ArticleSensors (Basel, Switzerland)2021
Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network.Sensors (Basel, Switzerland) · 2024Article
- Gearbox Fault Diagnosis Method Based on Multidomain Information Fusion.Sensors (Basel, Switzerland) · 2023Article
- An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions.Sensors (Basel, Switzerland) · 2022Article
- Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning.ACS omega · 2022Article
- Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals.Entropy (Basel, Switzerland) · 2022Article
- A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.Sensors (Basel, Switzerland) · 2022Article
- A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF.Sensors (Basel, Switzerland) · 2022Article
- A New Method of Image Classification Based on Domain Adaptation.Sensors (Basel, Switzerland) · 2022Article
- Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform.Sensors (Basel, Switzerland) · 2022Article
- A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment.Sensors (Basel, Switzerland) · 2022Article
- Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
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Abstract
Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.
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