Vibration-Based Condition Monitoring By Ensemble Deep Learning

TL;DR

This paper introduces an ensemble deep learning framework using CNNs and Dempster-Shafer theory for vibration-based condition monitoring of turbine blades, improving robustness and generalization over traditional methods.

Contribution

It proposes a novel ensemble deep learning approach with CNN diversity and fusion via Dempster-Shafer theory for enhanced condition monitoring.

Findings

Effective detection of blade conditions using the ensemble framework.

Improved generalization compared to single CNN models.

Successful application to real turbine blade data.

Abstract

Vibration-based techniques are among the most common condition monitoring approaches. With the advancement of computers, these approaches have also been improved such that recently, these approaches in conjunction with deep learning methods attract attention among researchers. This is mostly due to the nature of the deep learning method that could facilitate the monitoring procedure by integrating the feature extraction, feature selection, and classification steps into one automated step. However, this can be achieved at the expense of challenges in designing the architecture of a deep learner, tuning its hyper-parameters. Moreover, it sometimes gives low generalization capability. As a remedy to these problems, this study proposes a framework based on ensemble deep learning methodology. The framework was initiated by creating a pool of Convolutional neural networks (CNN). To create diversity to the CNNs, they are fed by frequency responses which are passed through different functions. As the next step, proper CNNs are selected based on an information criterion to be used for fusion. The fusion is then carried out by improved Dempster-Shafer theory. The proposed framework is applied to real test data collected from Equiax Polycrystalline Nickel alloy first-stage turbine blades with complex geometry.