EOT Enables Energy Operators to Create Enterprise Wide Cloud Historian With the Release of Asset Model Explorer, September 12, 2022
Press Release
EOT Enables Energy Operators to
Create Enterprise Wide Cloud Historian
With the Release of Asset Model Explorer
Operators can now establish an Enterprise Digital Twin and monitor industrial assets across siloed, incompatible SCADA systems and historians
HOUSTON, Texas, Sep. 12, 2022 – Today, Embassy of Things (EOT), an industrial software company providing an enterprise data integration and curation platform for industrial digital twins and AI/ML solutions released Asset Model Explorer (AME), a system specifically designed to navigate and explore diverse and often incompatible operational data sources. Under the hood, AME creates the single source of truth and enterprise cloud historian for distributed and siloed SCADA and historian systems.
“EOT is delivering the world’s first cloud-based historian and asset management solution that spans across many sites and different SCADA and data historians. Asset Model Explorer not only unlocks siloed operational data but enables industrial companies to grow faster by onboarding newly acquired assets,” said Matt Oberdorfer, CEO and President of EOT. “Asset hierarchies are now navigable by field engineers and board executives alike.”
Xecta’s builds Network Optimization Products for the Energy Industry, our cloud-first digital platform utilizes a fusion of AI and domain physics, to solve complex engineering problems related to operational optimization at scale that enable energy operators to create extraordinary operational and capital efficiency. “Our customers want to create immediate value to their P&L by augmenting their field personnel with more accurate and frequent recommendation, Xecta uses automated engines that self-calibrate to real-time performance data to optimize assets as a whole,” says Sanjay Paranji, CEO at Xecta Digital Labs (https://xecta.com), “We are excited about EOT’s Asset Model Explorer. In the journey of converging IT and OT, AME enables our customers to browse and query the cognitive twin using a hybrid edge to cloud architecture to make data driven decisions.”
AME is the world’s first industrial IOT platform that enables efficient enterprise wide queries and complex searches on enterprise-wide asset hierarchies and trees maintained at the edge. It provides energy companies the ability to explore assets at granular level, from disparate sources, in near real time and enables a data driven decision making approach with a unified platform across the enterprise. Learn more about AME: https://eot.one/asset-model-explorer/
About EOT
Embassy of Things, Inc. (EOT) provides Twin Talk a secure, scalable, and intelligent Data Integration and Curation Platform designed to liberate operational data from historians and SCADA systems for cloud analytics and using insights for enabling self-optimizing industrial plants. EOT is helping customers in energy, manufacturing and transportation to capitalize on production, asset and resource optimization, and cost savings by enabling event-driven, real-time architectures in the cloud and operational intelligence at the edge. EOT’s customers represent more than $160 billion in revenue, $45 billion in fixed assets and 60,000 employees. For more information, visit: https://www.embassyofthings.com or come see us at the 8th IoT Oil & Gas Event, booth 38/39.
Press Kit:
EOT’s Industrial Data Integration Platform for Digital Twins and Enterprise Cloud Historians Launches on AWS Marketplace, March 7, 2022
Press Release
EOT’s Industrial Data Integration Platform for Digital Twins and Enterprise Cloud Historians Launches on AWS Marketplace
New Offering Streamlines Deployment and Implementation of One-Day-Data-Lake, analytics, and AI/ML solutions for energy, manufacturing, and transportation industry
SAN DIEGO, Calif., Mar. 7, 2022 – Embassy of Things, Inc. (“EOT”) the leading Industrial Internet of Things company and maker of Twin Talk, an industrial data integration platform, today announced the availability of its platform in AWS Marketplace, an online software store that helps customers find, buy, and immediately start using the software and services that run on Amazon Web Services (AWS).
As a result, AWS customers will be able to access the power and capabilities of EOT’s Twin Talk platform and implement industrial data lakes and enterprise cloud historians which span across siloed SCADA systems and legacy on-premise historians.
Twin Talk enables the creation of enterprise cloud historians which provide the single source of truths for customers’ production calculation data. Enterprise cloud historians allow operational and business users to navigate through corporate industrial assets in a single, unified hierarchy. This enables operators to compare, analyze, mine, and predict similar corporate industrial assets across all industrial assets and apply modern data science to create analytical and machine learning models that can be used to reduce downtimes, increase production, and decrease environmental impacts.
EOT and AWS have been collaborating to build deep technology integrations, as well as their sales and marketing teams to serve new enterprise customers.
EOT’s platform utilizes AWS to host its data, providing security and easy integration services. Specifically, EOT uses a variety of AWS services, including Amazon Elastic Computing (Amazon EC2), AWS S3, AWS SiteWise, and AWS Athena. Successful customer implementations have resulted in consolidation and integration of the disparate systems typically found in the field into a single cloud historian on AWS.
“Our customers use industrial data lakes and enterprise cloud historians to unlock and maximize the hidden business value of industrial data,” said EOT’s Chief Executive Officer and Co-Founder, Matt Oberdorfer. “We’re seeing EOT’s Twin Talk being used as the fabric closing the gap between OT and IT, supercharging use cases across the board from production monitoring, anomaly detection, and predictive maintenance with unmatched AI and deep learning analytics solutions.”
For more information, please visit EOT’s product listings in the AWS Marketplace: https://aws.amazon.com/marketplace/pp/prodview-znr4ybfqzv4la or visit https://www.embassyofthings.com.
About EOT
Embassy of Things Inc. (EOT) provides a secure, scalable, and intelligent ETL++ and Industrial Data Integration Platform designed to liberate operational data from legacy, on-premise historians, and SCADA systems for implementing industrial data lakes and enterprise cloud historians. EOT is helping customers in energy, manufacturing, and transportation to capitalize on production, asset, and resource optimization, and cost savings by enabling event-driven, real-time architectures in the cloud and operational intelligence at the edge. For more information, visit: https://www.embassyofthings.com.
EOT Delivers Cost Reductions to Upstream and Midstream Operation Centers with Closed-Loop AI, May 26, 2021
Press Release
EOT Delivers Cost Reductions to Upstream and Midstream Operation Centers with Closed-Loop AI
May 26, 2021
SAN DIEGO – (Business Wire) – Today, Embassy of Things, Inc. (EOT) announced the full release of AI Edge Controller which enables the use of trained machine learning models to perform real-time predictions and anomaly detection at the edge of operation centers and uses closed-loop, event-response operational action to instantly avoid expensive downtime and increase production output.
“EOT is excited to deliver an essential breakthrough for making self-optimizing industrial plants a reality by closing the loop of analyzing operational data in the cloud and operationalizing AI insights at the edge in collaboration with AWS, Xecta, TensorIoT and CTG,” comments Matt Oberdorfer, CEO and President of EOT.
During the training phase of machine learning (ML) models, large data sets of sensors are needed to optimize their prediction quality. This requires significant compute and storage power which is only available in the cloud. However, the need for anomaly detection and self-optimization occurs at the operational edge where there is no compute, storage, or internet connection. EOT’s AI Edge Controller’s patent-pending technology serves as a bridge by delivering the rather small, trained ML models from the cloud to the edge. This is where EOT’s Twin Talk’s Operational Insight Engine streams real-time operational data through the trained ML models to instantly detect equipment abnormalities, diagnose issues, reduce false alerts, self-optimize production and avoid expensive downtime by acting before machine failures occur. To get started with AI Edge Controller and TwinTalk, visit: https://eot.one/ai-edge-controller.
“We are collaborating with EOT to leverage AI Edge Controller and TwinTalk as a part of the AWS production operations solution suite to support customers in their efforts to add incremental production, visualize and minimize rogue emissions and reduce lease operating expenses,” says Sid Bhattacharya, Worldwide Head of Energy Solutions at Amazon Web Services. “EOT’s solution is a significant leap in operational technology modernization – it helps customers make business-critical decisions real-time as opposed to the traditional reactive approach.” AWS’s Production Monitoring and Surveillance solution is a Production Data Lake and Edge Software that liberates operational data from legacy SCADA and Historian infrastructure to deliver real-time production and equipment surveillance, GHG emissions monitoring, and predictive maintenance (https://aws.amazon.com/energy/solutions/production-monitoring-surveillance/).
TensorIoT is an EOT Strategic Alliance Partner delivering complete end-to-end products and solutions in IoT, data engineering, machine learning, and artificial intelligence (https://www.tensoriot.com/). “TensorIoT builds AWS Cloud-based solutions to derive valuable business insights from on-premises historian and SCADA system data,” said Ravikumar Raghunathan, CEO of TensorIoT. “Working with EOT and integrating TwinTalk with our solutions expedites the process of bringing value to our customers. We are excited about AI Edge Controller’s capability to leverage machine learning at the operator on-premise level.”
CTG delivers digital solutions across the oil and gas industry that accelerate digital transformation (https://www.ctg.com/industries/oil-and-gas/). “CTG’s partnership with Embassy of Things reflects our ongoing commitment to deliver digital transformation to the energy sector,” said Barbara Locklair, Managing Director of CTG’s Energy Solutions. “CTG’s deep energy domain experience and IT technical expertise combined with EOT’s technologies such as AI Edge Controller and TwinTalk is transforming the energy sector by reducing the carbon footprint, lowering costs, and increasing production.”
Xecta’s cloud-first digital platform utilizes a fusion of AI, domain physics, and modern computing to solve complex engineering problems related to operational optimization at scale that enable energy operators to create extraordinary operational and capital efficiency. “Our customers want to use the next generation of autonomous models that self-calibrate to real-time performance data from a multitude of sensory inputs to optimize asset performance as a whole,” says Sanjay Paranji, CEO at Xecta Digital Labs (https://xecta.com). “We are excited about EOT’s AI Edge Controller and Twin Talk’s Insight Engine. It’s a true milestone for implementing a cognitive twin, because it enables to automate, manage, deploy and use of cloud-trained AI models in closed-loop operation centers.”
About Embassy of Things
Embassy of Things Inc. (EOT) provides secure, scalable, and intelligent ETL++ and Operational Data Management Systems designed to liberate operational data from historians and SCADA systems for cloud analytics and using insights for enabling self-optimizing industrial plants. EOT is helping customers in energy, manufacturing and transportation to capitalize on production, asset and resource optimization and cost savings by enabling event-driven, real-time architectures in the cloud and operational intelligence at the edge. For more information, visit: https://www.embassyofthings.com.
EOT Announces Support for OSDU Real-time Platform, May 17, 2021
EOT announced that Twin Talk will be ingesting operational data from historians, SCADA systems, and OPC UA servers into the OSDU Real-Time Platform via a new Twin Talk Apache Kafka connector. More details https://eot.one/osdu-platform/
EOT invited to join AWS ISV Accelerate Partner, April 28, 2021
EOT has joined the AWS ISV Accelerate Program
The AWS ISV Accelerate Program is a co-sell program for AWS Partners who provide software solutions that run on or integrate with AWS.
Three ways to learn more about EOT:
Partnering with AWS on the global Lookout for Equipment Launch, April 8, 2021
AWS Announces General Availability of Amazon Lookout for Equipment
Amazon Lookout for Equipment enables industrial customers to use machine learning to fully leverage their investment in equipment sensors to perform large-scale predictive maintenance across all of their industrial sites
Siemens Energy, Cepsa, Embassy of Things, RoviSys, Seeq, and TensorIoT among customers and partners using Lookout for Equipment
SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced the general availability of Amazon Lookout for Equipment, a new service that uses AWS-developed machine learning models to help customers perform predictive maintenance on the equipment in their facilities. Amazon Lookout for Equipment ingests sensor data from a customer’s industrial equipment (e.g. pressure, flow rate, RPMs, temperature, and power), and then it trains a unique machine learning model to accurately predict early warning signs of machine failure or suboptimal performance using real-time data streams from the customer’s equipment. With Amazon Lookout for Equipment, customers can detect equipment abnormalities with speed and precision, quickly diagnose issues, reduce false alerts, and avoid expensive downtime by taking action before machine failures occur. There are no up-front commitments or minimum fees with Amazon Lookout for Equipment, and customers pay for the amount of data ingested, the compute hours used to train a custom model, and the number of inference-hours used. To get started with Amazon Lookout for Equipment, visit: https://aws.amazon.com/lookout-for-equipment.
Industrial companies are constantly working to improve operational efficiency by avoiding unplanned downtime due to equipment failure. Over time, many of these companies have invested heavily in physical sensors, data connectivity, data storage, and dashboards to monitor their equipment health and performance. To analyze the data from their equipment, most companies typically use simple rules or modeling approaches to identify issues based on past performance. However, the rudimentary nature of these approaches often leads customers to identify issues after it is too late to take action, or receive false alarms based on misdiagnosed issues that require unnecessary and timely inspection. Instead, customers want to detect general operating conditions or failure types (e.g. high temperature due to friction) along with complex equipment failures (e.g. a failing pump indicated by high vibration and RPMs but low flow rates) that can only be derived by modeling the unique relationships between sensors. Today, advances in machine learning techniques have made it possible to quickly identify anomalies and learn the unique relationships between each piece of equipment’s historical data. However, most companies lack the expertise to build and scale custom machine learning models across their different industrial equipment. As a result, companies often fail to fully leverage their investment in sensors and data infrastructure, causing them to miss out on key actionable insights that could help them better manage their critical equipment’s health and performance.
With Amazon Lookout for Equipment, industrial and manufacturing customers can now quickly and easily build a predictive maintenance solution for an entire facility or across multiple locations. To get started, customers upload their sensor data (e.g. pressure, flow rate, RPMs, temperature, and power) to Amazon Simple Storage Service (S3) and provide the relevant S3 bucket location to Amazon Lookout for Equipment. The service will automatically analyze the data, assess normal or healthy patterns, and build a machine learning model that is tailored to the customer’s environment. Amazon Lookout for Equipment will then use the custom-built machine learning model to analyze incoming sensor data and identify early warning signs of machine failure or malfunction. For each alert, the service will specify which sensors are indicating an issue and measure the magnitude of its impact on the detected event. For example, if Amazon Lookout for Equipment detected an issue on a pump with 50 sensors, the service could show which five sensors indicate an issue on a specific motor, and relate that issue to the motor power current and temperature. This allows customers to identify the issue, diagnose the problem, prioritize needed actions, and perform precision maintenance before issues happen—saving them money and improving productivity by preventing down time. Amazon Lookout for Equipment allows customers to get more value from their existing sensors, and it helps them make timely decisions that can materially improve operational efficiency. Amazon Lookout for Equipment is available directly via the AWS console as well through supporting partners in the AWS Partner Network. The service is available today in US East (N. Virginia), EU (Ireland), and Asia Pacific (Seoul), with availability in additional regions in the coming months.
In addition to Amazon Lookout for Equipment, AWS offers industrial and manufacturing customers the broadest range of cloud-to-edge industrial machine learning services, including Amazon Monitron (for predictive maintenance using an end-to-end solution comprised of sensors, gateways, and a machine learning service), Amazon Lookout for Vision (for visual anomaly detection using computer vision models in the cloud), and AWS Panorama (for visual inspection using an Appliance and Software Development Kit that brings computer vision models to on-premises cameras).
“Many industrial and manufacturing companies have heavily invested in physical sensors and other technology with the aim of improving the maintenance of their equipment. But even with this gear in place, companies are not in a position to deploy machine learning models on top of the reams of data due to a lack of resources and the scarcity of data scientists. As a result, they miss out on critical insights and actionable findings that would help them better manage their operations,” said Swami Sivasubramanian, VP Amazon Machine Learning, AWS. “Today, we’re excited to announce the general availability of Amazon Lookout for Equipment, a new service that enables customers to benefit from custom machine learning models that are built for their specific environment to quickly and easily identify abnormal machine behavior—so that they can take action to avoid the impact and expense of equipment downtime.”
Siemens Energy offers products, solutions, and services across the entire energy value chain to support its customers on their way to a more sustainable future – no matter how far along the journey they are. “Siemens Energy works with our customers to improve performance, reliability, and safety through our existing business lines enhanced with digital service solutions. Digitalization is a key driver for a sustainable energy future,” said Amogh Bhonde, senior vice president digital solutions at Siemens Energy. “With Amazon Lookout for Equipment, we see an opportunity to combine AWS machine learning with Siemens Energy subject matter expertise to give improved visibility into the systems and equipment across the entirety of a customer’s operation. Amazon Lookout for Equipment’s automated machine learning workflow makes it easy to build and deploy models across a variety of assets types with no data science knowledge required. Siemens Energy values AWS as a trusted partner accelerating our continued development of the Omnivise suite of digital solutions.”
Cepsa is a global energy and chemical company operating end-to-end in every stage of the oil and gas value chain. Cepsa also manufactures products from raw materials of plant origin and is driving a new strategy to become a reference in the energy transition. “At Cepsa, digital transformation is focused on people. In that regard, our professionals are the engine behind our transformation. With Amazon Lookout for Equipment, we are bringing machine learning insights to the experts that know the equipment best—reliability and maintenance engineers—allowing them to make more informed decisions to drive higher uptime and lower operational costs,” said Alberto Gascón, head of advanced analytics at Cepsa. “Solutions like predictive maintenance for equipment traditionally involve manual and complex data science such as choosing the right algorithms and parameters, but Amazon Lookout for Equipment automates these processes so that engineers can focus on solving the most critical challenges that impact their business.”
Embassy of Things (EOT) is the creator of Twin Talk, a secure and scalable ETL++ Data Delivery System designed to tap into the unrealized value hidden within operational data from SCADA systems and historians and enable industrial operating companies to leverage the power of cloud-based data analytics, machine learning, and AI. “Using predictive analytics and anomaly detection for not just one, but across all production sites is the key that enables our customers to achieve the highest level of production optimizations as well as cost and emission reductions. Our Twin Talk System liberates operational data to enable cloud-based, event-driven real-time architectures for Amazon Cloud Services like IoT SiteWise and S3,” said Matt Oberdorfer, CEO of Embassy of Things. “We are leveraging Amazon Lookout for Equipment to our suite of solutions which enables an automated machine learning process that improves the accuracy of detecting the most meaningful insights and enables insights to action faster. Lookout for Equipment is a true game-changer because it puts AI in the hands of maintenance engineers by abstracting away traditionally data-science-heavy steps being scalable effectively across assets.” Continue to read the full AWS press release: Here
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Case Study with BPX and AWS: bpx energy Transforms Production Operations Through AWS Energy Industry Solutions, March 31, 2021
bpx energy Transforms Production Operations Through AWS Energy Industry Solutions
Solution overview
The following reference architecture shows the overall solution, with the Industrial Machine Connectivity (IMC) highlighted in orange below.
bpx energy Production Monitoring and Surveillance Reference architecture
IMC on AWS helps energy customers and partners accelerate digital transformation. It brings data from Industrial Internet of Things (IIoT) assets to AWS in a structured way. Customers can extract insights from their OT data, increase efficiency, and reduce operational costs. IMC is for developers, regional and global systems integrators (SI), independent software vendors (ISV), and original equipment manufacturers (OEM) who want to generate business value from an IIoT architecture. ISV partners provide edge software and cloud native applications that can extract data from OT data stores to generate insights. SI partners bring implementation experience to customers and further develop custom applications to meet their unique needs.
During bpx energy implementation, we worked with partner solutions and teams to unlock data from proprietary applications and data silos. The cloud native solution integrated OT and IT for value-added analytical use cases. Data eventually lands in the Industrial Data Lake (IDL), shown on the right half of the architecture. The IDL implements a Lake House architecture on AWS, using an “inside-out” approach, where data in the data lake is ingested into purpose-built data stores for analytics. For example, the IDL implementation uses Amazon Timestream, a time series database that enables customers to derive deeper insights from their industrial data through time series analytics using SQL.
Amazon Timestream is a purpose-built managed time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day. It saves time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream also has built-in time series analytics functions, helping customers identify data trends and patterns in near real-time. bpx energy uses Timestream to store time series data from IoT devices and from packaged OT software solutions.
Implementation partner teams included Embassy of Things (EoT) and TensorIoT. EoT deployed their ETL++ Operational Data Delivery Software System “TwinTalk” at bpx energy. TwinTalk extracts raw data tag values from the source SCADA system, enhances it with contextual meta data, and delivers it to AWS native services such as AWS IoT SiteWise and Amazon S3 via data pipelines in near real-time. Continue to read the full AWS Blog: Here
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Case Study presented at OSIsoft Houston Conference
“EMPOWER YOUR ANALYTICS WITH OPERATIONS DATA”
October 8, 2019
Customer Case Study: Oxy – PI Enabled Data Pipelines for Cloud Analytics
Co-presentation with Occidental Petroleum Corporation at OSIsoft Houston Conference, October 8, 2019
In this case study, leveraging PI AF and creating an efficient data pipeline, enabled by TwinTalk, feeds an advanced data analytics system that detects anomalies. The anomaly findings reduce downtime for offshore oil and gas platform equipment. Housed in Google Cloud Platform (GCP), the system ingests more than ten thousand rows of interpolated data per minute from the OSIsoft PI Archive data sources. The PI System coupled with TwinTalk helps lower operational costs and provides and easier self-service mechanism. This enables faster data-driven outcomes by lessening complex custom coded data pipelines. Also demonstrated, is how analytical results and operations data are combined to allow informed decisions. Download the presentation and learn more about TwinTalk here https://eot.one/wp-content/uploads/2020/01/OSIsoftPIEnabledDataPipelinesforCloudAnalytics.pdf.
Google Cloud Partner
April 15, 2019
It’s official. Embassy of Things is now official Google Cloud Partner
PRESS RELEASE
October 9, 2018
EOT Launches Secure Access to Operational Data to Accelerate Digital Transformation
Secure, Simple and Scalable Access for Analytics, Remote Monitoring Solutions to Operational Sensor Data Sources
SAN DIEGO – October 9, 2018 – Embassy of Things Inc. (EOT), a pioneer in cloud access security management, today announced TwinTalk, an industrial Identity Access Management (IAM) and software-defined Demilitarized Zone (DMZ) solution. TwinTalk is removing the burden associated with testing, validating and adopting modern machine learning (ML), and artificial intelligence (AI) technologies for industrial operating companies in oil and gas, transportation, energy and manufacturing and allows to securely tap into the unrealized value hidden within operational technologies (OT).
The vast potential of AI, ML and data science to reduce costs and increase productivity has encouraged leaders around the world to make significant investments in the latest industrial IoT (IIoT) and automation platforms. Yet, nearly 70% of IoT initiatives stall or fail at the Proof of Concept stage because of cyber security concerns. TwinTalk enables the rapid provisioning of highly secure access channels between industrial plants, the cloud and enterprise IT apps, keeping the plant operations safe while delivering tangible business value from modern software solutions.
“Data science and AI are the future for smart industrial corporates, but the barrier to access operational data sources prevents them from reaping the benefits” said Matt Oberdorfer, CEO of Embassy of Things. “TwinTalk gives OT organizations a simple way to transmit operational sensor data to the cloud without any impact to plants operations.”
TwinTalk’s highly resilient “security bridges” translate complex data models of operational data sources (historians, Industrial Controls Systems, sensors, SCADA, etc.) into formats that can be directly processed by Business Intelligence (BI), Security Information and Event Management (SIEM), and big data analytics solutions.
“Digital transformation presents new security challenges that if not properly resolved will compromise the OT environment,” said Dave Lafferty, President of Scientific Technical Services, “EOT helps industry- leading companies to overcome the biggest challenges to provide the needed OT data to applications in the cloud without compromising the integrity of the OT network and its mission critical control loops.”
To learn more about TwinTalk, visit https://eot.one/twintalk.
About Embassy of Things Inc.
Embassy of Things Inc. (EOT) enables secure, simple and scalable access to operational data. Industrial operating companies use EOT’s market-leading solutions to solve their toughest identity, access and security challenges in their digital transformation journeys. For more information, visit: https://www.embassyofthings.com.