The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines.

ABSTRACT: The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An ed

SUBMITTER: de Las Morenas J 

PROVIDER: S-EPMC10007525 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature