Multisensor-Based Heavy Machine Faulty Identification Using Sparse Autoencoder-Based Feature Fusion and Deep Belief Network-Based Ensemble Learning. (21st June 2022)

• Record Type: Journal Article
• Authors: Zhou, Yiqing; Wang, Jian; Wang, Zeru
• Other Names: Zhou Mu Academic Editor.
• Is Part Of: Journal of sensors. Volume 2022(2022)
• Publication Date: 2022-06-21
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2022/5796505
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Faulty identification plays a vital role in the area of prognostic and the health management (PHM) of the industrial equipment which offers great support to the maintenance strategy decision. Owing to the complexity of the machine internal component-system structure, the precise prediction of the heavy machine is hard to be obtained, thus full of uncertainty. Moreover, even for a single component, the feature representation of the acquired conditional monitoring signal can be different due to the different deployment of the sensor location and environmental inference, causing difficulty in feature selection and uncertainty in faulty identification. In order to improve the model identification reliability, a novel hybrid machine faulty identification approach based on sparse autoencoder- (SAE-) and deep belief network- (DBN-) based ensemble learning is proposed in this paper. First, six kinds of statistical features are extracted and normalized from multiple sensors monitoring the same target component. Second, the six extracted features are fused by the two-stage SAE proposed in this paper from the sensor dimension and feature dimension, respectively. The composite feature fused in the feature dimension is regarded as the comprehensive representation of the corresponding component. Finally, the fused features containing comprehensive representation of different components are utilized to predict the machine health condition by the ensemble of multiple deep belief classifiers. The effectiveness of the proposed method is validated by the two case studies of wind turbine gearbox and industrial port crane. The experimental result shows that the proposed ensemble learning approach outperforms other traditional deep learning approaches in terms of the prediction accuracy and the prediction stability when dealing with multisensor feature fusion and the precise faulty identification of the industrial heavy machine.