Next Article in Journal
Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel Diversity
Previous Article in Journal
A Novel Racing Array Transducer for Noninvasive Ultrasonic Retinal Stimulation: A Simulation Study
Previous Article in Special Issue
Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive
Article Menu

Export Article

Open AccessArticle
Sensors 2019, 19(8), 1826;

Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling

1,2, 3 and 3,4,*
State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China
School of Software, Henan University, Kaifeng 475004, China
School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Department of Electrical Automation, Shanghai Maritime University, Shanghai 201306, China
Author to whom correspondence should be addressed.
Received: 26 January 2019 / Revised: 5 April 2019 / Accepted: 9 April 2019 / Published: 17 April 2019
(This article belongs to the Special Issue Soft Sensors)
PDF [1253 KB, uploaded 17 April 2019]


Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result in a problem that a very small number of samples with a complete structure can be used for deep learning since the input of a deep neural network (DNN) is required to be a structurally complete sample. On the other hand, a large number of samples are required to ensure the efficiency of deep learning based fault diagnosis methods. To solve the problem that a structurally complete sample size is too small, this paper proposes a fault diagnosis framework of missing data based on transfer learning which makes full use of a large number of structurally incomplete samples. By designing suitable transfer learning mechanisms, extra useful fault features can be extracted to improve the accuracy of fault diagnosis based simply on structural complete samples. Thus, online fault diagnosis, as well as an offline learning scheme based on deep learning of multi-rate sampling data, can be developed. The efficiency of the proposed method is demonstrated by utilizing data collected from the QPZZ- II rotating machinery vibration experimental platform system. View Full-Text
Keywords: fault diagnosis; DNN; transfer learning; missing data fault diagnosis; DNN; transfer learning; missing data

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Chen, D.; Yang, S.; Zhou, F. Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling. Sensors 2019, 19, 1826.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top