Data-driven prognosis method using hybrid deep recurrent neural network
- Authors: Xia, Min , Zheng, Xi , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: Applied Soft Computing Journal Vol. 93, no. (2020), p.
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- Description: Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction. © 2020 Elsevier B.V.
SCA-LFD : side-channel analysis-based load forecasting disturbance in the energy internet
- Authors: Ding, Li , Wu, Jun , Li, Changlian , Jolfaei, Alireza , Zheng, Xi
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 70, no. 3 (2023), p. 3199-3208
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- Description: The energy Internet (EI) equipment may face threats that attackers poison federated learning (FL) models to disturb electricity load forecasting. To mitigate this vulnerability, it is important to study load forecasting disturbance approaches. This article proposes a side-channel analysis (SCA)-based disturbance approach. First, we design an FL SCA scheme to extract power information from the FL chip running forecasting model. Second, we propose an FL data speculation method using an optimized convolutional neural network trained with SCA information. Third, we design a label-flipping-based poisoning scheme with speculated data characteristics for load forecasting disturbance. Experimental results show attackers can successfully poison and disturb FL-based load forecasting. The average accuracy of EI load data speculation is 99.8%. This work is the first to study EI load forecasting disturbance from an SCA perspective. © 1982-2012 IEEE.