An improved Multi-channels Information Fusion Model with multi-scale signals for fault diagnosis

Ma, Xiangkai; Wang, Pei; Zhang, Bozhou; Sun, Ming

doi:10.1109/icbaie52039.2021.9389944

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…End-to-End Multisensor Model. Despite existing methods based on CNN have achieved the breakthrough performance in detection and diagnosis [21][22][23], there are still some shortcomings that can be improved: multirate information feature fusion can extract fault sensitive and complementary features, which are not contained in a single sensor signal, thus achieving higher accuracy and stability of the complex mechanical system [24,25]. Nevertheless, most existing methods only used a single sensor, and few researchers attempted the multirate sensor information fusion for diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A Multirate Sensor Information Fusion Strategy for Multitask Fault Diagnosis Based on Convolutional Neural Network

Wang

Zhang

et al. 2021

Journal of Sensors

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In complicated mechanical systems, fault diagnosis, especially regarding feature extraction from multiple sensors, remains a challenge. Most existing methods for feature extraction tend to assume that all sensors have uniform sampling rates. However, complex mechanical systems use multirate sensors. These methods use upsampling for data preprocessing to ensure that all signals at the same scale can cause certain time-frequency features to vanish. To address these issues, this paper proposes a Multirate Sensor Information Fusion Strategy (MRSIFS) for multitask fault diagnosis. The proposed method is based on multidimensional convolution blocks incorporating multisource information fusion into the convolutional neural network (CNN) architecture. Features with different sampling rates from the raw signals are run through a multichannel parallel fault feature extraction framework for fault diagnosis. Additionally, time-frequency analysis technology is used to reveal fault information in the association between time and frequency domains. The simulation platform’s experimental results show that the proposed multitask model achieves higher diagnosis accuracy than the existing methods. Furthermore, manual feature selection for each task becomes unnecessary in MRSIFS, which has the potential toward a general-purpose framework.

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