Next Article in Journal
Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications
Previous Article in Journal
A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(10), 1695; doi:10.3390/s16101695

Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

1
College of Automation, Hangzhou Dianzi University, 310018 Hangzhou, China
2
State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, 310027 Hangzhou, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 15 August 2016 / Revised: 27 September 2016 / Accepted: 6 October 2016 / Published: 13 October 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [3058 KB, uploaded 13 October 2016]   |  

Abstract

Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. View Full-Text
Keywords: fault diagnosis; deep learning; deep neural network; active learning; big sensor data fault diagnosis; deep learning; deep neural network; active learning; big sensor data
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jiang, P.; Hu, Z.; Liu, J.; Yu, S.; Wu, F. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network. Sensors 2016, 16, 1695.

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

1

Comments

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