Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion

Abstract:Convolutional Neural Networks (CNNs) are used to evaluate accelerometer and microphone data for bearing and induction motor diagnosis. A Long Short-Term Memory (LSTM) recurrent neural network is used to combine sensor information effectively, highlighting the benefits of data fusion. This approach encourages researchers to focus on multi model diagnosis for constant speed data collection by proposing a comprehensive way to use deep learning and sensor fusion and encourages data scientists to collect more multi-sensor data, including acoustic and accelerometer datasets.

Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2506.11032 [cs.LG]

(or arXiv:2506.11032v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2506.11032 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.