EKATRA provides Realtime digital twins for contextual and situational analysis of complex industrial process such as power-generating plants. The demo shows a smart predictive maintenance scenario addressed.
EKATRA provides Realtime digital twins for contextual and situational analysis of complex industrial process such as power-generating plants. The demo shows a smart predictive maintenance scenario addressed.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
Integrating AI into IoT networks is becoming a prerequisite for success in today’s data-driven digital ecosystems. The only way to keep up with IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT. Join this webinar to understand how IoT and AI may work together.
EKATRA provides Realtime digital twins for contextual and situational analysis of complex industrial process such as power-generating plants. The demo shows a smart predictive maintenance scenario addressed.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
Integrating AI into IoT networks is becoming a prerequisite for success in today’s data-driven digital ecosystems. The only way to keep up with IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT. Join this webinar to understand how IoT and AI may work together.
Big Data for Big Power: How smart is the grid if the infrastructure is stupid?OReillyStrata
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Industrial Internet of things has continued to create the buzz, one can’t deny that it is the one of the fastest emerging technology with humpty amount of Data getting captured daily and with every industry demanding optimisation, analytics, automations and valuable insights Industrial Internet of things is something which the need of the day.
Session about "Microsoft and Internet of Things" at #NuvolaRosa - Naples (Italy) 12 May 2016
http://www.nuvolarosa.eu/corsi-napoli/
Main Themes:
Internet of Things
Windows 10 IoT Core
Windows Azure Services
Windows IoT Hub
Stream Analytics
Azure Blob Storage
Power Bi
From the Testers: Measuring for Energy Efficiency and Energy LabelingIRS srl
New energy efficiency standards and labeling are enforced throughout the world. At this session, hear appliance and HVAC test system experts share why you need to pay special attention to sensor selection and system integration. The NI modular platform can lower complexity, reduce development time, and add machine learning to the task.
Hyperthings is an innovative provider of “Internet of Things” based products and solutions. Interoperability lies at the heart of everything we do and we aim to be the organization which develops solutions to provide more straight forward interaction between physical and virtual world
The combination of the Internet of Things and augmented reality not only allows organizations to collect data through sensor networks and generate actionable insights but also delivers these insights to the right people at the right time, with the greatest impact.
The impact of emerging IoT Technology and BigData. This is the slide presentation I did at the http://globalbigdatabootcamp.com/speakers/sanjay-sabnis/
Java in the Air: A Case Study for Java-based Environment Monitoring StationsEurotech
Eurotech and Oracle Joint presentation at JavaOne 2014 that introduces:
IoT Present and Challenges
Java, OSGi and Eclipse Kura: IoT Gateway Services
Embedded Data Stream: Edge Analytics
Use Case: Environment Monitoring Stations
Big Data for Big Power: How smart is the grid if the infrastructure is stupid?OReillyStrata
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.
Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.
Industrial Internet of things has continued to create the buzz, one can’t deny that it is the one of the fastest emerging technology with humpty amount of Data getting captured daily and with every industry demanding optimisation, analytics, automations and valuable insights Industrial Internet of things is something which the need of the day.
Session about "Microsoft and Internet of Things" at #NuvolaRosa - Naples (Italy) 12 May 2016
http://www.nuvolarosa.eu/corsi-napoli/
Main Themes:
Internet of Things
Windows 10 IoT Core
Windows Azure Services
Windows IoT Hub
Stream Analytics
Azure Blob Storage
Power Bi
From the Testers: Measuring for Energy Efficiency and Energy LabelingIRS srl
New energy efficiency standards and labeling are enforced throughout the world. At this session, hear appliance and HVAC test system experts share why you need to pay special attention to sensor selection and system integration. The NI modular platform can lower complexity, reduce development time, and add machine learning to the task.
Hyperthings is an innovative provider of “Internet of Things” based products and solutions. Interoperability lies at the heart of everything we do and we aim to be the organization which develops solutions to provide more straight forward interaction between physical and virtual world
The combination of the Internet of Things and augmented reality not only allows organizations to collect data through sensor networks and generate actionable insights but also delivers these insights to the right people at the right time, with the greatest impact.
The impact of emerging IoT Technology and BigData. This is the slide presentation I did at the http://globalbigdatabootcamp.com/speakers/sanjay-sabnis/
Java in the Air: A Case Study for Java-based Environment Monitoring StationsEurotech
Eurotech and Oracle Joint presentation at JavaOne 2014 that introduces:
IoT Present and Challenges
Java, OSGi and Eclipse Kura: IoT Gateway Services
Embedded Data Stream: Edge Analytics
Use Case: Environment Monitoring Stations
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Mic...BAINIDA
Data Analytics in your IoT SolutionFukiat Julnual, Technical Evangelist, Microsoft (Thailand) Limited ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
IoT - Retour d'expérience de projets clients dans le domaine IoT. Michael Epprecht, Technical Specialist in the Global Black Belt IoT Team at Microsoft. Conférence donnée dans le cadre du Swiss Data Forum, du 24 novembre 2015 à Lausanne
Get exclusive insights on IoT technology that has the potential to accelerate your business and give you the necessary agility to keep up with the pace of business. Join us and learn about the current and future state of the IoT landscape and what it takes to be successful in IoT. Gain insights from customer stories and discover how to get started building successful IoT solutions with Microsoft Azure.
Discover the webcast: https://bit.ly/2U1N8iI
The evolution of machine learning and IoT have made it possible for manufacturers to build more effective applications for predictive maintenance than ever before. Despite the huge potential that machine learning offers for predictive maintenance, it's challenging to build solutions that can handle the speed of IoT data streams and the massively large datasets required to train models that can forecast rare events like mechanical failures. Solving these challenges requires knowledge about state-of-the-art dataware, such as MapR, and cluster computing frameworks, such as Spark, which give developers foundational APIs for consuming and transforming data into feature tables useful for machine learning.
Tiarrah Computing: The Next Generation of ComputingIJECEIAES
The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge.
Disoriented about all the Azure services in the IoT and Industrial IoT that you can use for building a modern Architecture on the Cloud and on the Edge? Well, this session aims to describe a reference architecture like Lambda and to map it to Azure services like Event Hubs, IoT Hubs just to mention a few. It also presents different approaches on how to handle communication from a more commercial devices to discrete manufacturing ones, with different standards like OPC UA. All those bricks will also help you to use already-build solutions like our Accelerators and IoT Central.
How to Use Artificial Intelligence to improve the profitability of restaurants.
1. Mini MBA on Customers Data Analysis
2. BUSINESS CUSTOMERS X-RAY Module
3. CUSTOMER CARE Module
4. MENU ENGINEERING Module
5.PERSONNEL DEVELOPMENT Module
6. EXPECTED ROI AND FINAL CONSIDERATIONS
Value Amplify Consulting Group, offers the opportunity to hire Chief AI Officers trained to lead your organization in the following services, roadmaps and create your AI Playbook
This Workshop Teaches Business Leaders How To Implement AI Technologies To Serve Customers Better Than Anybody Else.
AGENDA
Introduction to Artificial Intelligence
Extracting Value & Delivering Value
Predictive & Preventive maintenance
Marine market, Jet engines
How to prepare & implement AI Playbook
AI and Automation in the most valuable business decisions. Leveraging REJ (Rapid Economic Justification) to identify the best use of AI. Presentation from the Infosys AI Summit in Miami.
What is Bitcoin, Blockchain? . How do they work?
How automated trading robot BOT BitConnect increases profits.
Start using BIT at: https://bitconnect.co/?ref=Giuseppemasc
Keynote presentation at the HUBB Conference.
Adj Prof Mascarella clarifies terms, mechanisms and what is the roadmap to use innovation for new business.
What Is Machine Learning?
Where do we deploy machine learning and what software and cloud services are out there to support it?
What are the trends in deploying these systems and what are the benefits for IT?
Do you have a IoT Machine Learning Case Study in the Cloud?
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
EKATRA IoT Digital Twin Presentation at FOG World Congress
1. Adj. Prof. Giuseppe Mascarella
giuseppe@ektra.io
Alexey Klimenko
alexey@ektra.io
2. AGENDA
1. What Problem Is Ekatra FOG Trying To Solve
2. EKATRA Solution Architecture
3. The Digital Value Chain
3. The Challenges
1. How do you connected siloed
data and systems?
2. How to deliver data and insights
to the right decision point on
time?
3. How do we propel the digital
transformation for digital value
chain?
5. EKATRA OEE (Overall Equipment Effectiveness) Maximizer
Context>Situational Predictive Action
Asset Utilization
and
Maintenance • Avoid unexpected downtime
• Avoid over- and under- maintenance
• Operate Asset as a Service
• Take preemptive corrective actions
Overall
Equipment
Effectiveness
(OEE)
• Create New
Business
Models
Cost of
Incidents and
Maintenance
Intelligence
Reports
ERP, Maint. Data
Data
6. AGENDA
1. What Problem Is Ekatra FOG Trying To Solve
2. EKATRA Solution Architecture
3. FOG In The Digital Value Chain
7. EKATRA = Interconnected Nodes + Services
1. Ekatra Sensor Hub
Connect to sensors, equipment, PLCs,
legacy systems, etc.
Process data in real-time
Securely transfer data across nodes via
very efficient protocol
2. Ekatra Data
Management
Store and process time-series
data at massive scale
Manage digital twin metadata
3. Ekatra HMI
Unique UX & visualization for
digital twins, analytics and
dashboards
4. Ekatra ML
Create and run ML & AI models of
equipment and processes
5. Ekatra Business Apps
Marketplace of pre-built solutions:
predictive maintenance, etc.
6. Security & Orchestration
Tools to assemble, monitor and manage
Ekatra across all the nodes.
Flexible
deployment
Location and
context – aware
Inter-node secure
communication
Nodes can contain
different services
8. Ekatra Services Architecture
Built on Open Source Components
Business Apps
Marketplace
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torquent per conubia nostra.
03
HMI Dashboards
& Visualization
Sensor Hub Data
Management
Machine
Learning
& AI
Data
EKATRA IoT Intelligence
OPC UA
Industrial
Protocols
Legacy protocols
IoT Protocols
File exchange
Sensors
Azure, AWS,
Google Iot
Integration
Data Streaming
(RTDB)
Pub-sub events
RDF Metadata
Store
Time Series DB
Automation Engine
Ekatra ML
microservices
TensorFlow
Keras
Python
IEC-61131
External ML
Azure
AWS
Google
Role-based
dashboards
Digital Twin
models
Real-time web
screens
Screen generation
tools
Web, mobile,
embedded
Power BI support
Predictive
Maintenance
Business ROI
impact
Visualization and
control
Vertical industry
packages:
Energy
Machinery
Agriculture
etc.
Security and Orchestration
Sensors
Industrial Control systems
SCADA
Legacy Systems
Proprietary Protocols
Other data sources and apps
Local/edge deployment
Cellular
Internet
LAN/WAN
Satellite
Low-bandwidth relay
Modem connection
9. FOG IoT Demo Of Predictive Maintenance for
Power Plant Turbo-Power Generator
EKATRA Digital Twin
11. Business Problem
1. Prevent turbo generator failures
2. Know state of equipment, predict
it’s condition, maintenance
needs
3. Increase power output while
maintaining safe operations
17 turbo generators ~ 1000MWt each
across on sites hundreds of miles apart
Each day turbo generator is under
maintenance can cost up to $1MM
Solution
• Ekatra nodes at each location connected to TG control systems
• Predictive ML models to anticipate and detect failures
• Visualization and HMI
• Real-time data accumulation over thousands of parameters for
future diagnostics and model improvement
12. AGENDA
1. What Problem Is Ekatra FOG Trying To Solve
2. EKATRA Solution Architecture
3. The Digital Value Chain
13. EKATRA In Digital Transformation
Of The Value Chain
External
Home, Office
Outages , Quality SCADA
PLC
IoTsensors
IoTsensors
Plant Regulatory
Agency
PLC
IoTsensors
IoTsensors
SCADA
PLC
Headquarters
Emergency
Management
EKATRA S HUB
Customer
Experience
SMEs
HMI
HMI
HMI
HMI
HMI
HMI
14. Adj. Prof. Giuseppe Mascarella
giuseppe@ekatra.io
Contact Us For A Free:
IoT In The Value Chain
Workshop
Editor's Notes
The market is full of great promises about connected systems, IoT miracles and Big Data potential, it is all great and true, but let’s face reality - there so many businesses and systems that are simply not viable to modernize by overhauling.
How do you transition from disconnected legacy systems into an integrated digitized system that allows you to enable greater efficiency, new business models
Fast Data Ingestion 500K in near real time from SCADA, OPC UA, PLCs, GeoData, etc
Contextual Awareness. It creates intelligence out of the data, with better visualizations that enable operators in the Control Center to see changes in the system quickly.
Situational Intelligence. It follows and integrates geospatial and value chain data to give a precise understanding of specific events as they happen.Data from the Army Corps of Engineers, Sate Transportation, FIRE Dept, and other third parties allows a tactical analysis mapping integration.
Predictive Analytics. Ekatra finally take all of that real-time data and pull out patterns that signal approaching abnormal events, allowing for proactive responsiveness.
The market is full of great promises about connected systems, IoT miracles and Big Data potential, it is all great and true, but let’s face reality - there so many businesses and systems that are simply not viable to modernize by overhauling.
How do you transition from disconnected legacy systems into an integrated digitized system that allows you to enable greater efficiency, new business models