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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Li, Ye Yang, Jingli Gao, Tianyu Wang, Zihan Department of Automatic Test and Control Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350354010

Mechanical equipment operating in extreme environments is prone to damage critical components, thus affecting its performance and remaining service life, which leads mechanical equipment diagnosis to be a focus. Data-driven fault diagnosis approaches have been recognized as the emerging research directions. However, they are limited by the fact that only few-sample unbalanced datasets can be applied in real industrial backgrounds, leading to serious overfitting and performance degradation. Therefore, an unbalanced few-sample fault diagnosis approach for mechanical equipment based on multi-scale class-weighted balanced network is proposed to improve the diagnosis performance by gradually resolving the quantitative and qualitative imbalance among classes. In the feature extraction stage, multi-scale feature fusion convolution module and channel class balancing mechanism are designed to enhance the ability of recognizing few classes. In the network optimization stage, the quantitative imbalance among classes and the accuracy imbalance within classes are firstly calculated to weight the cross-entropy loss, and then the triplet loss in metric learning is added, making it possible to ignore the quantitative and qualitative imbalance and guide the network to efficiently learn the features of all the classes. Experiments are conducted on the Case Western Reserve University bearing dataset to validate the effectiveness. The results show that even under the most extreme unbalanced few-sample condition, the average fault diagnosis accuracies of the proposed approach under all operating conditions are still above 94%, which are better than comparison approaches. © 2024 IEEE.

关键词： Bearings (machine parts)

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Fault diagnosis method of rotating machinery based on multi-sensor information decision-level fusion 14

Fault diagnosis method of rotating machinery based on multi-...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Yang, Jingli Huang, Yuxiang Zhang, Huiyuan Gao, Tianyu Department of Automatic Test and Control Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350301359

In order to improve the reliability and safety of rotating machinery systems, a fault diagnosis method based on consistency class weighted soft voting strategy is proposed in this paper, which consists of the basis classifier and decision fusion. The basis classifier includes a multi-scale feature extraction module and a classification module. In the first module, A multi-scale convolution kernel is employed to achieve the extraction and fusion of diverse features. To further improve the generalization performance, feature dimensionality reduction of the global average pooling layer is also introduced to effectively suppress noise interference and reduce overfitting. Then, the multi-scale features are input into the classification module to obtain the prediction label. Finally, a consistent category-weighted soft voting strategy is designed to perform decision fusion, which takes into account the difference in recognition ability of different basis classifiers for the same class while focusing on the difference in recognition ability of the base classifier for each fault class. By combining the probability distribution of prediction labels, the combined weights of different prediction labels are reasonably assigned. The fault dataset of a mechanical fault comprehensive simulation test-bed with Gaussian white noise is used to evaluate the method. The experimental results show that the proposed method is superior to other existing fault diagnosis methods for rotating machinery under noise interference, which proves that it has strong noise suppression ability. © 2023 IEEE.

关键词： fault diagnosis Information decision level fusion multisensor rotating machinery

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The Trimmed LASSO for Direction of Arrival Estimation by the Generalized Soft-Min Penalty 16

The Trimmed LASSO for Direction of Arrival Estimation by the...

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16th International Conference on Signal Processing and Communication System, ICSPCS 2023

作者： Bai, Longxin Zhang, Jingchao Fan, Meiyu Qiao, Liyan Harbin Institute of Technology Dept. Automatic Test and Control Harbin China

ISBN: (纸本)9798350333510

The trimmed lasso is a new nonconvex regularized approach for sparse signal recovery, which shows better estimation results in the single measurement vector problem. In this paper, we define the penalty term of the trimmed lasso in the multiple measurement vector model by £2,1-norm. We then present an approach to apply the trimmed lasso to on-grid based direction-of-arrival estimation problems by the alternating directions method of multipliers (ADMM) and majorization-minimization algorithm, which change the nonconvex objective to the convex weighted problem. Numerical simulations show that the proposed method improves the performance over £2,1 minimization algorithm. © 2023 IEEE.

关键词： Signal reconstruction

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A reliability analysis method for electronic systems based on survival signature 3

A reliability analysis method for electronic systems based o...

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3rd International Conference on Electrical, Electronics, and Information Engineering, EEIE 2024

作者： Xiao, Ziwen Peng, Yu Liu, Sizhe Liu, Yuhang Yang, Zhiming Yu, Yang Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9781510688179

Due to the complex structure and numerous electronic components, the reliability analysis of electronic systems has become more challenging. Therefore, we propose a reliability analysis method for electronic systems based on survival signature. The level of fault impact factors is evaluated based on the survival signature theory, and the weight of fault impact factors is evaluated through the analytic hierarchy process. This method simplifies the structural analysis process of complex systems, making reliability analysis more objective and efficient. The effectiveness of this method was verified through reliability analysis of the digital output control circuit. © The Authors.

关键词： analytic hierarchy process Electronic systems reliability analysis survival signature

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Fault Diagnosis Method for Rotating Machinery Based on Attention Adversarial Transfer Networks Under Variable Operating Conditions 14

Fault Diagnosis Method for Rotating Machinery Based on Atten...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Gao, Tianyu Yang, Cheng Yang, Jingli Department of Automatic Test and Control Harbin Institute of Technology Harbin China China Institute of Marine Technology & Economy Beijing China

ISBN: (纸本)9798350301359

Recently, deep learning-based fault diagnosis methods for rotating machinery have achieved promising classification performance under the independent and identical distribution of training and test data. However, the complex and variable operating conditions, including bearing speed, working load, and load torque, of rotating machinery in practical applications lead to the problem of inconsistent distribution between training and testing data. To address the above issue, an attentional adversarial transfer network is developed to improve the accuracy of rotating machinery fault diagnosis under variable operating conditions by using adversarial transfer learning techniques. First, a feature extractor based on self-normalized convolutional neural networks (SCNN) is constructed to provide effective features for different domains. A lightweight attention layer is further designed to reinforce the structural information in the features while reducing the redundant disturbances. Then, a discriminator based on probability calculation is employed to perform adversarial learning with the feature extractor so as to achieve the alignment of feature distribution between different domains. Finally, an efficient loss function is designed to drive efficient adversarial transfer learning for the whole model. To verify the effectiveness and superiority of the proposal, the Paderborn University (PU) bearing dataset is selected for fault diagnosis experiments, where proposed method achieves an average cross-domain fault diagnosis accuracy of 88.36%. The experimental results indicate that this approach can implement accurate fault identification for rotating machinery under variable operating conditions. © 2023 IEEE.

关键词： adversarial transfer learning attention mechanism fault diagnosis rotating machinery

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Face Feature Selection and Recognition using Separability Criterion and Binary Particle Swarm Optimization Algorithm

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Chinese Journal of Electronics 2025年第2期23卷 361-365页

作者： Hongtao Yin Ping Fu Zhen Sun Automatic Test and Control Institute Harbin Institute of Technology Harbin China

Discrete cosine transform (DCT) is an effective method to extract proper features for face recognition. Discrete cosine transform can only map the resource data to another data field instead of compress data. How to select the DCT coefficients that are most effective for classification is an important problem. This paper proposes a novel method to search the best discriminant combination of DCT coefficients. A feature selection algorithm according to the separability criterion is used to preselect the DCT coefficients, and then follows a search algorithm based on binary particle swarm optimization and support vector machine to find an optimal combination of the DCT coefficient. The performance of the algorithm is assessed by computing the recognition rate and the number of selected features on ORL database and Cropped Yale database.

关键词： Support vector machines Image recognition Databases Face recognition Aerospace electronics Feature extraction Vectors Classification algorithms Discrete cosine transforms Particle swarm optimization

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A Fault Feature Learning Model for Zero-shot Intelligent Diagnosis of Compound Faults in Rotating Machinery 14

A Fault Feature Learning Model for Zero-shot Intelligent Dia...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Yin, Shuangyan Yang, Jingli Tang, Qing Department of Automatic Test and Control Harbin Institute of Technology Harbin China China Institute of Marine Technology & Economy Beijing China

ISBN: (纸本)9798350301359

Due to the intricacy of rotating machinery and the interconnections among its systems, diagnosing compound faults in equipment poses a major challenge in this research area. Existing intelligent compound fault diagnosis methods rely on having sufficient compound fault data for training. However, obtaining complete compound fault data in industrial scenarios is an arduous task, and compound faults are often considered as unknown faults. This paper introduces a zero-shot intelligent diagnosis model that focuses on discriminating unknown compound faults. The method comprises four stages: data acquisition and preparation, fault features learning, fault semantics construction, and feature semantics mapping. Our primary focus is on the feature learning stage. The fault feature learning model of the zero-shot intelligent diagnosis method (FFL-ZID) incorporates a feature-level contraction block and a decision-level contraction block. The former filters valid information to extract essential features from the raw data, while the latter promotes feature decoupling to ensure independence among selected features and reduce redundancy. Experimental results on the mechanical comprehensive diagnostic simulation platform (MCDSP) demonstrate the effectiveness of the proposed method for identifying unknown compound faults in rotating machinery. © 2023 IEEE.

关键词： compound fault diagnosis fault feature learning rotating machinery zero-shot learning

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Class Incremental Learning Method for Hyperspectral Images Based on Real Data Playback Mechanism and Classification Network Optimization 15th

Class Incremental Learning Method for Hyperspectral Images B...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Liu, Xiaodong Wang, Siqi Liu, Huanyu Li, Junbao Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China School of Computer Science and Technology Information Countermeasure Technique Institute Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

In recent years, the hyperspectral image (HSI) classification has attracted great attention in the field of earth observation. With the expansion of application scenarios and the continuous improvement of application requirements, new classes of HSI continue to emerge. In the face of open and dynamic application scenarios, the model is required to be able to continuously learn new categories based on maintaining existing category knowledge. Class incremental learning algorithms have received extensive attention as key solutions to the problem above. In this paper, we use a regularization-based incremental learning algorithm, elastic weight consolidation (EWC) for class incremental learning for HSI classification. After data preprocessing to reduce data dimensions, we select new classes for research on class incremental learning. We combine 3D-2D convolutional structures and residual blocks as the classification network and use real data playback mechanism to improve class recognition accuracy and realize class incremental recognition of HSI data. We demonstrate the feasibility of the algorithm with extensive experiments based on three widely used hyperspectral datasets. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Cascaded Compensation of Filter Phase Imperfections in Random Demodulation 16

Cascaded Compensation of Filter Phase Imperfections in Rando...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Bian, Chenxi Zhang, Jingchao Li, Lin Qiao, Liyan Harbin Institute of Technology Dept. Automatic Test and Control Harbin China Navigation and Control Technology Research Institute of Norinco Group Beijing China

ISBN: (纸本)9798350327144

Analog-to-information converter (AIC) is an architecture that implements the compressive sensing (CS) theory and it is capable to generate compressed and digitized output directly from an analog input. Unfortunately, the hardware implementation of AIC inevitably faces the impact of non-ideal factors in the devices. The calibration link is crucial and can have a significant impact on the quality of the signal reconstruction at the back end of the AIC. Random Demodulation (RD) is one of the AICs with a simple structure, and its own structure and calibration link are relatively easy to implement. Low-pass filtering plays an important role in the RD analog acquisition process. In the back-end of the AIC, the impulse response of the low-pass filter in the time domain is constructed as part of the measurement matrix, which directly affects the accuracy of the signal reconstruction in the digital part of the AIC. Existing calibrations mostly focus on amplitude calibration, while the phase distortion of the low-pass filter reduces the accuracy of signal recovery in the RD, which is hardly considered in the calibrations of low-pass filters. To tackle this problem, a Cascaded compensation method of filter phase imperfections is designed, in which a specific all-pass filter is cascaded with the original phase distortion filter so that the phase of the cascaded filters is linear. Comparative simulation experiments were obtained for input signals with different sparsity. The results show that the Signal-to-Noise Ratio (SNR) is significantly improved by using the cascaded phase compensation method, which demonstrates that the proposed method is effective. © 2023 IEEE.

关键词： Demodulation

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Compressed Gridless Frequency Estimation by Segmented Atomic Norm Minimization for Random Demodulation 16

Compressed Gridless Frequency Estimation by Segmented Atomic...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Fan, Meiyu Han, Bingtong Zhang, Jingchao Qiao, Liyan Harbin Institute of Technology Dept. Automatic Test and Control Harbin China Beijing Institute of Radio Measurement Beijing China

ISBN: (纸本)9798350327144

The fixed-dictionary based gridded frequency sparse representation in random demodulation systems faces the grid mismatch problem, which may lead to severe degradation of the compression algorithm to the extent that high precision signal parameters and signal reconstruction cannot be obtained. Gridless compressed sensing introduces the concept of atomic norm, which solves the grid problem caused by spectrum discretization and greatly improves the accuracy of signal frequency estimation. However, in practice, solving large-scale meshless compressed sensing problems directly can consume a lot of time and storage resources. In this paper, we borrow the idea of segmented random sampling to compress the signal by generating segmented pseudo-random sequences in a random demodulation system. Then each segment of the compressed sampled signal is reconstructed and frequency estimated by atomic norm separately, and the obtained frequency estimates of several segments are averaged to obtain the frequency estimates of the original signal. The algorithm can achieve a recovery signal-to-noise ratio of 68 dB for a signal with 256 points per segment, and the recovery accuracy is only related to the number of points within the segment, independent of the number of segments. The algorithm reduces the computational complexity and improves the solution size and the accuracy of frequency estimation. © 2023 IEEE.

关键词： Demodulation

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