Amazon Monitron Starter Kit, an end-to-end system for equipment monitoring

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Over time, the gateway will keep sending this data securely to AWS, where it will be analyzed for early signs of failure. Should either of my assets exhibit these, I would receive an alert in the mobile application, where I could visualize historical data, and decide what the best course of action would be. Failure cause – This can be one of the following: ADMINISTRATION, DESIGN, FABRICATION, MAINTENANCE, OPERATION, OTHER, QUALITY, WEAR, or UNDEDETERMINED AWS Panorama Appliance enables customers with existing cameras in their industrial facilities with the ability to use computer vision to improve quality control and workplace safety The output of this analysis can be visualized by a bar chart in Grafana, and the alarm in alarm state can be easily visualized as shown in the following screenshot.

I repeat the same operation for the pump. Looking at my asset, I see that both sensors are operational. Condition-based maintenance: where maintenance is completed when the condition of a monitored component breaches a defined threshold. Monitoring physical characteristics such as tolerance, vibration or temperature is a more optimal strategy, requiring less maintenance and reducing maintenance costs.

The following screenshot is an example of what you can achieve at the end of this post. This dashboard is divided into three sections: As you can guess, building and deploying such maintenance systems can be a long, complex, and costly project involving bespoke hardware, software, infrastructure, and processes. Our customers asked us for help, and we got to work. Select Add Database, and enter a name for the database. This creates the Athena database where your metadata tables are located after the crawler is complete. The following bar gauge is used to visualize the preceding query output, with the top performing assets showing 0 days of alarm states, and the bottom performing assets showing accumulated alarming states over the past year. There have been common challenges with condition-based monitoring to generate actionable insights for large industrial asset fleets. These challenges include but are not limited to: build and maintain a complex infrastructure of sensors collecting data from the field, obtain a reliable high-level summary of industrial asset fleets, efficiently manage failure alerts, identify possible root causes of anomalies, and effectively visualize the state of industrial assets at scale.

Note that any live data export enabled after April 4th, 2023 will stream data following the Kinesis Data Streams v2 schema. If you have an existing data export that was enabled before this date, the schema will follow the v1 format. My next step is to create an asset that I’d like to monitor, say a process water pump set, with a motor and a pump that I would like to monitor. I first create the asset itself, simply defining its name, and the appropriate ISO 20816 class (a standard for measurement and evaluation of machine vibration). On the Grafana workspace console, in the navigation pane, choose the lower AWS icon (there are two) and then choose Athena on the Data sources menu. One use case where AWS customers are excited to deploy computer vision with their cameras is for quality control. Industrial companies must maintain constant diligence to maintain quality control. In the manufacturing industry alone, production line shutdowns due to overlooked errors result in millions of dollars of cost overruns and lost revenue every year. The visual inspection of industrial processes typically requires human inspection, which can be tedious and inconsistent. Computer vision brings the speed and accuracy needed to identify defects consistently, but implementation can be complex and require teams of data scientists to build, deploy, and manage the machine learning models. Because of these barriers, machine learning-powered visual anomaly systems remain out of reach for the vast majority of companies. Here’s how AWS can now help these companies:

Configure Kinesis Data Firehose to deliver data to an S3 bucket

Condition-based maintenance and predictive maintenance require sensors to be installed on critical equipment. These sensors measure and capture physical quantities such as temperature and vibration, whose change is a leading indicator of a potential failure or a deteriorating condition. Die Sensoren erfassen Vibrations- & Temperaturdaten und zeigen diese im zeitlichen Verlauf auf der Handy-App oder der Webapp. Die Daten werden in übersichtlichen Grafiken und Diagrammen dargestellt, die eine schnelle und einfache Analyse ermöglichen. The event payload associated to the asset state transition contains all this information, the previous state of the asset, and the new state of the asset. Stay tuned for an update of this post with more details on how you can use this information in an additional Grafana panel to build Pareto charts of the most common failures and actions taken across your assets. Conclusion AWS Panorama Software Development Kit (SDK) allows industrial camera manufacturers to embed computer vision capabilities in new cameras