Advances in Machine Learning for Sensing and Condition Monitoring

In order to overcome the complexities encountered in sensing devices with data collection, transmission, storage and analysis toward condition monitoring, estimation and control system purposes, machine learning algorithms have gained popularity to analyze and interpret big sensory data in modern industry. This paper put forward a comprehensive survey on the advances in the technology of machine learning algorithms and their most recent applications in the sensing and condition monitoring fields. Current case studies of developing tailor-made data mining and deep learning algorithms from practical aspects are carefully selected and discussed. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.

Automated summary

This paper presents a comprehensive survey on the advances in machine learning algorithms and their recent applications in the sensing and condition monitoring fields. It carefully selects and discusses case studies of developing tailor-made data mining and deep learning algorithms from practical aspects. The characteristics and contributions of these algorithms to the sensing and monitoring fields are elaborated.