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
Open AccessArticle

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

by Peng Jiang 1,*, Zhixin Hu 1, Jun Liu 2, Shanen Yu 1 and Feng Wu 1
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
Sensors 2016, 16(10), 1695; https://doi.org/10.3390/s16101695
Received: 15 August 2016 / Revised: 27 September 2016 / Accepted: 6 October 2016 / Published: 13 October 2016
(This article belongs to the Section Sensor Networks)
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
Show Figures

Figure 1

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.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop