CNN-DST: ensemble deep learning based on Dempster-Shafer theory for vibration-based fault recognition

TL;DR

This paper introduces CNN-DST, an ensemble deep learning framework combining CNNs and Dempster-Shafer theory for vibration-based fault recognition, achieving high accuracy and robustness in classifying turbine blade damage.

Contribution

It proposes a novel ensemble CNN framework integrated with DST for improved fault classification and robustness in vibration data analysis.

Findings

Achieved 97.19% average prediction accuracy.

Demonstrated high noise resistance of the framework.

Most fault information is in a small frequency range.

Abstract

Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster-Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline Nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.