Rapid Deployment of Deep Learning-Based Condition Monitoring for Rotating Machines

Rotating machines are extremely common in many industries, and their maintenance involves substantial costs and labor. Most recent studies aiming to automate fault diagnosis have focused on deep learning, but industry adoption has been slow owing to the lack of well-curated datasets and the complexity of the methods. We propose a new method called Rapid Few-shot Condition Monitoring (Rapid-FSCM), which enables the rapid deployment of deep learning-based condition monitoring models and is readily extensible to future advancements in the field. This will make it simpler for the industry to conduct machine condition monitoring without the cost of an expert. Rapid-FSCM utilizes few-shot learning and the InceptionTime convolutional neural network to enable training on data from a related base domain more readily available than data from the target domain. In addition, the prototypical networks method for few-shot learning is modified to enable the deployment of the model as an anomaly detector, even before any fault samples have been recorded. After faults have occurred and been recorded, the model demonstrates the ability to initiate fault diagnosis without further retraining. Validated with three datasets, two gear datasets from a test bench with complex features, and the CWRU bearing dataset, the model was shown to have high accuracy in target domains containing unseen faults, sensors, operating conditions, and even entirely new components. The developed method can be used to rapidly deploy a condition monitoring model for any rotating machine without the need to first conduct a large data acquisition process.