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). A user role with administrator access (service access associated with this role can be constrained further when the workflow goes to production). 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. Failure cause – This can be one of the following: ADMINISTRATION, DESIGN, FABRICATION, MAINTENANCE, OPERATION, OTHER, QUALITY, WEAR, or UNDEDETERMINED

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.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. 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.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. 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. 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: 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.