AWS Announces General Availability of Amazon Lookout for Equipment
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.
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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.