Detection of Pitting in Gears Using a Deep Sparse Autoencoder

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

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In this paper; a Toiletry Bags new method for gear pitting fault detection is presented.The presented method is developed based on a deep sparse autoencoder.The method integrates dictionary learning in sparse coding into a stacked autoencoder network.Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis.An autoencoder is an unsupervised machine learning technique.

A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network.The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically.These features are then used to perform gear Hockey Protective - Shoulder Pads - Intermediate pitting fault detection.The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.