Deep residual learning-based fault diagnosis method for rotating machinery.

Area of Science:

• Mechanical Engineering
• Artificial Intelligence
• Signal Processing

Background:

• Data-driven fault diagnosis for rotating machinery is crucial for industrial applications.
• Deep neural networks offer high accuracy but face training challenges like vanishing/exploding gradients.
• Processing long vibration signal sequences requires deep models with large capacity.

Purpose of the Study:

• To propose a novel residual learning algorithm to enhance deep neural network training for rotating machinery fault diagnosis.
• To improve information flow within deep models for processing variable-length vibration signals.
• To facilitate industrial applications by reducing the need for extensive expertise.

Main Methods:

• Implementation of a residual learning algorithm within a deep neural network architecture.
• Processing of machinery vibration signals with variable sequential lengths.
• Validation using experiments on a standard rolling bearing dataset.

Main Results:

• The proposed architecture significantly improves information flow, aiding network training.
• The method is well-suited for analyzing machinery vibration signals.
• Experimental results demonstrate the effectiveness of the proposed intelligent fault diagnosis approach.

Conclusions:

• The residual learning algorithm offers a promising new approach for intelligent fault diagnosis in rotating machinery.
• The method enhances model performance and facilitates practical industrial implementation.
• This technique addresses key challenges in training deep learning models for vibration signal analysis.