Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion
This work addresses fault diagnosis for industrial machinery maintenance, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles fault diagnosis in bearings and induction motors by using CNNs on accelerometer and microphone data, combined with an LSTM for sensor fusion, achieving effective multi-sensor analysis for constant speed conditions.
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.