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CIMCON Digital

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. 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:

iData Platform Providers

IoT Edge Platform

Smart Manufacturing Industry 4.0

iCloud Software Solutions

 

CIMCON Digital

What is a Smart Factory?

A Smart factory uses technology to automatically share information digitally across the operation, including data from materials, people, and machines. Smart manufacturing relies on an integrated system consisting of simulation technologies, connected equipment, and collaboration tools.

 

There is no single technology that turns an analog factory into a Smart factory. However, there are many common technologies and traits that Smart factories share, and manufacturers that blend multiple technologies are the ones most likely to be considered “Smart factories.”

 

What are the benefits of a Smart Factory?

One of the most obvious benefits of a Smart factory is a major boost to efficiency. By removing inefficient human decision-making processes that can be not only slow, but also biased or simply incorrect, factories can produce more with less – less time, less material resources, fewer breakdowns, and scrap parts, etc.

 

Although many manufacturing employees worry that the influx of automation and other Smart technologies will put them out of the job, Smart manufacturing opens up opportunities for more, new, higher paying jobs that many people find more engaging and fulfilling. These highe rpaying jobs also attract young, new talent to the field with new insights and concepts to better the facility.

 

In the same context, Smart factories are breeding grounds for innovation. Because of the agility filled into the system by way of real-time analytics, there is more room to experiment, be creative, find solutions to new problems within the market, and test ideas at scale without spending unnecessary resources. Smart factories even see an uptick in customer satisfaction, because costs can go down, shipping times can go down, all the while quality and consistency go up.

 

Technology in a Smart Factory includes:

Industrial Internet of Things (IIoT)

This consists of small sensors and other hardware that are connected and communicate with one another. They might be used for asset management, energy reduction and lighting, or machine data collection, although there is a nearly unlimited number of use cases for IIoT in manufacturing.

 

Edge Computing Technology

Edge computing takes data coming from the factory floor and processes it close by, removing the wait time it can take to upload to the cloud, analyse, and redistribute info to the factory floor. Edge computing enables real-time analytics and ultra-fast decision-making using data, and is perfect for safety mechanisms, predictive maintenance, and similarly time-sensitive computing tasks.

 

Predictive Analytics and Machine Learning

In regards to manufacturing, as well as other businesses, machine learning and predictive analytics are one such use case for the collected data mentioned above. Data can be combined and used to fuel machine learning models that offer decision making insights from sets of information that can be too complex for humans to derive value from alone. Machine learning and predictive analytics can be

used to forecast demand, perform predictive and prescriptive maintenance on machines, spot openings and opportunities in the market, and much, much more. This is a very powerful feature of a Smart factory.

 

Automation

With smart, connected machines comes the opportunity for humans to step outside of the circle, and allow automation to step in. In many cases, machines are better able to handle tasks faster and more accurately than their human counterparts. Industrial automation releases these humans to focus on other complex cognitive tasks that are better suited for human minds than machine minds.

 

Big Data

When IIoT devices collect data, it has to go somewhere. Same for other data that manufacturers commonly collect like customer data, production data, supplier data, etc. Big data simply refers to these massive stores of information that manufacturers can pull from as well as ways to sort and manage this information for use with other tools and analytics software.

 

What else do Smart factories have in common?

Paperless: Because processes are digitised, there is no need for paper in a Smart factory. Everything is stored on the cloud or locally, in Smart format.

Real-Time Metrics: To operate with the type of efficiency expected from a Smart factory, manufacturers must have access to real-time metrics that let them adjust on the fly to ensure production goals and other company creativities are continuing to be met – no surprises.

Big Data Analytics: Having tonnes of data does no good unless it is processed and analysed. This type of analysis helps Smart factories make more informed decisions that are based on the numbers,

allowing them to spot trends, opportunities, problems, and areas to increase efficiencies.

 

Linked Stack: As mentioned, it’s less about having one technology or another and more about having a system of integrated technologies. This can include PLC info from the floor, merged with ERP data, merged with MES and SCADA data, etc. This exchange of information between

machines allows for quick, data-driven, machine-led decision making at all levels of the manufacturing process.

 

Tags:

IoT Edge Platform for Industries

iCloud Smart Manufacturing

Smart Manufacturing Process US

Smart Water Management Using IoT


CIMCON Digital

Just like you visit the doctor, your industrial equipment needs a system to monitor its health.

Last week, I visited a doctor for a throat infection. The doctor checked my body temperature, blood pressure, and oxygen saturation level. Then he asked me to open my mouth to see the severity of the infection in my throat. The doctor also asked me few questions to get more contexts about the infection: When did you consume any cold drinks or ice cream last week? Did you notice this infection after consuming them? Do you smoke? Also, the doctor looked through my medical history on his iPad. Finally, he was able to prescribe the right medicine to help me heal quickly.

 

After I left his clinic, I could not help but think about the analogy between how the doctor diagnosed my throat infection and how service engineers in an industrial environment analyse the health of industrial equipment and evaluate their performance. After all, I am an engineer to the core!

 

Here, my question to Industrial Engineers (our machine doctors) working at Original Machine Manufactures and industrial facilities is this: when your plant is talking, are you listening? Your plant, be it machines, pumps, motors, or any other equipment, continuously generates critical data that could provide an in-depth understanding of its health. This data could alert you to potential failures down the line. Data comes in various forms. It could be as simple as the sound or vibrations coming from your motors, the status of multiple lamps on your machines, or the amount of power consumed.

 

Doctors need sensor readings, such as body temperature, blood pressure, pulse, and oxygen saturation to observe human health. You also need a 360° view of your plant and equipment. After all, you are responsible for the health of hundreds of complex machines in your plant. You also need machine sensor data history in various contexts: mechanical health, efficiency, and motor health to diagnose machine faults. With many sensors already installed on the machine, your machine “patient” can provide feedback about its health in real-time 24/7 if it is connected to intelligent anomaly detection and diagnostics platform. Indeed, you can prevent machine breakdown. Repairs can also be less expensive if you prescribe work orders with the correct actionable information.

 

CIMCON’s digital end-to-end solution uses iEdge 360 intelligent platforms for machine health monitoring, fault detection, and diagnostics, providing you with actionable insight on machine health and performance in 3 simple steps.

 

How the solution works:

Outcome: Machine Health Key Performance Monitoring (KPI’s)

iEdge provides role-based and KPI-based visualization of data in the enterprise and machine hierarchy view. This mode of data visualization enables fast, accessible analysis. That way, there is more time for the diagnosis and repair of machine faults.

 

Just like with your health, early, specific, and precise detection of issues is required to keep your plant healthy.

 

Don’t you think real-time actionable information is vital? Implementation of a Predictive Maintenance solution is secure and straightforward with CIMCON’s iEdge 360 platform. Our engineers can get you monitoring your machine’s health on your computer or mobile device within a few hours. Comment below or email us for a detailed demonstration.

 

Tags:

Digital Transformation

IoT Edge Platform

Smart Manufacturing

 

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