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arXiv Machine Learning By Mert Sehri, Merve Ertargin, Ozal Yildirim, Ahmet Orhan, Patrick Dumond

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

Read the original on arXiv Machine Learning →

The paper presents a deep learning framework that uses Convolutional Neural Networks to analyze accelerometer and microphone data for diagnosing bearing and induction motor faults. It further employs a Long Short-Term Memory network to fuse these sensor streams, demonstrating the advantages of data fusion. The authors advocate for multi‑model diagnosis and encourage the collection of diverse multi‑sensor datasets, such as acoustic and accelerometer recordings, for constant‑speed data collection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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