Light-Weight CNN Enabled Edge-Based Framework for Machine Health Diagnosis

Condition Based Monitoring (CBM) leverages sensor measurements for measuring state of health of an asset and autonomous diagnosis of faults to trigger remedial actions. Countless deep learning architectures are available for cloud-based feature engineering, feature extraction and classification of data for CBM models. However, complex models pose memory and processing speed constraints for edge implementation, while cloud-based computing poses high latency, high cost of data transmission and storage and privacy threats. This calls for a data driven machine health diagnosis system that is effective yet edge-compatible and secure. In this work, we present a model that is light-weight and non-redundant by optimizing the model size and complexity for edge implementation onto resource constrained, low-cost hardware. The model is based on a light-weight, edge implementable Convolutional Neural Network (CNN) algorithm that utilizes vibration sensor measurements for fault event estimation of machines. The model was trained and tested on two publicly available and widely studied vibration datasets for rolling element bearing faults. This CNN based classification system using spectrograms is able to achieve near perfect accuracy for both binary as well as multi-class fault classification. Lastly, the trained model was implemented and tested on a Raspberry Pi single-board computer, thus realizing an edge-compatible implementation which is cost-saving, secure and reliable.