A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning

Early warning is crucial for preventing industrial accidents and mitigating damage, but current methods are often time-consuming, error-prone, and incompetent to deal with uncertainty. This paper presents a fuzzy deep neural network for early warning of industrial accidents, which equips the classic...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 25; no. 6; pp. 1561 - 1575
Main Authors: Zheng, Yu-Jun, Chen, Sheng-Yong, Xue, Yu, Xue, Jin-Yun
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accident early warning
Accidents
Algorithms
Aluminum
Artificial neural networks
biogeography-based optimization (BBO)
Damage prevention
deep denoising autoencoder (DDAE)
deep learning
evolutionary learning
Explosions
Fuzzy logic
Fuzzy sets
Heuristic methods
Industrial accidents
Machine learning
Mathematical models
Neural networks
Noise reduction
Optimization
Order parameters
pythagorean fuzzy set (PFS)
Robustness (mathematics)
Uncertainty
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:Early warning is crucial for preventing industrial accidents and mitigating damage, but current methods are often time-consuming, error-prone, and incompetent to deal with uncertainty. This paper presents a fuzzy deep neural network for early warning of industrial accidents, which equips the classical deep denoising autoencoder (DDAE) model with Pythagorean-type fuzzy parameters in order to enhance the model's representation ability and robustness. To efficiently train the fuzzy deep model, we propose a hybrid algorithm combining Hessian-free optimization and biogeography-based optimization metaheuristic to balance global search and local search. Experiments on datasets from several industrial zones in China show that the proposed Pythagorean-type fuzzy DDAE (PFDDAE) can achieve much higher accuracy of accident risk classification than the classical DDAE and the fuzzy DDAE using regular fuzzy parameters, and the proposed hybrid learning algorithm exhibits significant performance advantage over some other learning algorithms in training PFDDAE. In particular, a test on the 2014 Kunshan aluminum dust explosion accident shows that the deep learning model would be very likely to prevent the accident if it was adopted in advance.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2017.2738605