scholarly journals Nearest Neighbor Convex Hull Tensor Classification for Gear Intelligent Fault Diagnosis Based on Multi-Sensor Signals

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 140781-140793 ◽  
Author(s):  
Zhengyang Cheng ◽  
Rongji Wang
Keyword(s):  
Fault Diagnosis ◽  
Convex Hull ◽  
Nearest Neighbor ◽  
Intelligent Fault Diagnosis ◽  
Sensor Signals

scholarly journals Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery

2021 ◽  
Vol 11 (3) ◽  
pp. 919
Author(s):  
Jiantao Lu ◽  
Weiwei Qian ◽  
Shunming Li ◽  
Rongqing Cui
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Vibration Signal ◽  
Reconstruction Algorithm ◽  
Nearest Neighbors ◽  
Health Condition ◽  
Rotating Machinery ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor ◽  
Case Based

Case-based intelligent fault diagnosis methods of rotating machinery can deal with new faults effectively by adding them into the case library. However, case-based methods scarcely refer to automatic feature extraction, and k-nearest neighbor (KNN) commonly required by case-based methods is unable to determine the nearest neighbors for different testing samples adaptively. To solve these problems, a new intelligent fault diagnosis method of rotating machinery is proposed based on enhanced KNN (EKNN), which can take advantage of both parameter-based and case-based methods. First, EKNN is embedded with a dimension-reduction stage, which extracts the discriminative features of samples via sparse filtering (SF). Second, to locate the nearest neighbors for various testing samples adaptively, a case-based reconstruction algorithm is designed to obtain the correlation vectors between training samples and testing samples. Finally, according to the optimized correlation vector of each testing sample, its nearest neighbors can be adaptively selected to obtain its corresponding health condition label. Extensive experiments on vibration signal datasets of bearings are also conducted to verify the effectiveness of the proposed method.

scholarly journals DC-NNMN: Across Components Fault Diagnosis Based on Deep Few-Shot Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Juan Xu ◽  
Pengfei Xu ◽  
Zhenchun Wei ◽  
Xu Ding ◽  
Lei Shi
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor ◽  
Vibration Data ◽  
Support Set ◽  
Unlabeled Sample ◽  
Testing Dataset ◽  
Bearing Vibration

In recent years, deep learning has become a popular topic in the intelligent fault diagnosis of industrial equipment. In practical working conditions, how to realize intelligent fault diagnosis in the case of the different mechanical components with a tiny labeled sample is a challenging problem. That means training with one component sample but testing with another component sample has not been resolved. In this paper, we propose a deep convolutional nearest neighbor matching network (DC-NNMN) based on few-shot learning. The 1D convolution embedding network is constructed to extract the high-dimensional fault feature. The cosine distance is merged into the K-Nearest Neighbor method to model the distance distribution between the unlabeled sample from the query set and labeled sample from the support set in high-dimensional fault features. The multiple few-shot learning fault diagnosis tasks as the testing dataset are constructed, and then the network parameters are optimized through training in multiple tasks. Thus, a robust network model is obtained to classify the unknown fault categories in different components with tiny labeled fault samples. We use the CWRU bearing vibration dataset, the bearing vibration data selected from the Lab-built experimental platform, and another gearing vibration dataset for across components experiment to prove the proposed method. Experimental results show that the proposed method can achieve fault diagnosis accuracy of 82.19% for gearing and 82.63% for bearings with only one sample of each fault category. The proposed DC-NNMN model provides a new approach to solve the across components fault diagnosis in few-shot learning.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

scholarly journals Research on Intelligent Fault Diagnosis of Servo Hydraulic Drawing Cushion

2021 ◽  
Vol 1966 (1) ◽  
pp. 012031
Author(s):  
Zikou Yu ◽  
Yongyong Duan ◽  
Zongling Wu ◽  
Yuhang Wang
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Fault Diagnosis
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