A new Local-Global Deep Neural Network and its application in rotating machinery fault diagnosis

Abstract

Currently, it is a great challenge to effectively acquire more widespread equipment health information for guaranteeing safe production and timely fault maintenance in the process of industrial informationization. Aimed at the present issue that the conventional machine learning algorithm cannot juggle local and global fault feature information of rotating machinery, a novel fault diagnosis method based on Local-Global Deep Neural Network (LGDNN) algorithm is proposed in this paper. First of all, this fault diagnosis method can directly use the proposed LGDNN algorithm to extract local and global structural features from the original vibration spectral signals. Subsequently, the extracted high-level features are applied to classify different fault conditions by using soft-max classifier. Crucially, the core of this fault diagnosis method is that the anterior local layer of LGDNN utilizes a novel local feature extractor of the improved Convolutional Deep Belief Network (CDBN) based on the Fisher parameter optimization criterion (called Fisher-CDBN) to efficiently extract local discriminant information of data. Afterwards, its secondary global layer uses the global feature extractor of Kernel Principal Component Analysis (KPCA) to reduce the redundancy attribute of data. Ultimately, the vibration signals of the rolling bearing and the fan's gear are used to validate the effectiveness of the proposed method. And the experimental results demonstrate that the proposed algorithm and fault diagnosis method can juggle local and global fault feature information of rotating machinery. Besides, it also provides a novel research reference for deep learning fault diagnosis of rotating machinery.