A mixture model application in monitoring error message rates for a distributed industrial fleet

Remotely monitoring industrial printers for an unexpected increase of warning and error messages reduces equipment downtime and increases customer satisfaction. Directly tracking raw error messages rates during a given observation period poses some issues. Firstly, when a printer has not been used much during the observation period, its actual printing time is low. In this situation, even a small set of error messages can become an unexpectedly large rate of messages per printing hour. Secondly, classifying printers in error messages groups based on their rate (for instance, low, medium and high) and studying group changes over time, is useful in identifying potential problems. To overcome these issues, a nonparametric estimation method which simultaneously obtains empirical Bayes estimations of error messages rates and the number of error messages groups is used. This approach has been used in epidemiology, mainly in disease mapping research, but not in an industrial reliability context. The objective of our work is to show the application of the mixture model to real-time monitoring of printers' error message rates in a way that addresses the two issues mentioned above.

Automated summary

Remotely monitoring industrial printers for warning and error messages reduces equipment downtime, while directly tracking error message rates poses some issues. To overcome these issues, a nonparametric estimation method is used to classify and identify potential problems.