New – Amazon Lookout for Equipment Analyzes Sensor Data to Help Detect Equipment Failure

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Companies that operate industrial equipment are constantly working to improve operational efficiency and avoid unplanned downtime due to component failure. They invest heavily and repeatedly in physical sensors (tags), data connectivity, data storage, and building dashboards over the years to monitor the condition of their equipment and get real-time alerts. The primary data analysis methods are single-variable threshold and physics-based modeling approaches, and while these methods are effective in detecting specific failure types and operating conditions, they can often miss important information detected by deriving multivariate relationships for each piece of equipment.

With machine learning, more powerful technologies have become available that can provide data-driven models that learn from an equipment’s historical data. However, implementing such machine learning solutions is time-consuming and expensive owing to capital investment and training of engineers.

Today, we are happy to announce Amazon Lookout for Equipment, an API-based machine learning (ML) service that detects abnormal equipment behavior. With Lookout for Equipment, customers can bring in historical time series data and past maintenance events generated from industrial equipment that can have up to 300 data tags from components such as sensors and actuators per model. Lookout for Equipment automatically tests the possible combinations and builds an optimal machine learning model to learn the normal behavior of the equipment. Engineers don’t need machine learning expertise and can easily deploy models for real-time processing in the cloud.

Customers can then easily perform ML inference to detect abnormal behavior of the equipment. The results can be integrated into existing monitoring software or AWS IoT SiteWise Monitor to visualize the real-time output or to receive alerts if an asset tends toward anomalous conditions.

How Lookout for Equipment Works
Lookout for Equipment reads directly from Amazon S3 buckets. Customers can publish their industrial data in S3 and leverage Lookout for Equipment for model development. A user determines the value or time period to be used for training and assigns an appropriate label. Given this information, Lookout for Equipment launches a task to learn and creates the best ML model for each customer.

Because Lookout for Equipment is an automated machine learning tool, it gets smarter over time as users use Lookout for Equipment to retrain their models with new data. This is useful for model re-creation when new invisible failures occur, or when the model drifts over time. Once the model is complete and can be inferred, Lookout for Equipment provides real-time analysis.

With the equipment data being published to S3, the user can scheduled inference that ranges from 5 minutes to one hour. When the user data arrives in S3, Lookout for Equipment fetches the new data on the desired schedule, performs data inference, and stores the results in another S3 bucket.

Set up Lookout for Equipment with these simply steps:

  1. Upload data to S3 buckets
  2. Create datasets
  3. Ingest data
  4. Create a model
  5. Schedule inference (if you need real-time analysis)

1. Upload data
You need to upload tag data from equipment to any S3 bucket.

2. Create Datasets

Select Create dataset, and set Dataset name, and set Data Schema. Data schema is like a data design document that defines the data to be fed in later. Then select Create.

creating datasets console

3. Ingest data
After a dataset is created, the next step is to ingest data. If you are familiar with Amazon Personalize or Amazon Forecast, doesn’t this screen feel familiar? Yes, Lookout for Equipment is as easy to use as those are.

Select Ingest data.

Ingesting data consoleSpecify the S3 bucket location where you uploaded your data, and an IAM role. The IAM role has to have a trust relationship to “lookoutequipment.amazonaws.com” You can use the following policy file for the test.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lookoutequipment.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

The data format in the S3 bucket has to match the Data Schema you set up in step 2. Please check our technical documents for more detail. Ingesting data takes a few minutes to tens of minutes depending on your data volume.

4. Create a model
After data ingest is completed, you can train your own ML model now. Select Create new model. Fields show us a list of fields in the ingested data. By default, no field is selected. You can select fields you want Lookout for Equipment to learn. Lookout for Equipment automatically finds and trains correlations from multiple specified fields and creates a model.

Image illustrates setting up fields.

If you are sure that your data has some unusual data included, you can optionally set the windows to exclude that data.

setting up maintenance windowOptionally, you can divide ingested data for training and then for evaluation. The data specified during the evaluation period is checked compared to the trained model.

setting up evaluation window

Once you select Create, Lookout for Equipment starts to train your model. This process takes minutes to hours depending on your data volume. After training is finished, you can evaluate your model with the evaluation period data.

model performance console

5. Schedule Inference
Now it is time to analyze your real time data. Select Schedule Inference, and set up your S3 buckets for input.

setting up input S3 bucket

You can also set Data upload frequency, which is actually the same as inferencing frequency, and Offset delay time. Then, you need to set up Output data as Lookout for Equipment outputs the result of inference.

setting up inferenced output S3 bucket

Amazon Lookout for Equipment is In Preview Today
Amazon Lookout for Equipment is in preview today at US East (N. Virginia), Asia Pacific (Seoul), and Europe (Ireland) and you can see the documentation here.

– Kame

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