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How an ML model from AWS detects abnormal machine behavior

AWS Lookout for Equipment

When it comes to machines, wear and tear are inevitable. However, if left unattended it can lead to inefficiency and by default, major losses. Operational efficiency is directly proportional to the health of equipment. With this as a reference point, Amazon Web Services(AWS), recently made an announcement concerning the general availability of Lookout for Equipment.

Lookout for Equipment

This new service includes machine learning models from AWS. Lookout for Equipment will be remarkable in empowering industrial customers. With the help of this service, they can leverage machine learning for the optimization of their equipment sensors which will help in carrying out large-scale predictive maintenance.

The service can be used for the identification of equipment anomalies, quick diagnosis of problems, and minimize false warnings, in addition to preventing costly downtime by taking action prior to system failure.

How ML helps Lookout for Equipment

Companies usually depend on physical sensors, data connectivity, and storage, and dashboards for tracking the performance of equipment. However, it goes without saying that the traditional methods used to point out defects are outdated. This is due to the fact that, more often than not, the detection is too late or unnecessary, and an avoidable burden falls on the company due to false alarms created by the wrong approach. This will take a toll on the cost and productivity which is not favorable for the growth of the company. Predictive maintenance requires complex data science and the appropriate selection of algorithms and parameters.

Now, with the advances in machine learning techniques, it is possible to detect the anomalies faster, in addition to discovering the unique relationship between the historical data of each piece of equipment.

However, the challenge faced by most of the companies is that they lack resources for building and scaling machine learning models which can facilitate predictive maintenance. Optimization of investment particularly in sensors and data infrastructure that can be of substantial help in improving operational efficiency and ROI often touches the realm of failure and companies fail to reap the benefits of the insights.

The Lookout for Equipment will facilitate the building of customized solutions for predictive maintenance with increased ease and convenience. Amazon’s Simple Storage Service(S3) is a platform that helps to upload sensor data, complete with power, vibration, temperature, velocity, and RPM. The customer will then provide the S3 bucket location as the input for the Lookout for Equipment, which will analyze the data automatically and help in determining the suitability of patterns. This will then help in the creation of a machine learning model that is in line with the customer’s facility.

These customized models will then run a thorough analysis that will help in the detection of loopholes and warning signs that indicate machine breakdown or malfunction. Depending on the sensor data, each event will be thoroughly scrutinized. The unique algorithm of the system will help in the identification of a particular sensor suggesting the problem. The effect of the problem on the detected incident will be quantified and its severity will be made clear. This will facilitate the detection of the issue, diagnosis of the problem, and then take suitable actions to prevent unplanned downtime.

The precision of identification of the most critical insights is improved by the Amazon Lookout for Equipment using an automated ML algorithm. This will help in giving a speed boost to the actions based on those insights.

 

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