An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Jing, Lu; Wang, Tao; Zhao, Ming; Wang, Peng

doi:10.3390/s17020414

Cited by 322 publications

(159 citation statements)

References 44 publications

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“…Through layer-by-layer extraction, deep neural networks extract different data features at different layers. Jing [13] proposed an adaptive multi-sensor data fusion method based on a deep convolutional neural network (DCNN) for fault diagnosis. The proposed method can learn features from raw data and adaptively optimize different fusions with combination of levels to meet the requirements of any troubleshooting task.…”

Section: Related Workmentioning

confidence: 99%

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Zhang

Fang-ai

et al. 2019

Future Internet

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Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.

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Section: Related Workmentioning

confidence: 99%

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Zhang

Fang-ai

et al. 2019

Future Internet

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“…It is evaluated by adding all of the values of a signal together, and divide it by N. the mean can be evaluated by using Eqn. (9).…”

Section: Meanmentioning

confidence: 99%

“…While this ends in a complicated process structure, which conceals process control, the in industry the occurrence Big Data massively eases process monitoring. Today from the key process variables the faults can be detected and diagnosed by using industrial Big Data [8,9]. A process fault happens least in a single perceived variable or the systems processed parameter when there is an unpermitted deviation and the controllers will not be able to reverse.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Keshari¹,

Krishna²

2019

IJITEE

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Intelligent fault detection is promising to deal with big data due to its ability in rapidly and efficiently processing collected signals and providing accurate detection results. In traditional fault detection methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise, such processes take advantage of human ingenuity but are time-consuming. Inspired by the idea of unsupervised feature learning artificial intelligence techniques are used to learn features from the raw data.As the dimensionality increases, the accuracy of fault identification methods implemented on big data decreases significantly. For supervised learning, large volume of data is needed which leads to high cost and time consuming. In this paper, an unsupervised learning approach is proposed on the basis of weighted softmax regression for fault detection using the power signals. In the proposed approach, the features are extracted from the unlabelled data. The developed approach is based on squirrel search algorithm and voting based weighted softmax regression with jaya optimization algorithm (IWSRJO_SSA). The proposed approach is simple and easy to carry out, though it attains high accuracy when compared to that of more advanced techniques. The features from the signals are extracted normally and some of the best features are selected using the squirrel search algorithm (SSA). Because of selecting the best features and using the improved weighted softmax regression, the proposed method achieves high accuracy in fault detection. Experiment on the power signal dataset shows the applicability of the proposed approach in fault detection on big data. The proposed system will be applicable on medical field, industrial field, electrical field and so on. The experimental results prove that the proposed method attains high accuracy and is superior to the other advanced methods.

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“…Regarding the estimation of T g , neural networks have attracted attention as a method of estimating a certain state from multiple sensor data. (13,14) A neural network has several intermediate layers between the input layer and the output layer, and each intermediate layer is composed of multiple nodes. As these nodes and layers increase in number, the variety of states that can be estimated becomes richer.…”

Section: Introductionmentioning

confidence: 99%

Method of Estimating Heatstroke Risk Using Wristwatch-type Device

Kaiho¹,

Takamatsu²,

Itoh³

2019

Sensors and Materials

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Keywords: heatstroke, WBGT, black globe temperature, neural network, wearable device As a method of estimating the risk of heatstroke with a wearable device, we have developed a method of calculating the wet bulb globe temperature (WBGT) by estimating the black globe temperature (T g ) only from sensors that can be mounted on a wristwatch-type device. In WBGT measurement, the conventional method requires a large sensor for measuring T g , and it has been difficult to grasp an individual's heatstroke risk. In this research, we proposed a method of estimating T g using a neural network and compared the estimation accuracy for different numbers of layers and nodes. In the T g range of 31 to 41 ℃, it was confirmed that when T g was estimated by the fully connected neural network of three layers and 20 nodes, the regression coefficient between the measured T g and the estimated T g was 0.90, indicating a high accuracy.

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An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

Cited by 322 publications

References 44 publications

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Method of Estimating Heatstroke Risk Using Wristwatch-type Device

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