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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Vydané v:	IEEE transactions on fuzzy systems Ročník 25; číslo 6; s. 1561 - 1575
Hlavní autori:	Zheng, Yu-Jun, Chen, Sheng-Yong, Xue, Yu, Xue, Jin-Yun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Yu-Jun Zheng Sheng-Yong Chen Yu Xue Jin-Yun Xue
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SubjectTerms	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
Title	A Pythagorean-Type Fuzzy Deep Denoising Autoencoder for Industrial Accident Early Warning
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