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Zhiyuan Xie

Bio: Zhiyuan Xie is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Deep learning & Support vector machine. The author has an hindex of 1, co-authored 2 publications receiving 7 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article, a joint distribution adaptation-based transfer network with diverse feature aggregation (JDFA) is proposed, where the diverse feature aggregates module is added to enhance feature extraction capability across large domain gaps.

29 citations

Journal ArticleDOI
TL;DR: A two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability.
Abstract: Remaining Useful Life (RUL) prediction is significant in indicating the health status of the sophisticated equipment, and it requires historical data because of its complexity. The number and complexity of such environmental parameters as vibration and temperature can cause non-linear states of data, making prediction tremendously difficult. Conventional machine learning models such as support vector machine (SVM), random forest, and back propagation neural network (BPNN), however, have limited capacity to predict accurately. In this paper, a two-phase deep-learning-model attention-convolutional forget-gate recurrent network (AM-ConvFGRNET) for RUL prediction is proposed. The first phase, forget-gate convolutional recurrent network (ConvFGRNET) is proposed based on a one-dimensional analog long short-term memory (LSTM), which removes all the gates except the forget gate and uses chrono-initialized biases. The second phase is the attention mechanism, which ensures the model to extract more specific features for generating an output, compensating the drawbacks of the FGRNET that it is a black box model and improving the interpretability. The performance and effectiveness of AM-ConvFGRNET for RUL prediction is validated by comparing it with other machine learning methods and deep learning methods on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and a dataset of ball screw experiment.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system.
Abstract: Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

54 citations

Journal ArticleDOI
13 Jan 2021
TL;DR: In this article, the authors developed a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions, which can be used for fault diagnostics.
Abstract: A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

54 citations

Journal ArticleDOI
TL;DR: A novel deep attention residual neural network (DARNN) is proposed by us for RUL prediction of machinery, which significantly surpassed some existing methods in prediction performance and self-stability.

30 citations

Journal ArticleDOI
Qin Hu, Xiaosheng Si, Aisong Qin, Yunrong Lv, Mei Liu 
TL;DR: This study proposes a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization based transfer learning, which can achieve an accurate diagnosis and outperform several existing transfer learning methods.
Abstract: In fault diagnosis field, inconsistent distribution between training and testing data, resulted from variable working conditions of rotating machinery, inevitably leads to degradation of diagnostic performance. To address this issue, this study proposes a novel fault diagnosis method based on enhanced multi-scale sample entropies and balanced adaptation regularization based transfer learning. Specifically, different statistics-based multi-scale sample entropies are used to improve feature discriminability for different fault patterns under each working condition and enhance similarity of fault information between different working conditions. Then, based on these hand-crafted features, an improved transfer learning algorithm, referred to as balanced adaptation regularization based transfer learning, simultaneously exploring balanced distribution adaptation and balanced label propagation, is utilized to learn an adaptive classifier to perform cross-domain fault diagnosis. Finally, two public rolling bearing datasets verify that the proposed method can achieve an accurate diagnosis and outperform several existing transfer learning methods.

16 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors used a parallel one-dimensional convolutional neural network (CNN) and the pooling layer to extract and fuse features from the multiple signals, and then the regression layer was designed to generate the remaining useful life (RUL).

16 citations