Toward Robust Fault Identification of Complex Industrial Processes Using Stacked Sparse-Denoising Autoencoder With Softmax Classifier
| Autor: | Jie Wang, Hadi Jahanshahi, Huazhan Yin, Zhaohui Tang, Tianyu Ma, Longcheng Xu, Weihua Gui, Jinping Liu, Yongfang Xie |
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| Rok vydání: | 2023 |
| Předmět: |
Hyperparameter
business.industry Computer science Deep learning Pattern recognition Autoencoder Computer Science Applications Human-Computer Interaction Control and Systems Engineering Robustness (computer science) Hyperparameter optimization Classifier (linguistics) Softmax function Feature (machine learning) Artificial intelligence Electrical and Electronic Engineering business Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 53:428-442 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2021.3109618 |
| Popis: | This article proposes a robust end-to-end deep learning-induced fault recognition scheme by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked spare-denoising autoencoder (SSDAE)-Softmax, for the fault identification of complex industrial processes (CIPs). Specifically, sparse denoising autoencoder (SDAE) is established by integrating a sparse AE (SAE) with a denoising AE (DAE) for the low-dimensional but intrinsic feature representation of the CIP monitoring data (CIPMD) with possible noise contamination. SSDAE-Softmax is established by stacking multiple SDAEs with a layerwise pretraining procedure, and a Softmax classifier with a global fine-tuning strategy. Furthermore, SSDAE-Softmax hyperparameters are optimized by a relatively new global optimization algorithm, referred to as the state transition algorithm (STA). Benefiting from the deep learning-based feature representation scheme with the STA-based hyperparameter optimization, the underlying intrinsic characteristics of CIPMD can be learned automatically and adaptively for accurate fault identification. A numeric simulation system, the benchmark Tennessee Eastman process (TEP), and a real industrial process, that is, the continuous casting process (CCP) from a top steel plant of China, are used to validate the performance of the proposed method. Experimental results show that the proposed SSDAE-Softmax model can effectively identify various process faults, and has stronger robustness and adaptability against the noise interference in CIPMD for the process monitoring of CIPs. |
| Databáze: | OpenAIRE |
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