Amazon Lookout for Equipment
Successfully implementing predictive maintenance requires using the specific data collected from all of your machine sensors, under your unique operating conditions and then applying machine learning (ML) to enable highly accurate predictions. However, implementing an ML solution for your equipment can be difficult and time-consuming.
Amazon Lookout for Equipment analyzes the data from the sensors on your equipment (e.g. pressure in a generator, flow rate of a compressor, revolutions per minute of fans), to automatically train a machine learning model based on just your data, for your equipment – with no ML expertise required. Lookout for Equipment uses your unique ML model to analyze incoming sensor data in real-time and accurately identify early warning signs that could lead to machine failures. This means you can detect equipment abnormalities with speed and precision, quickly diagnose issues, take action to reduce expensive downtime, and reduce false alerts.
High accuracy results
Amazon Lookout for Equipment uses data from your existing sensors to build a custom ML model specific to your equipment. It handles data from up to 300 sensors in one model, along with historical logs, to give you accurate alerts when your equipment behaves abnormally.
Respond to issues faster and with precision
Amazon Lookout for Equipment automatically monitors your equipment and identifies any anomalies compared to healthy operation. Lookout for Equipment can then pinpoint the sensor or sensors indicating anomalies, enabling you to respond quickly.
Accelerate issue resolution
You can use data from Amazon Lookout for Equipment to set up automatic actions to be taken when anomalies are detected, such as filing a trouble ticket, or sending an automatic alarm that notifies you immediately of any issues. The data from Lookout for Equipment can be integrated into your existing monitoring software or you can use AWS IoT SiteWise to collect, store, organize and monitor data. Lookout for Equipment can also work with data from common industrial data systems such as OSIsoft.
Improve accuracy of alerts over time
Amazon Lookout for Equipment continuously improves model performance and accuracy of alerts by incorporating human review feedback on the anomalies detected and learning expected operational usage trends.
Amazon Lookout for Equipment can analyze sensor data from a broad range of industrial machinery.
Analyze sensor data from generators such as flow rates, temperature, pressure, and oxygen levels data to detect equipment shutdowns before they happen.
Analyze historical maintenance data and sensor data from compressors such as RPMs, inlet/outlet flow rates, temperature, pressure, and vibration to identify abnormal machinery behavior.
For equipment like wind turbines, you can analyze sensor data like power, pitch blade angle, RPMs, wind speed, and generator temperature to quickly identify issues before they occur.
Siemens Energy offers products, solutions, and services across the entire energy value chain. Siemens Energy supports 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. 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.”
Amogh Bhonde, Sr. Vice President Digital Solutions - Siemens Energy
Koch Ag & Energy Solutions
Koch Ag & Energy Solutions, LLC and its affiliates (KAES), subsidiaries of Koch Industries, are global providers of value-added solutions for the agriculture, energy and chemical markets.
"Today we are streaming thousands of sensors into AWS and want to leverage this data to help reduce maintenance and downtime for our operations. However, being able to model equipment behavior and diagnose issues from these sensors requires significant investment in time and resources, which has been a hurdle for scaling across our assets. Amazon Lookout for Equipment accelerates and reduces the heavy lift of generating insights through a highly scalable automated machine learning service that minimizes the touchpoints in a data science workflow. "
Dave Kroening, IT Leader - Koch Ag and Energy 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 & maintenance engineers -- allowing them to make more informed decisions to drive higher uptime and lower operational costs. 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."
Alberto Gascón, Head of Advanced Analytics - Cepsa
Hitachi Vantara, a wholly owned subsidiary of Hitachi Ltd, combines best-in-class IT infrastructure, data management, and analytics, with expertise in AI, industrial solutions, and digital transformation. Hitachi Vantara aims to become the world's preferred digital innovation partner by unlocking the "good" in data that benefits customers.
" Hitachi Vantara has direct experience with manufacturing products and software for heavy industries as well as operational technology (OT) domain knowledge, IT-OT system integration expertise, and IIoT solution capabilities. Hitachi Vantara's Lumada Manufacturing Insights solution plans to leverage Amazon Lookout for Equipment as part of its comprehensive industrial analytics workbench solution offering, where customers can streamline ML workflows, perform model management, and execute the models in the analytics run-time environment. This allows our customers to accelerate new applications of ML and improve total cost of ownership of asset health solutions."
Sid Verma, GM Manufacturing Practice - Hitachi Vantara
Seeq is an advanced analytics solution that enables engineers and subject matter experts in process manufacturing organizations to rapidly investigate and share insights from data in historians, IIoT platforms, AWS services, and manufacturing and business systems.
"W e are pleased to be announcing our work with AWS to develop solutions that deliver diagnostic, monitoring, and predictive analytics powered by big data and machine learning innovations. Using Seeq with Amazon Lookout for Equipment will help organizations turn data into insights that deliver continuous improvement and sustainability objectives."
Megan Buntain, Director of Cloud Partnerships - Seeq Corporation
"TensorIoT builds solutions with AWS services to accelerate integration of machine learning into products and processes across industrial operations. Leveraging Amazon Lookout for Equipment can help reduce the heavy lift of implementing machine learning by automatically developing, managing, and supporting the continuous improvement of anomaly detection models. This minimizes the number of manual touchpoints needed, and allows engineers to turn insights into operational improvements. Simply put, Lookout for Equipment allows companies to innovate faster."
Charles Burden, VP of Consulting - TensorIoT
Choose the cloud provider with proven experience in industrial operations
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