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

It ha s not been certified by p eer reviewers." 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 networ k is used to combine sensor information effectively, highli ghting 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. Deep learning is a field of machine learning with applications such as machine di agnosis. There is an increased interest in collecting large datasets for bearing and induction motor fault diagnosis. However, only a limited number of vibration and acoustic datasets are publicly available for constant-speed data collection. This paper proposes a methodology to ...