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

TL;DR

This paper presents a deep learning framework combining CNNs and LSTMs for fault diagnosis in bearings and induction motors, emphasizing the advantages of multi-sensor data fusion for improved diagnostic accuracy.

Contribution

It introduces a novel data fusion approach using CNNs and LSTMs to enhance fault diagnosis in motors, promoting multi-sensor data collection and analysis.

Findings

Effective sensor data fusion improves diagnosis accuracy.

Combining accelerometer and microphone data enhances fault detection.

Encourages multi-sensor data collection for better diagnostics.

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.