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.”
RoviSys is a Global Operational Technology systems integrator, and a leading independent provider of comprehensive process automation solutions and services. “Machine learning is one of the most promising technologies for industrial customers, and has the potential to provide major value by decreasing maintenance and operational costs,” said Bryan DeBois, director of industrial AI at Rovisys. “RoviSys is working with AWS to integrate Amazon Lookout for Equipment with data from on-premises equipment and infrastructure using AWS IoT services, in order to enable advanced machine learning maintenance solutions at scale. This technology lets our customers leverage existing infrastructure, but unlock even more value from that data quickly and easily.”
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. “We 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,” said Megan Buntain, director of cloud partnerships at Seeq Corporation. “Using Seeq with Amazon Lookout for Equipment will help organizations turn data into insights that deliver continuous improvement and sustainability objectives.”
TensorIoT is an AWS Advanced Consulting Partner delivering complete end-to-end products and solutions in IoT, data engineering, machine learning, and artificial intelligence. “TensorIOT builds solutions with AWS services to accelerate integration of machine learning in products and processes across industrial operations,” said Charles Burden, vice president of consulting at TensorIoT. “Leveraging Amazon Lookout for Equipment can help reduce the heavy lift of leveraging machine learning by automatically developing, managing, and supporting the continuous improvement of anomaly detection models. This greatly reduces 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.”
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