Today, the improvement of the product value in consumer goods, such as new services to increase the positive customer experience, is the subject of many research activities. In a context where the product complexity becomes ever greater and the product life-cycle is always shorter, the use of intelligent tools for supporting all phases of the product life-cycle is very important. One of the aspects that is taking interest is to support the consumer in fault management. This analysis are well-known practices in the industrial, automotive fields, etc. but less used for consumer electronics. This paper analizes a Cloud service based on a Machine Learning (ML) approach used to provide fault detection capabilities to household appliances equipped with electric motors and compare the results with on premise ML algorithms provided research tools. The purpose of this paper is to perform a preliminary comparison of ML algorithm performances provided by two software, namely Microsoft Azure (cloud solution) and MATLAB (on premise solution), on a study case. In detail, the vibration data of an asynchronous motor installed in an oven extractor hood for commercial restaurant kitchen have been analyzed. To this end, two classification algorithms have been selected to implement fault diagnosis techniques.
Prist, M., Longhi, S., Monteriu, A., Freddi, A., Pallotta, E., Ciabattoni, L., et al. (2020). Machine learning-as-a-service for consumer electronics fault diagnosis: A comparison between matlab and azure ML. In Digest of Technical Papers - IEEE International Conference on Consumer Electronics (pp.1-5). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICCE46568.2020.9043014].