Ingesting sensor data from a customer’s industrial equipment (e.g. pressure, flow rate, RPMs, temperature, and power), Amazon Lookout trains a 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. The company says it 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 for customers.
It’s a common story: 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 analyse the data from their equipment, most companies typically use simple rules or modelling 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, 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. Amazon believes its Lookout offers the solution.
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, 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 analyse 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.
“Many industrial and manufacturing companies have heavily invested in physical sensors and other technology with the aim of improving the maintenance of their equipment”, said Swami Sivasubramanian, VP Amazon Machine Learning, AWS. “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.
“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”.
In addition to Amazon Lookout for Equipment, AWS offers industrial and manufacturing customers a 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).
Amogh Bhonde, senior vice president of digital solutions at Siemens Energy, said: “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”.
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.
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