Generate actionable insights for predictive upkeep administration with Amazon Monitron and Amazon Kinesis

Reliability managers and technicians in industrial environments similar to manufacturing manufacturing strains, warehouses, and industrial vegetation are eager to enhance tools well being and uptime to maximise product output and high quality. Machine and course of failures are sometimes addressed by reactive exercise after incidents occur or by pricey preventive upkeep, the place you run the chance of over-maintaining the tools or lacking points that might occur between the periodic upkeep cycles. Predictive condition-based upkeep is a proactive technique that’s higher than reactive or preventive ones. Certainly, this method combines steady monitoring, predictive analytics, and just-in-time motion. This permits upkeep and reliability groups to service tools solely when needed, primarily based on the precise tools situation.

There have been widespread challenges with condition-based monitoring to generate actionable insights for giant industrial asset fleets. These challenges embrace however usually are not restricted to: construct and preserve a posh infrastructure of sensors accumulating knowledge from the sphere, get hold of a dependable high-level abstract of commercial asset fleets, effectively handle failure alerts, establish attainable root causes of anomalies, and successfully visualize the state of commercial property at scale.

Amazon Monitron is an end-to-end situation monitoring answer that allows you to begin monitoring tools well being with the help of machine studying (ML) in minutes, so you’ll be able to implement predictive upkeep and scale back unplanned downtime. It consists of sensor gadgets to seize vibration and temperature knowledge, a gateway system to securely switch knowledge to the AWS Cloud, the Amazon Monitron service that analyzes the information for anomalies with ML, and a companion cellular app to trace potential failures in your equipment. Your subject engineers and operators can straight use the app to diagnose and plan upkeep for industrial property.

From the operational know-how (OT) staff standpoint, utilizing the Amazon Monitron knowledge additionally opens up new methods to enhance how they function giant industrial asset fleets due to AI. OT groups can reinforce the predictive upkeep observe from their group by constructing a consolidated view throughout a number of hierarchies (property, websites, and vegetation). They’ll mix precise measurement and ML inference outcomes with unacknowledged alarms, sensors or getaways connectivity standing, or asset state transitions to construct a high-level abstract for the scope (asset, web site, mission) they’re targeted on.

With the just lately launched Amazon Monitron Kinesis data export v2 feature, your OT staff can stream incoming measurement knowledge and inference outcomes from Amazon Monitron by way of Amazon Kinesis to AWS Simple Storage Service (Amazon S3) to construct an Web of Issues (IoT) knowledge lake. By leveraging the latest data export schema, you’ll be able to get hold of sensors connectivity standing, gateways connectivity standing, measurement classification outcomes, closure cause code and particulars of asset state transition occasions.

Use circumstances overview

The enriched knowledge stream Amazon Monitron now exposes allows you to implement a number of key use circumstances similar to automated work order creation, enriching an operational single pane of glass or automating failure reporting. Let’s dive into these use circumstances.

You need to use the Amazon Monitron Kinesis knowledge export v2 to create work orders in Enterprise Asset Administration (EAM) techniques similar to Infor EAM, SAP Asset Administration, or IBM Maximo. For instance, within the video avoiding mechanical issues with predictive maintenance & Amazon Monitron, you’ll be able to uncover how our Amazon Achievement Facilities are avoiding mechanical points on conveyor belts with Amazon Monitron sensors built-in with third-party software program such because the EAM used at Amazon in addition to with the chat rooms technicians used. This exhibits how one can naturally combine Amazon Monitron insights into your current workflows. Keep tuned within the coming months to learn the following installment of this collection with an precise implementation of this integration works.

You too can use the information stream to ingest Amazon Monitron insights again into a store flooring system similar to a Supervisory Management and Information Acquisition (SCADA) or a Historian. Store flooring operators are extra environment friendly when all of the insights about their property and processes are supplied in a single pane of glass. On this idea, Amazon Monitron doesn’t change into one more device technicians have to observe, however one other knowledge supply with insights supplied within the single view they’re already used to. Later this 12 months, we can even describe an structure you should utilize to carry out this job and ship Amazon Monitron suggestions to main third-party SCADA techniques and Historians.

Final however not least, the brand new knowledge stream from Amazon Monitron consists of the asset state transitions and closure codes supplied by customers when acknowledging alarms (which set off the transition to a brand new state). Due to this knowledge, you’ll be able to routinely construct visualizations that present real-time reporting of the failures and actions taken whereas working their property.

Your staff can then construct a broader knowledge analytics dashboard to help your industrial fleet administration observe by combining this asset state knowledge with Amazon Monitron measurement knowledge and different IoT knowledge throughout giant industrial asset fleets through the use of key AWS providers, which we describe on this submit. We clarify easy methods to construct an IoT knowledge lake, the workflow to supply and devour the information, in addition to a abstract dashboard to visualise Amazon Monitron sensors knowledge and inference outcomes. We use an Amazon Monitron dataset coming from about 780 sensors put in in an industrial warehouse, which has been operating for greater than 1 12 months. For the detailed Amazon Monitron set up information, check with Getting started with Amazon Monitron.

Resolution overview

Amazon Monitron supplies ML inference of asset well being standing after 21 days of the ML model training period for every asset. On this answer, the measurement knowledge and ML inference from these sensors are exported to Amazon S3 by way of Amazon Kinesis Data Streams through the use of the latest Amazon Monitron data export feature. As quickly as Amazon Monitron IoT knowledge is on the market in Amazon S3, a database and desk are created in Amazon Athena through the use of an AWS Glue crawler. You’ll be able to question Amazon Monitron knowledge by way of AWS Glue tables with Athena, and visualize the measurement knowledge and ML inference with Amazon Managed Grafana. With Amazon Managed Grafana, you’ll be able to create, discover, and share observability dashboards together with your staff, and spend much less time managing your Grafana infrastructure. On this submit, you join Amazon Managed Grafana to Athena, and discover ways to construct an information analytics dashboard with Amazon Monitron knowledge that will help you plan industrial asset operations at scale.

The next screenshot is an instance of what you’ll be able to obtain on the finish of this submit. This dashboard is split into three sections:

  • Plant View – Analytical data from all sensors throughout vegetation; for instance, the general counts of assorted states of sensors (Wholesome, Warning, or Alarm), variety of unacknowledged and acknowledged alarms, gateway connectivity, and common time for upkeep
  • Web site View – Web site-level statistics, similar to asset standing statistics at every web site, complete variety of days that an alarm stays unacknowledged, prime/backside performing property at every web site, and extra
  • Asset View – Abstract data for the Amazon Monitron mission on the asset stage, such because the alarm kind for an unacknowledged alarm (ISO or ML), the timeline for an alarm, and extra

These panels are examples that may assist strategic operational planning, however they aren’t unique. You need to use an analogous workflow to customise the dashboard in keeping with your focused KPI.

Structure overview

The answer you’ll construct on this submit combines Amazon Monitron, Kinesis Information Streams, Amazon Kinesis Data Firehose, Amazon S3, AWS Glue, Athena, and Amazon Managed Grafana.

The next diagram illustrates the answer structure. Amazon Monitron sensors measure and detect anomalies from tools. Each measurement knowledge and ML inference outputs are exported at a frequency of as soon as per hour to a Kinesis knowledge stream, and they’re delivered to Amazon S3 by way of Kinesis Information Firehose with a 1-minute buffer. The exported Amazon Monitron knowledge is in JSON format. An AWS Glue crawler analyzes the Amazon Monitron knowledge in Amazon S3 at a selected frequency of as soon as per hour, builds a metadata schema, and creates tables in Athena. Lastly, Amazon Managed Grafana makes use of Athena to question the Amazon S3 knowledge, permitting dashboards to be constructed to visualise each measurement knowledge and system well being standing.

To construct this answer, you full the next high-level steps:

  1. Allow a Kinesis Information Stream export from Amazon Monitron and create an information stream.
  2. Configure Kinesis Information Firehose to ship knowledge from the information stream to an S3 bucket.
  3. Construct the AWS Glue crawler to create a desk of Amazon S3 knowledge in Athena.
  4. Create a dashboard of Amazon Monitron gadgets with Amazon Managed Grafana.


For this walkthrough, you must have the next conditions:

Moreover, make it possible for all of the sources you deploy are in the identical Area.

Allow a Kinesis knowledge stream export from Amazon Monitron and create an information stream

To configure your knowledge stream export, full the next steps:

  1. On the Amazon Monitron console, out of your mission’s important web page, select Begin stay knowledge export.
  2. Underneath Choose Amazon Kinesis knowledge stream, select Create a brand new knowledge stream.
  3. Underneath Information stream configuration, enter your knowledge stream identify.
  4. For Information stream capability, select On-demand.
  5. Select Create knowledge stream.

Notice that any stay knowledge export enabled after April 4th, 2023 will stream knowledge following the Kinesis Information Streams v2 schema. When you have an current knowledge export that was enabled earlier than this date, the schema will comply with the v1 format.

Now you can see stay knowledge export data on the Amazon Monitron console together with your specified Kinesis knowledge stream.

Configure Kinesis Information Firehose to ship knowledge to an S3 bucket

To configure your Firehose supply stream, full the next steps:

  1. On the Kinesis console, select Supply streams within the navigation pane.
  2. Select Create supply stream.
  3. For Supply, choose Amazon Kinesis Information Streams.
  4. For Vacation spot, choose Amazon S3.
  5. Underneath Supply settings, for Kinesis knowledge stream, enter the ARN of your Kinesis knowledge stream.
  6. Underneath Supply stream identify, enter the identify of your Kinesis knowledge stream.
  7. Underneath Vacation spot settings, select an S3 bucket or enter a bucket URI. You’ll be able to both use an current S3 bucket to retailer Amazon Monitron knowledge, or you’ll be able to create a brand new S3 bucket.
  8. Allow dynamic partitioning utilizing inline parsing for JSON:
    • Select Enabled for Dynamic partitioning.
    • Select Enabled for Inline parsing for JSON.
    • Underneath Dynamic partitioning keys, add the next partition keys:
Key Title JQ Expression
mission .projectName| "mission=(.)"
web site .eventPayload.siteName| "web site=(.)"
asset .eventPayload.assetName| "asset=(.)"
place .eventPayload.positionName| "place=(.)"
time .timestamp| sub(" [0-9]{2}:[0-9]{2}:[0-9]{2}.[0-9]{3}$"; "")| "time=(.)"
  1. Select Apply dynamic partitioning keys and ensure the generated S3 bucket prefix is:
!{partitionKeyFromQuery:mission}/!{partitionKeyFromQuery:web site}/!{partitionKeyFromQuery:asset}/!{partitionKeyFromQuery:place}/!{partitionKeyFromQuery:time}/.

  1. Enter a prefix for S3 bucket error output prefix. Any JSON payload that doesn’t include the keys described earlier will probably be delivered on this prefix. For example, thegatewayConnectedand gatewayDisconnected occasions usually are not linked to a given asset or place. Due to this fact, they gained’t include the assetName and positionName fields. Specifying this elective prefix right here means that you can monitor this location and course of these occasions accordingly.
  2. Select Create supply stream.

You’ll be able to examine the Amazon Monitron knowledge within the S3 bucket. Notice that the Amazon Monitron knowledge will export stay knowledge at a frequency of as soon as per hour, so watch for 1 hour to examine the information.

This Kinesis Information Firehose setup allows dynamic partitioning, and the S3 objects delivered will use the next key format:

/mission={projectName}/web site={siteDisplayName}/asset={assetDisplayName}/ place={sensorPositionDisplayName}/time={yyyy-mm-dd 00:00:00}/{filename}.

Construct the AWS Glue crawler to create a desk of Amazon S3 knowledge in Athena

After the stay knowledge has been exported to Amazon S3, we use an AWS Glue crawler to generate the metadata tables. On this submit, we use AWS Glue crawlers to routinely infer database and desk schema from Amazon Monitron knowledge exported in Amazon S3, and retailer the related metadata within the AWS Glue Information Catalog. Athena then makes use of the desk metadata from the Information Catalog to search out, learn, and course of the information in Amazon S3. Full the next steps to create your database and desk schema:

  1. On the AWS Glue console, select Crawlers within the navigation pane.
  2. Select Create crawler.
  3. Enter a reputation for the crawler (for instance,XXX_xxxx_monitron).
  4. Select Subsequent.
  5. For Is your knowledge already mapped to Glue tables, select Not but.
  6. For Information Supply, select S3.
  7. For Location of S3 knowledge, select On this Account, and enter the trail of your S3 bucket listing you arrange within the earlier part (s3://YourBucketName).
  8. For Repeat crawls of S3 knowledge shops, choose Crawl all sub-folders.
  9. Lastly, select Subsequent.
  10. Choose Create new IAM function and enter a reputation for the function.
  11. Select Subsequent.
  12. Choose Add Database, and enter a reputation for the database. This creates the Athena database the place your metadata tables are positioned after the crawler is full.
  13. For Crawler Schedule, choose a most popular time-based scheduler (for instance, hourly) to refresh the Amazon Monitron knowledge within the database, and select Subsequent.
  14. Assessment the crawler particulars and select Create.
  15. On the Crawlers web page of the AWS Glue console, choose the crawler you created and select Run crawler.

You could want to attend a couple of minutes, relying on the scale of the information. When it’s full, the crawler’s standing exhibits as Prepared. To see the metadata tables, navigate to your database on the Databases web page and select Tables within the navigation pane.

You too can view knowledge by selecting Desk knowledge on the console.

You’re redirected to the Athena console to view the highest 10 data of the Amazon Monitron knowledge in Amazon S3.

Create a dashboard of Amazon Monitron gadgets with Amazon Managed Grafana

On this part, we construct a custom-made dashboard with Amazon Managed Grafana to visualise Amazon Monitron knowledge in Amazon S3, in order that OT staff can get streamlined entry to property in alarm throughout their entire Amazon Monitron sensors fleet. This can allow the OT staff to plan subsequent step actions primarily based on the attainable root reason behind the anomalies.

To create a Grafana workspace, full the next steps:

  1. Make sure that your person function is admin or editor.
  2. On the Amazon Managed Grafana console, select Create workspace.
  3. For Workspace identify, enter a reputation for the workspace.
  4. Select Subsequent.
  5. For Authentication entry, choose AWS IAM Identification Heart (successor to AWS Single Signal-On). You need to use the identical AWS IAM Identity Center user that you just used to arrange your Amazon Monitron mission.
  6. Select Subsequent.
  7. For this primary workspace, verify that Service managed is chosen for Permission kind. This choice allows Amazon Managed Grafana to routinely provision the permissions you want for the AWS knowledge sources that you just use for this workspace.
  8. Select Present account.
  9. Select Subsequent.
  10. Verify the workspace particulars, and select Create workspace. The workspace particulars web page seems. Initially, the standing is CREATING.
  11. Wait till the standing is ACTIVE to proceed to the following step.

To configure your Athena knowledge supply, full the next steps:

  1. On the Amazon Managed Grafana console, select the workspace you need to work on.
  2. On the Information sources tab, choose Amazon Athena, and select Actions, Allow service-managed coverage.
  3. Select Configure in Grafana within the Amazon Athena row.
  4. Check in to the Grafana workspace console utilizing IAM Identification Heart if needed. The person ought to have the Athena entry coverage connected to the person or function to have entry to the Athena knowledge supply. See AWS managed policy: AmazonGrafanaAthenaAccess for more information.
  5. On the Grafana workspace console, within the navigation pane, select the decrease AWS icon (there are two) after which select Athena on the Information sources menu.
  6. Choose the default Area that you really want the Athena knowledge supply to question from, choose the accounts that you really want, then select Add knowledge supply.
  7. Observe the steps to configure Athena details.

In case your workgroup in Athena doesn’t have an output location configured already, that you must specify an S3 bucket and folder to make use of for question outcomes. After organising the information supply, you’ll be able to view or edit it within the Configuration pane.

Within the following subsections, we reveal a number of panels within the Amazon Monitron dashboard in-built Amazon Managed Grafana to achieve operational insights. The Athena knowledge supply supplies an ordinary SQL question editor that we’ll use to investigate the Amazon Monitron knowledge to generate desired analytics.

First, if there are various sensors within the Amazon Monitron mission and they’re in numerous states (wholesome, warning, alarm, and wishes upkeep), the OT staff desires to visually see the depend of positions that sensors are in varied states. You’ll be able to get hold of such data as a pie chart widget in Grafana by way of the next Athena question:

Choose * FROM (Choose latest_status, COUNT(assetdisplayname)OVER (PARTITION BY latest_status) AS asset_health_count FROM (SELECT timestamp, sitedisplayname, assetdisplayname, assetState.newState as latest_status, RANK() OVER (PARTITION BY assetdisplayname ORDER BY timestamp DESC)AS rnk FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify") tt WHERE tt.rnk=1) GROUP BY latest_status, asset_health_count; 

The next screenshot exhibits a panel with the newest distribution of Amazon Monitron sensor standing.

To format your SQL question for Amazon Monitron knowledge, check with Understanding the data export schema.

Subsequent, your Operations Expertise staff could need to plan predictive upkeep primarily based on property which can be in alarm standing, and subsequently they need to shortly know the whole variety of acknowledged alarms vs. unacknowledged alarms. You’ll be able to present the abstract data of alarm state as easy stats panels in Grafana:

Choose COUNT(*) FROM (Choose timestamp, sitedisplayname, assetdisplayname, assetState.newState as latest_status, RANK() OVER (PARTITION BY assetdisplayname ORDER BY timestamp DESC)AS rnk FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify") tt WHERE tt.rnk=1 AND tt.latest_status="Alarm";

The next panel exhibits acknowledged and unacknowledged alarms.

The OT staff also can question the period of time the sensors stay in alarm standing, in order that they’ll determine their upkeep precedence:

Choose c.assetdisplayname, b.sensorpositiondisplayname, b.alarm_date FROM (Choose a.assetdisplayname, a.sensorpositiondisplayname, COUNT(*)/24+1 AS number_of_days_in_alarm_state FROM (Choose * FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify" WHERE (assetState.newState="ALARM" AND assetState.newState = assetState.previousState) ORDER BY timestamp DESC) a GROUP BY a.assetdisplayname, a.sensorpositiondisplayname) b INNER JOIN (Choose * FROM (Choose timestamp, sitedisplayname, assetdisplayname, assetState.newState AS latest_status, RANK() OVER (PARTITION BY assetdisplayname ORDER BY timestamp DESC)AS rnk FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify") tt WHERE tt.rnk=1 AND tt.latest_status="ALARM") c ON b.assetdisplayname = c.assetdisplayname;

The output of this evaluation may be visualized by a bar chart in Grafana, and the alarm in alarm state may be simply visualized as proven within the following screenshot.

To research prime/backside asset efficiency primarily based on the whole period of time the property are in an alarm or want upkeep state, use the next question:

Choose s.sitedisplayname, s.assetdisplayname, COUNT(s.timestamp)/24 AS trouble_time FROM (Choose timestamp, sitedisplayname, assetdisplayname, sensorpositiondisplayname, assetState.newState FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify" WHERE assetState.newState="ALARM" OR assetState.newState="NEEDS_MAINTENANCE") AS s GROUP BY s.assetdisplayname, s.sitedisplayname ORDER BY trouble_time, s.assetdisplayname ASC LIMIT 5;

The next bar gauge is used to visualise the previous question output, with the highest performing property displaying 0 days of alarm states, and the underside performing property displaying amassed alarming states over the previous 12 months.

To assist the OT staff perceive the attainable root reason behind an anomaly, the alarm varieties may be displayed for these property nonetheless in alarm state with the next question:

Choose a.assetdisplayname, a.sensorpositiondisplayname, a.latest_status, CASE WHEN a.temperatureML != 'HEALTHY' THEN 'TEMP' WHEN a.vibrationISO != 'HEALTHY' THEN 'VIBRATION_ISO' ELSE 'VIBRATION_ML' END AS alarm_type  FROM (Choose sitedisplayname, assetdisplayname, sensorpositiondisplayname, fashions.temperatureML.persistentClassificationOutput as temperatureML, fashions.vibrationISO.persistentClassificationOutput as vibrationISO, fashions.vibrationML.persistentClassificationOutput as vibrationML, assetState.newState as latest_status FROM (Choose *, RANK() OVER (PARTITION BY assetdisplayname, sensorpositiondisplayname ORDER BY timestamp DESC)AS rnk FROM "AwsDataCatalog"."Exchange together with your Athena database identify"."Exchange together with your Athena desk identify") tt WHERE tt.rnk=1 AND assetState.newState="ALARM" ) a WHERE (a.temperatureML != 'HEALTHY' OR a. vibrationISO != 'HEALTHY' OR a. vibrationML != 'HEALTHY');

You’ll be able to visualize this evaluation as a desk in Grafana. On this Amazon Monitron mission, two alarms have been triggered by ML fashions for vibration measurement.

The Amazon Managed Grafana dashboard is proven right here for illustration functions. You’ll be able to adapt the dashboard design in keeping with your individual enterprise wants.

Failure Studies

When a person acknowledges an alarm within the Amazon Monitron app, the related property transition to a brand new state. The person additionally has the chance to offer some particulars about this alarm:


The occasion payload related to the asset state transition accommodates all this data, the earlier state of the asset, and the brand new state of the asset. Keep tuned for an replace of this submit with extra particulars on how you should utilize this data in a further Grafana panel to construct Pareto charts of the most typical failures and actions taken throughout your property.


Enterprise prospects of Amazon Monitron are in search of an answer to construct an IoT knowledge lake with Amazon Monitron’s stay knowledge, to allow them to handle a number of Amazon Monitron tasks and property, and generate analytics studies throughout a number of Amazon Monitron tasks. This submit present an in depth walkthrough of an answer to construct this IoT knowledge lake with the newest Amazon Monitron Kinesis data export v2 feature. This answer additionally confirmed easy methods to use different AWS providers, similar to AWS Glue and Athena to question the information, generate analytics outputs, and visualize such outputs with Amazon Managed Grafana with frequent refresh.

As a subsequent step, you’ll be able to increase this answer by sending ML inference outcomes to different EAM techniques that you just may use for work order administration. This can enable your operation staff to combine Amazon Monitron with different enterprise purposes, and enhance their operation effectivity. You too can begin constructing extra in-depth insights into your failure modes and actions taken by processing the asset state transitions and the closure codes that at the moment are a part of the Kinesis knowledge stream payload.

Concerning the authors

Julia Hu is a Sr. AI/ML Options Architect at Amazon Net Companies. She has intensive expertise in IoT structure and Utilized Information Science, and is a part of each the Machine Studying and IoT Technical Area Group. She works with prospects, starting from start-ups to enterprises, to develop AWSome IoT machine studying (ML) options, on the Edge and within the Cloud. She enjoys leveraging newest IoT and large knowledge know-how to scale up her ML answer, scale back latency, and speed up business adoption.

Bishr Tabbaa is a options architect at Amazon Net Companies. Bishr makes a speciality of serving to prospects with machine studying, safety, and observability purposes. Exterior of labor, he enjoys enjoying tennis, cooking, and spending time with household.

Shalika Pargal is a Product Supervisor at Amazon Net Companies. Shalika focuses on constructing AI services and products for Industrial prospects. She brings important expertise on the intersection of Product, Industrial and Enterprise Growth. She just lately shared Monitron’s success story at Reinvent 2022.

Garry Galinsky is a Principal Options Architect supporting Amazon on AWS. He has been concerned with Monitron since its debut and has helped combine and deploy the answer into Amazon’s worldwide success community. He just lately shared Amazon’s Monitron success story at re:Invent 2022.

Michaël Hoarau is an AI/ML Specialist Options Architect at AWS who alternates between knowledge scientist and machine studying architect, relying on the second. He’s enthusiastic about bringing the AI/ML energy to the store flooring of his industrial prospects and has labored on a variety of ML use circumstances, starting from anomaly detection to predictive product high quality or manufacturing optimization. He printed a book on time series analysis in 2022 and frequently writes about this subject on LinkedIn and Medium. When not serving to prospects develop the following greatest machine studying experiences, he enjoys observing the celebrities, touring, or enjoying the piano.

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