An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network

Shi, Li; Wang, Huaqing; Song, Liuyang; Wang, Pengxin; Cui, Lingli; Lin, Tianjiao

doi:10.1016/j.measurement.2020.108122

Cited by 93 publications

(39 citation statements)

References 31 publications

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“…However, most existing work in the area of CNN for multisensor feature fusion usage upsampling may make the vanish of certain time-frequency features, unless all sample rates are sufficiently close, and thus degrades the accuracy of fault diagnosis. In the multisensor system with different sampling rates, Li et al [18] present an improved information fusion framework based on the atrous convolution. Specifically, to avoid tedious preprocessing, the model extracts fault features from multisource signals by constructing a convolution kernel of adaptive size matching the data source channels.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, to extract the diagnosis information of the fused data effectively, one-dimensional CNN and global average pooling methods are adopted to improve the domain adaptation of the network. More details of FA-CNN can be found in [18] (4) MRSIFS: in comparison experiments, MRSIFS were tested on layers 1 to 4 to test the depth of influence of MRSIFS on diagnostic performance. In addition, to provide a fair comparison, all comparison models have the same model depth as the MRSIFS proposed…”

Section: Compared Modelsmentioning

confidence: 99%

“…The multirate sensor problem has become an urgent problem to be solved. However, in the previous paper [18], only the raw signals from the low-rate sensor are transferred through the upsampling network. As mentioned earlier, the existing deep learning model cannot solve the multirate sensor problem well in a complicated mechanical system with multiple components.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Compared Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Moreover, L in Input( ⋅ ) represents the length It is worth mentioning that a fully connected layer is connected behind the GAP layer in the designed CNN-GAP. While there are some existing methods in other fields [35,36], the GAP layer is directly used to replace the entire full connection layer. In such a case, the number of convolution kernels is equal to the number of classes.…”

Section: Validation Setup and The Structure Of The Cnn-gapmentioning

confidence: 99%

A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

et al. 2021

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This paper develops a novel soft fault diagnosis approach for analog circuits. The proposed method employs the backward difference strategy to process the data, and a novel variant of convolutional neural network, i.e., convolutional neural network with global average pooling (CNN-GAP) is taken for feature extraction and fault classification. Specifically, the measured raw domain response signals are firstly processed by the backward difference strategy and the first-order and the second-order backward difference sequences are generated, which contain the signal variation and the rate of variation characteristics. Then, based on the one-dimensional convolutional neural network, the CNN-GAP is developed by introducing the global average pooling technical. Since global average pooling calculates each input vector’s mean value, the designed CNN-GAP could deal with different lengths of input signals and be applied to diagnose different circuits. Additionally, the first-order and the second-order backward difference sequences along with the raw domain response signals are directly fed into the CNN-GAP, in which the convolutional layers automatically extract and fuse multi-scale features. Finally, fault classification is performed by the fully connected layer of the CNN-GAP. The effectiveness of our proposal is verified by two benchmark circuits under symmetric and asymmetric fault conditions. Experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

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“…Deep learning is a recent research direction in fault diagnosis of rotating machines. Li et al [20] developed a diagnosis method based on multisensory data fusion and a Convolutional Neural Network (CNN) for the health state classification of centrifugal pump faults. Zhao et al [2] proposed an automated diagnosis system by using deep learning and soft max regression analysis.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

et al. 2021

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Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and generally, statistical features are extracted from the vibration signals to retain meaningful fault information. However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults, thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT) is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault information in both the time and frequency domains. The CWT scales form two-dimensional timefrequency images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images (SGI). Over the past few decades, CNN models have been established as an effective practice to process images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed existing methods with an average performance improvement of 4.7 -15.6%.

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An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network

Cited by 93 publications

References 31 publications

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

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

A Novel Analog Circuit Soft Fault Diagnosis Method Based on Convolutional Neural Network and Backward Difference

A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning

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