Explained: How An ML Model From AWS Detects Abnormal Machine Behaviour
Every machine is subject to wear and tear and can lead to a loss of efficiency if left unattended. The health of equipment is key to driving operational efficiencies at shop floors. To that end, Amazon Web Services (AWS) recently announced the general availability of Lookout for Equipment. The new service is equipped with machine learning models from AWS. Amazon Lookout for Equipment empowers industrial customers to leverage machine learning to optimise their equipment sensors to carry out large-scale predictive maintenance. Customers can use the service to precisely identify equipment anomalies, diagnose problems quickly, minimise false warnings, and prevent costly downtime by taking action before system failure. How ML powers Lookout for Equipment Companies invest in physical sensors, data connectivity, data storage, and dashboards to track their equipment’s performance. Traditional methods like deploying models to point out machines’ defects based on past performance data are outdated. Either the issue is detected too late, or the wrong approach leads to false alarms, putting an unnecessary burden on the company in terms of cost and productivity. Predictive maintenance for equipment has historically required manual and complex data science, such as selecting the appropriate algorithms and parameters. Now, the advances in machine learning techniques help with faster detection of anomalies and to discover the unique relationships between each piece of equipment’s historical data. However, most companies don’t have the resources to build and scale custom machine learning models for predictive maintenance. Companies fail to optimise their investment in sensors and data infrastructure and miss out on actionable insights that could improve operational efficiency and maximise ROI. The Lookout for Equipment will help customers to build customised predictive maintenance solutions for their facilities with ease. Amazon’s Simple Storage Service (S3) is a platform to upload sensor data, including power, vibration, temperature, velocity, and RPMs. The customer will then input the S3 bucket location to Lookout for Equipment. The service will automatically analyse the data, determine whether the patterns are normal or safe, and create a machine learning model tuned to the customer’s facility. Once the customised models are ready, incoming sensor data will be thoroughly analysed to detect loopholes and warning signs for machine breakdown or malfunctioning. Every event will be scrutinised depending on the sensor data received. The system’s unique algorithm will identify a particular sensor suggesting the problem and quantify the severity of the problem’s effect on the detected incident. For example, if Amazon Lookout for Equipment spot an issue on a pump with 50 sensors, the service could identify which exact five sensors face an issue on a specific motor and correlate it to the motor power current and temperature. This allows customers to detect the issue, diagnose the problem, take actions, and improve operational efficiency by avoiding unplanned downtime. Amazon Lookout for Equipment uses an automated ML algorithm to improve the precision of identifying the most critical insights and speed up the time to put those insights into action. Wrapping up With Amazon Lookout for Equipment, customers can pay for the amount of data ingested, the compute hours used to train a custom model, and the number of inference hours used, the company has said. Siemens Energy, Cepsa, Embassy of Things, RoviSys, Seeq, and TensorIoT use Lookout for Equipment to improve outcomes. Amazon Lookout for Equipment is available directly via the AWS console and through supporting partners in the AWS Partner Network. The service is available in US East (N. Virginia), EU (Ireland), and Asia Pacific (Seoul), with availability in additional regions in the coming months.
Customers can use the service to precisely identify equipment anomalies, diagnose problems quickly, minimise false warnings, and prevent costly downtime.
Customers can use the service to precisely identify equipment anomalies, diagnose problems quickly, minimise false warnings etc.
["data sciences", "Machine Learning"]
["data science", "data sciences", "Go", "machine learning", "AWS", "AI", "cloud_platforms:AWS", "ML", "Machine Learning", "RAG", "ViT", "R"]
["AI", "machine learning", "ML", "data science", "RAG", "AWS", "R", "Go", "ViT", "cloud_platforms:AWS"]
Kumar Gandharv, PGD in English Journalism (IIMC, Delhi), is setting out on a journey as a tech Journalist at AIM. A keen observer of National and IR-related news.