Log on trigger vs. log on changeDifferent methods of logging machine data and how to use them

Logging machine data can benefit machine builders in many ways. Gaining insight into your machines allows you, among others, to optimize machine efficiency and even predict when maintenance will be needed. IXON’s Cloud Logging offers all the necessary tools for safely collecting, storing and visualising machine data. To optimize the data collection process, we currently offer three methods for logging your data:

  • Log on interval – logging data in static intervals
  • Log on change – logging data when a variable’s value changes
  • Log on trigger – logging data upon variable trigger

Cloud Logging methods explained

At first glance, log on trigger may seem quite similar to the log on change solution, but the mechanism that triggers the logging is very different. So what exactly is the difference between log on change and log on trigger? Let’s break it down.

Log on change will log once the value of the variable changes (e.g. from True to False or vice versa). This is useful for values that don’t change very often. The main advantage of logging on change is that it prevents data clutter, because consecutive values that stay the same aren’t logged again (e.g. True, True, True, etc.). One example of this is logging a machine’s status. The machine can be either “running”, “paused” or “off” and the status will only be logged when the current value changes.

With log on trigger, on the other hand, you can configure a variable as trigger and connect other variables to this trigger. Once the trigger happens, all linked variables will be logged once. This can be used for logging multiple variables simultaneously based on a certain trigger and will allow you to look back at your data associated with the trigger in a clear and easy overview. One example is logging your product’s measurement data when the product passes a certain sensor. In this case you could set up the sensor registering a product as the trigger and attach other variables (weight, size, type, container, etc.) to this trigger to make sure each time a product passes the sensor, all product measurement variables are logged once simultaneously. Another example is logging the machine state and critical data when an error happens. It is possible that the machine turns out to always be in manual state when an error happens. Thanks to log on trigger, you are now able to analyze situations where an error occurred and take action.

In conclusion, logging machine data can provide you with important insight into your machine. The key is choosing an appropriate logging method for your situation and with Cloud Logging’s log on trigger, log on change and log on interval methods, the possibilities are endless.

Are you eager to try Cloud Logging yourself to make the most of your machine data? Click the button below to learn more and start your free 30-day Cloud Logging trial.

Learn more or download the Cloud Logging case study.

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