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Fault Detection based on Deep Learning for Digital VLSI Circuits

Procedia Computer Science 2021年 194卷 122-131页

作者： Lamya Gaber Aziza I. Hussein Mohammed Moness Computers and Systems Eng. Dept. Minia University Minia 61111 Egypt Electrical and Computer Eng. Dept. Effat University Jeddah 22332 Saudi Arabia

As growing complexity of digital VLSI circuits, fault detection and correction processes have been the most crucial phases during IC design. Many CAD tools and formal approaches have been used for debugging and localizing different kinds of design bugs. However, the search space explosion problem remains the main problem for IC designers. Recently, Artificial intelligence and machine learning models have been expanded in feature extraction and reduction models. In this paper, we introduce a new fault detection model based on deep learning for extracting features and detecting faults from large-sized digital circuits. The main goal of the proposed model is to avoid the search space using stacked sparse autoencoder, a specific type of artificial neural network. The model consists of three phases: test pattern generation using ATALANTA software, feature reduction using SSAE and classification for fault detection. Test vectors are utilized in SSAE as a training data for unsupervised learning phase. The performance of feature extraction is tested by changing the architecture of SSAE network and sparsity constraint. The proposed algorithm has been implemented using eight combinational digital circuits from ISCAS’85. From experimental results, the maximum fault coverage using ATALANTA tool delivers around 99.2% using ISCAS’85. In addition, the maximum validation accuracy of proposed SSAE model delivers around 99.7% in feature reduction phase.

关键词： Design Debugging sparse autoencoder ML Deep Learning

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Multi-lead model-based ECG signal denoising by guided filter

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 79卷 34-44页

作者： Hao, Huaqing Liu, Ming Xiong, Peng Du, Haiman Zhang, Hong Lin, Feng Hou, Zengguang Liu, Xiuling Hebei Univ Key Lab Digital Med Engn Hebei Prov Coll Elect & Informat Engn Baoding Peoples R China Hebei Univ Affiliated Hosp Baoding Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Chinese Acad Sci Inst Automat Beijing Peoples R China

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.

关键词： Electrocardiograph (ECG) denoising Multi-lead model-based ECG signal Guided filter sparse autoencoder

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Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis

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ISA TRANSACTIONS 2019年 95卷 306-319页

作者： Yang, Jing Xie, Guo Yang, Yanxi Zhang, Youmin Liu, Wei Xian Univ Technol Sch Automat & Informat Engn Jinhua South Rd Xian Shaanxi Peoples R China Concordia Univ 1455 de Maisonneuve Blvd W Montreal PQ H3G 1M8 Canada Tianshui Normal Univ Sch Mechatron & Automot Engn Xihe South Rd Tianshui Peoples R China

Currently, single fault diagnosis has received mass concern, and the related research achievements are remarkable. However, because of the mutual interaction of subsystems and the coupling of faults characteristics, the diagnosis of multiple intermittent faults commonly existing in industrial systems is still an intractable problem. In order to solve the problem, an improved Constrained sparse autoencoder integrated with Correlation Analysis (CA-CSAE) is proposed, further, a diagnosis scheme for multiple intermittent faults is formulated in this paper. The main strategies are as follows. (1) An adaptive loss function and a constraint for initial weight are designed to improve the diversity and accuracy of SAE feature learning. (2) A relational constraint term is constructed to mitigate the effect of data correlation. (3) The evaluation criterion of data correlation degree is put forward to quantify the scope of the method. (4) In order to improve the diagnostic efficiency, ReLU is introduced as the activation function of hidden layer, and L-BFGS algorithm is employed to obtain the optimal solution. (5) Softmax classifier is employed as the output layer to identify fault mode and ensure the reliability of diagnosis results. Finally, comparison experiments and results analysis are conducted to verify the effectiveness and practicability of the proposed method. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： sparse autoencoder Correlation analysis Intermittent faults Fault diagnosis

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An improved deep network for intelligent diagnosis of machinery faults with similar features

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第12期14卷 1851-1864页

作者： Yang, Jing Xie, Guo Yang, Yanxi Li, Xin Mu, Lingxia Takahashi, Sei Mochizuki, Hiroshi Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Nihon Univ Coll Sci & Technol Dept Comp Engn Funabashi Chiba 2748501 Japan

Currently, fault diagnosis, especially single fault diagnosis, has yielded fruitful research results. However, for concurrent faults, which exist more widely in real industrial systems. Due to the challenges of complex model mechanism, single and coupled monitoring data, as well as difficulty distinguished similar features, causing the existing research findings on concurrent faults have not yet achieved satisfactory effects. Considering the problem and in order to extract features automatically while enhancing the diagnostic accuracy, an improved sparse autoencoder (SAE) network for intelligent diagnosis is proposed. Specifically, the main contents are as follows: firstly, the original time-domain signals are preprocessed to eliminate the noise disturbance. The processed signals are then adopted as the input of diagnostic network. Secondly, an initial weight constraint and an adaptive loss function are constructed to improve the diversity of SAE feature learning. Thirdly, limited-memory quasi-newton algorithm (L-BFGS) algorithm is used to solve the optimal solution and the machinery health condition identification is achieved by softmax classifier. Finally, the verification experiments based on the monitoring data obtained from different machinery are conducted. The results demonstrate the effectiveness and practicability of the proposed method. (c) 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： deep learning sparse autoencoder concurrent faults similar features fault diagnosis

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An Improved Approach to Audio Segmentation and Classification in Broadcasting Industries

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JOURNAL OF DATABASE MANAGEMENT 2019年第2期30卷 44-66页

作者： Sun, Jingzhou Wang, Yongbin Commun China Sch Comp Sci & Cybersecur Beijing Peoples R China

Audio segmentation and classification are the basis of audio processing in broadcasting industries. A Dual-CNN (Dual-Convolutional Neural Network) method is proposed in this article in which it is possible to pre-train a CNN with unlabeled audio data so as to deal with the scarcity of labeled data. Auto-encoders (including an encoder and a decoder) are utilized, thus the name "Dual." In the first place, audio sampling points and the derived STFT (Short-Time Fourier Transform) spectrograms pass through their own CNNs. Fusion of the extracted features is then performed. Finally, the merged features are sent to a fully connected network and the classification results are produced via Softmax. Being one of the segmentation-by-classification approaches, our solution also presents a novel smoothing method (SEG-smoothing) in order to deliver the best result of segmentation. A series of experiments have been conducted and their result verifies that the proposed approach for segmentation and classification outperforms alternative solutions.

关键词： Classification-Oriented Audio Processing Dual-CNN Smoothing sparse autoencoder

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Identification of Partial Discharge Defects Based on Deep Learning Method

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IEEE TRANSACTIONS ON POWER DELIVERY 2019年第4期34卷 1557-1568页

作者： Duan, Lian Hu, Jun Zhao, Gen Chen, Kunjin He, Jinliang Wang, Shan X. Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Stanford Univ Ctr Magnet Nanotechnol Stanford CA 94305 USA

Since repetitive partial discharge (PD) leads to insulation breakdown, it is one of the most critical defects that affect operation life of electrical equipment. In this paper, four kinds of PDdefects are identifiedwith deep learning (DL) method according to the current waveforms. A modified IEC-60270 experiment platform with ultra-high frequency (UHF) and current probe is built to acquire PD current waveforms and their corresponding detecting pulse current and UHF pulse signal. Fourier transform, principle component analysis, and t-distributed stochastic neighbor embedding methods are applied to visualize the data set, which proves the feasibility of classifying the PD current waveform. Two basic parts of this DL framework are sparse autoencoder layer and softmax layer, the former extracting features of the input signal and the latter operating as the classifier. Hyper-parameters of the network such as sparsity, activation function, number of hidden nodes, and network depth were discussed. The final classifying accuracy of the proposed method is up to 99.7%, that is much better than the traditional identifying method. A comprehensive blind test is designed to prove the general validity and robustness of the proposed model.

关键词： Partial discharge deep learning sparse autoencoder softmax feature extraction

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Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2019年第5期233卷 719-730页

作者： Qu, Yongzhi Zhang, Yue He, Miao He, David Jiao, Chen Zhou, Zude Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Hubei Peoples R China Univ Illinois Dept Mech & Ind Engn Chicago IL USA

Effective feature extraction is critical for machinery fault diagnosis and prognosis. The use of time-frequency features for machinery fault diagnosis has prevailed in the last decade. However, more attentions have been drawn to machine learning-based features. While time-frequency domain features can be directly correlated to fault types and fault levels, data-driven features are typically abstract representations. Therefore, classical machine learning approaches require large amount of training data to classify these abstract features for fault diagnosis. This article proposed a fully unsupervised feature extraction method for "meaningful" feature mining, named disentangled tone mining. It is shown that disentangled tone mining can effectively extract the hidden "trend" associated with machinery health state, which can be used directly for online anomaly detection and prediction. Compared with wavelet transform and time domain statistics, disentangled tone mining can better extract fault-related features and reflect the fault degradation process. Shallow methods, such as principal component analysis, multidimensional scaling and single-layer sparse autoencoder, are shown to be inferior in terms of disentangled feature learning for machinery signals. Simulation analysis is also provided to demonstrate and explain the potential mechanism underlying the proposed method.

关键词： Gear fault pitting trending analysis deep learning wavelet analysis sparse autoencoder

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Network Anomaly Detection using Threshold-based sparse 20

Network Anomaly Detection using Threshold-based Sparse

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Proceedings of the 11th International Conference on Advances in Information Technology

作者： May Thet Tun Dim En Nyaung Myat Pwint Phyu University of Information Technology Yangon Myanmar

ISBN: (纸本)9781450377591

Nowadays, cyber-attacks have been dramatically increased due to the rapid development of Internet-based services. The current network anomaly detection solutions such as Firewall, Snort, honeypots are useful, but they are insufficient to deal with zero-day attacks. At present, unsupervised deep learning technologies focus on increasing the performance of anomaly-based network attack detection in the recent research area. The normal and attack transactions can be distinguished by the threshold in the network anomaly detection using a sparse autoencoder. The threshold value estimation is not a non-trivial task. The key factor of the threshold value which draws a line between normal and attack highly depends on the accuracy of the network anomaly detection. In this paper, the network anomaly detection based on a sparse autoencoder with a percentile-based threshold selection algorithm is proposed. The results of the proposed system have been validated concerning the accuracy, precision, recall and false positive rate. As a result, the experimental results on UNSW-NB15 and NSL-KDD datasets have shown that the proposed system provides higher accuracy and outperforms the previous related works in recent years.

关键词： Chi-squared feature selection Deep learning Network anomaly detection Percentile threshold approach sparse autoencoder

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Locally weighted embedding topic modeling by markov random walk structure approximation and sparse regularization

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NEUROCOMPUTING 2018年 285卷 35-50页

作者： Wei, Chao Luo, Senlin Pan, Limin Wu, Zhouting Zhang, Ji Safi, Qamas Gul Khan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Engn & Technol Dept Comp Sci Taxila 47050 Punjab Pakistan

Topic model is a practical method for learning interpretable models of text corpora and have become a key problem of document representation. Some recently proposed topic models incorporate the intrinsic geometrical information of the document manifold and yield a discriminative topic representation. However, the existing manifold-inspired topic models fail to provide the probability weighting information of local geometrical pattern, thus leads to a limitation to estimate intrinsic semantic information of topic representation. In this paper, we consider the problem of topic modeling with intrinsic structure of document manifold and propose an unsupervised autoencoder-based topic modeling framework, named locally weighted embedding topic model (LWE-TM). Different from existing manifold-inspired topic models, LWE-TM defines a group of probability coefficients to uncover the local geometrical pattern by the Markov random walk structure of affinity graph, and regularizes the training of sparse autoencoder (sAE) to explicitly recover such local geometrical pattern with the topics encoding. Under the regularized training framework, the encoding network becomes local-invariant around the neighborhood of the document manifold and enable us to perform a readily topic inference for out-of-sample documents, efficiently improving the generalization and discrimination of topics encoding. The experimental results on two widely-used corpus demonstrate the superiority of LWE-TM to comparative models in document modeling, document clustering and classification tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Topic model sparse autoencoder Markov random walk Affine mapping

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DISTRIBUTION PRESERVING NETWORK EMBEDDING 44

DISTRIBUTION PRESERVING NETWORK EMBEDDING

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Qin, Anyong Shang, Zhaowei Zhang, Taiping Tang, Yuan Yan Chongqing Univ Chongqing Peoples R China Univ Macau Macau Peoples R China

ISBN: (纸本)9781479981311

The deep autoencoder network which is based on constraining non-negative weights, can learn a low dimensional part-based representation. On the other hand, the inherent structure of the each data cluster can be described by the distribution of the intraclass sample. Then one hopes to learn a new low dimensional feature which can preserve the intrinsic structure embedded in the high dimensional data space perfectly. In this paper, by preserving data distribution, a deep part-based representation can be learned, and the novel algorithm is called Distribution Preserving Network Embedding (DPNE). In DPNE, we first need to estimate the distribution of the original data, and then we seek a part-based representation which respects the distribution. The experimental results on real-world data sets show that the proposed algorithm has good performance in terms of cluster accuracy and adjusted mutual information (AMI).

关键词： Distribution preserving manifold structure part-based representation sparse autoencoder clustering

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