arXiv subjects

Yadong Zhang

Publications and source records attributed to Yadong Zhang.

25 records · Page 2Linked to original sources

arXiv2022

Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction

The precise estimate of remaining useful life (RUL) is vital for the prognostic analysis and predictive maintenance that can significantly reduce failure rate and maintenance costs. The degradation-related features extracted from the sensor streaming data with neural networks can dramatically improve the accuracy of the RUL prediction. The Temporal deep degradation network (TDDN) model is proposed to make the RUL prediction with the degradation-related features given by the one-dimensional convolutional neural network (1D CNN) feature extraction and attention mechanism. 1D CNN is used to extract the temporal features from the streaming sensor data. Temporal features have monotonic degradation trends from the fluctuating raw sensor streaming data. Attention mechanism can improve the RUL prediction performance by capturing the fault characteristics and the degradation development with the attention weights. The performance of the TDDN model is evaluated on the public C-MAPSS dataset and compared with the existing methods. The results show that the TDDN model can achieve the best RUL prediction accuracy in complex conditions compared to current machine learning models. The degradation-related features extracted from the high-dimension sensor streaming data demonstrate the clear degradation trajectories and degradation stages that enable TDDN to predict the turbofan-engine RUL accurately and efficiently.

cs.LG↗

arXiv2022

Fast Transient Stability Prediction Using Grid-informed Temporal and Topological Embedding Deep Neural Network

Transient stability prediction is critically essential to the fast online assessment and maintaining the stable operation in power systems. The wide deployment of phasor measurement units (PMUs) promotes the development of data-driven approaches for transient stability assessment. This paper proposes the temporal and topological embedding deep neural network (TTEDNN) model to forecast transient stability with the early transient dynamics. The TTEDNN model can accurately and efficiently predict the transient stability by extracting the temporal and topological features from the time-series data of the early transient dynamics. The grid-informed adjacency matrix is used to incorporate the power grid structural and electrical parameter information. The transient dynamics simulation environments under the single-node and multiple-node perturbations are used to test the performance of the TTEDNN model for the IEEE 39-bus and IEEE 118-bus power systems. The results show that the TTEDNN model has the best and most robust prediction performance. Furthermore, the TTEDNN model also demonstrates the transfer capability to predict the transient stability in the more complicated transient dynamics simulation environments.

eess.SY↗

arXiv2021

Cross-Linking of Doped Organic Semiconductor Interlayers for Organic Solar Cells: Potential and Challenges

Solution-processable interlayers are an important building block for the commercialization of organic electronic devices such as organic solar cells. Here, the potential of cross-linking to provide an insoluble, stable and versatile charge transport layer based on soluble organic semiconductors is studied. For this purpose, a photo-reactive tris-azide cross-linker is synthesized. The capability of the small molecular cross-linker is illustrated by applying it to a p-doped polymer used as a hole transport layer in organic solar cells. High cross-linking efficiency and excellent charge extraction properties of the cross-linked doped hole transport layer are demonstrated. However, at high doping levels in the interlayer, the solar cell efficiency is found to deteriorate. Based on charge extraction measurements and numerical device simulations, it is shown that this is due to diffusion of dopants into the active layer of the solar cell. Thus, in the development of future cross-linker materials, care must be taken to ensure that they immobilize not only the host, but also the dopants.

physics.app-ph↗

arXiv2021

High thermoelectric power factor of poly(3-hexylthiophene) through in-plane alignment and doping with a molybdenum dithiolene complex

Here we report a record thermoelectric power factor of up to 160 $μ$ W m-1 K-2 for the conjugated polymer poly(3-hexylthiophene) (P3HT). This result is achieved through the combination of high-temperature rubbing of thin films together with the use of a large molybdenum dithiolene p-dopant with a high electron affinity. Comparison of the UV-vis-NIR spectra of the chemically doped samples to electrochemically oxidized material reveals an oxidation level of 10%, i.e. one polaron for every 10 repeat units. The high power factor arises due to an increase in the charge-carrier mobility and hence electrical conductivity along the rubbing direction. We conclude that P3HT, with its facile synthesis and outstanding processability, should not be ruled out as a potential thermoelectric material.

physics.app-ph↗

arXiv2020

Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification

In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.

cs.LG↗

arXiv2020

Triad State Space Construction for Chaotic Signal Classification with Deep Learning

Inspired by the well-known permutation entropy (PE), an effective image encoding scheme for chaotic time series, Triad State Space Construction (TSSC), is proposed. The TSSC image can recognize higher-order temporal patterns and identify new forbidden regions in time series motifs beyond the Bandt-Pompe probabilities. The Convolutional Neural Network (ConvNet) is widely used in image classification. The ConvNet classifier based on TSSC images (TSSC-ConvNet) are highly accurate and very robust in the chaotic signal classification.

cs.LG↗

arXiv2020

Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification

Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed. Compared to other image representations of time series, MDF is simple and easy to construct. With the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the superior performance on the UCR time series dataset in benchmark with other time series classification methods. It is interesting to find that the triadic time series motifs give the best result in the test. Due to the motif clustering reflected in MDF, the significant motifs are detected with the help of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series. However, the signature patterns cannot be identified with the neural network classifiers directly based on the time series.

cs.LG↗