML technologies built into the platform use
video data to detect equipment failure conditions, triggering one of several
custom workflows based on the events in the video. This feature allows you to
monitor your business for production line efficiency, item counting on a
conveyor belt, and even theft prevention.
The iEdge
360 IoT Platform is designed for collecting sensor data at scale and
transforming that data into actionable intelligence using its powerful on-board
processor as well as its high-level, general-purpose programming language; it
uses Python and Flowchart programming, among other easy-to-use features. In
this way, the platform is extremely user-friendly—it is not designed to be difficult
to understand or obscure like some of its competitors. Rather, it is
streamlined to make your business operate at peak efficiency. Its powerful quad
core processor with modern microservice based architecture allows Edge AI/ML
Algorithms to transform data into actionable insight at the edge.
Additionally,
edge hardware moves the computing resources closer to the data source.
Therefore, it compiles and filters data rapidly, alleviating bandwidth
challenges. The platform pushes intelligence, data processing, analytics, and
communication capabilities close to the locations of the sensors which gather
the data.
Do you want
to improve your bandwidth utilization while simultaneously generating insights
into machine health, operational efficiency, and Key Performance Indicators?
Are you ready to move into the world of the Internet of Things? We can walk
your business through its digital transformation smoothly and efficiently. We
will enable you to meet your KPIs while reducing operational downtime and utilizing
the data you generate.
Would you
like to know more about CIMCON iEdge 360 Platform solutions? Just send a
message to our IoT application engineering team, and we will be happy to answer
all your questions and provide product demonstrations.
Tags:
Smart Manufacturing Industry
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