Description
The concept of Industry 4.0 provides promising approaches to reducedowntime and increase overall equipment efficiency in manufacturing processesthrough interconnected devices in the industrial internet of things (IIoT). As theprocurement of new IIoT-ready machines is costly, the retrofit of old machinescan be an idea worth exploring. In this paper, we designed a simple experimentsetup using affordable sensors and a coffee machine (due to the absence ofmachinery) to measure grinding vibrations and to predict the last coffee beforegrinder no-load. Microsoft Azure Machine Learning Studio was used to deploymachine learning techniques in order train prediction models. While predictionaccuracy in this experiment was non-satisfactory, our results nonetheless indicatethat retrofit is indeed a proper approach to make an older machine park smart,provided that sensors (especially their sample rate) are suitable for theapplication.
Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case
The concept of Industry 4.0 provides promising approaches to reducedowntime and increase overall equipment efficiency in manufacturing processesthrough interconnected devices in the industrial internet of things (IIoT). As theprocurement of new IIoT-ready machines is costly, the retrofit of old machinescan be an idea worth exploring. In this paper, we designed a simple experimentsetup using affordable sensors and a coffee machine (due to the absence ofmachinery) to measure grinding vibrations and to predict the last coffee beforegrinder no-load. Microsoft Azure Machine Learning Studio was used to deploymachine learning techniques in order train prediction models. While predictionaccuracy in this experiment was non-satisfactory, our results nonetheless indicatethat retrofit is indeed a proper approach to make an older machine park smart,provided that sensors (especially their sample rate) are suitable for theapplication.