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

Product Information

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.

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.I start by physically attaching the sensor to the motor using the suggested adhesive. Next, I specify a sensor position, enable the NFC on my smartphone, and tap the Monitron sensor that I attached to the motor with my phone. Within seconds, the sensor is commissioned. AWS Panorama Software Development Kit (SDK) allows industrial camera manufacturers to embed computer vision capabilities in new cameras Die Benutzeroberfläche des Systems ist benutzerfreundlich und intuitiv, so dass selbst unerfahrene Benutzer das System schnell und einfach nutzen können. Darüber hinaus bietet das System eine Reihe von Alarmfunktionen, die es ermöglichen, sofort auf Abweichungen von den normalen Betriebsbedingungen zu reagieren und potenzielle Probleme frühzeitig zu erkennen.

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. The following screenshot is an example of what you can achieve at the end of this post. This dashboard is divided into three sections:

Use cases overview

Axis, ADLINK Technology, BP, Fender,GE Healthcare, andSiemens Mobilityamong customers and partners using new AWS industrial machine learning services Insbesondere der Installations- & Inbetriebnahmeprozess ist perfekt gelöst! Ich muss mich nicht mit einer langen Inbetriebnahme rumschlagen, sondern kann die Sensoren ganz einfach mittels der Monitron Smartphone App und NFC in Betrieb nehmen. Predictive maintenance can be applied to existing assets, for example by applying add-on sensors, but even better, it is incorporated right from the start in the development of a new asset, enabling optimal sensor integration. 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. 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

These panels are examples that can help strategic operational planning, but they are not exclusive. You can use a similar workflow to customize the dashboard according to your targeted KPI. No specialized knowledge is required to build this solution, but basic Linux, Python, and SQL knowledge will help. 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. Select the default Region that you want the Athena data source to query from, select the accounts that you want, then choose Add data source.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.

An AWS account. If you don’t have an AWS account, follow the instructions to create one, unless you have been provided event engine details. 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. 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. To avoid breakdowns, reliability managers and maintenance technicians often combine four strategies:Planned maintenance: where predefined maintenance activities are performed on a periodic or meter basis, regardless of condition. The effectiveness of planned maintenance activities is dependent on the quality of the maintenance instructions and planned cycle. It risks equipment being both over- and under-maintained, incurring unnecessary cost or still experiencing breakdowns. A user role with administrator access (service access associated with this role can be constrained further when the workflow goes to production). 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). I select the gateway, and I configure it with my WiFi credentials to let it connect to AWS. A few seconds later, the gateway is online. You can observe how uncommon signals are detected early, so that you can make interventions to prevent incidents. However, soon you will realize that your real-world operation requires a more dedicated system. So yes, do get started with a low-entry predictive maintenance application, and start experimenting. But please keep in mind that a professional consultancy and implementation partner will help you get the most out of such a system.