IEEE Transactions on Instrumentation and Measurement最新文献_第4页

Impact of Insulation Degradation Length, Severity, and Boundary on Cable Defect Localization Using Broadband Impedance Spectrum 利用宽带阻抗谱分析绝缘退化长度、严重程度和边界对电缆缺陷定位的影响

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-20 DOI: 10.1109/TIM.2025.3581621

Yaqiang Deng;Bo Zhang

{"title":"Impact of Insulation Degradation Length, Severity, and Boundary on Cable Defect Localization Using Broadband Impedance Spectrum","authors":"Yaqiang Deng;Bo Zhang","doi":"10.1109/TIM.2025.3581621","DOIUrl":"https://doi.org/10.1109/TIM.2025.3581621","url":null,"abstract":"Insulation degradation is a common type of minor defect in cables and often exhibits a large spatial scale. Broadband impedance spectrum (BIS), as a frequency-domain reflectometry (FDR) technique, is effective for diagnosing and localizing such defects. However, most existing studies assume concentrated defects and overlook the effects of defect length, severity, and boundary, which are critical in the context of insulation degradation. This article systematically investigates these factors in coaxial cables through rigorous theoretical derivation, supported by simulation validation. The results show that while the location of the defect start remains unaffected, the localization of the defect end is shifted. This shift increases linearly with defect length and severity and further propagates to subsequent discontinuities along the cable. Moreover, defect boundaries introduce additional shifts, which are related to the integral of the relative permittivity variation across the boundary region. The boundary also reduces the amplitude and broadens the width of the associated localization peaks. The applicability of these conclusions to coaxial power cables is discussed, and a method is proposed for identifying long spatial-scale defects based on BIS measurements from both cable terminals. These findings provide theoretical insights and practical guidance, serving as a supplement to existing localization methods based on BIS.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Ordinary Differential Equations With Sparse Attention for Power Load Forecasting 基于稀疏关注的常微分方程积分的电力负荷预测

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-20 DOI: 10.1109/TIM.2025.3581667

Jiacheng Li;Wei Chen;Yican Liu;Junmei Yang;Zhiheng Zhou;Delu Zeng

{"title":"Integrating Ordinary Differential Equations With Sparse Attention for Power Load Forecasting","authors":"Jiacheng Li;Wei Chen;Yican Liu;Junmei Yang;Zhiheng Zhou;Delu Zeng","doi":"10.1109/TIM.2025.3581667","DOIUrl":"https://doi.org/10.1109/TIM.2025.3581667","url":null,"abstract":"Accurate load forecasting plays an essential role in the measurement, monitoring, and control frameworks of modern power systems, particularly given the continuous influx of high-resolution data from advanced metering devices. Traditional forecasting methods often struggle due to the inherent nonstationarity and multiscale dynamics observed in these data streams. To address these challenges, this article introduces EvolvInformer, a novel long-sequence forecasting framework that integrates ordinary differential equations (ODEs) solver within a ProbSparse self-attention decoder architecture. The ODE module provides a physics-inspired, continuous-time representation of hidden state dynamics, enabling the model to capture subtle fluctuations and abrupt regime shifts commonly found in instrumented load profiles. Comprehensive experiments conducted on five large-scale power load datasets demonstrate that EvolvInformer achieves a 29.7% reduction in mean-squared error (mse) compared to state-of-the-art baseline models while preserving the logarithmic memory complexity characteristic of ProbSparse attention. Moreover, EvolvInformer consistently models both global trends and localized transient phenomena under stringent computational constraints, making it particularly suitable for embedded and edge-based metering applications. By effectively coupling continuous-time modeling via ODE with an efficient sparse attention mechanism for long-sequence forecasting, EvolvInformer provides a robust and scalable solution for measurement-centric load prediction tasks, with broad potential applications in adaptive energy management, grid load forecasting, and metering data quality assessment.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid Mueller Matrix Holographic Microscopy Imaging for Polarization Sensitive Materials 偏振敏感材料的快速穆勒矩阵全息显微成像

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-19 DOI: 10.1109/TIM.2025.3580876

Xintian Yu;Lei Liu;Zhi Zhong;Lei Yu;Qing Dong;Bei Lu;Nan Li;Mingguang Shan

{"title":"Rapid Mueller Matrix Holographic Microscopy Imaging for Polarization Sensitive Materials","authors":"Xintian Yu;Lei Liu;Zhi Zhong;Lei Yu;Qing Dong;Bei Lu;Nan Li;Mingguang Shan","doi":"10.1109/TIM.2025.3580876","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580876","url":null,"abstract":"Mueller matrix polarimetry (MMP) is a powerful technique employed in various fields, such as biomedical optics, material science, and remote sensing. However, existing MMP techniques typically require multiple exposures (12 or more), which compromises measurement efficiency and increases susceptibility to errors. In this study, a rapid Mueller matrix holographic microscopy (RMHM) was proposed for extracting the complete <inline-formula> <tex-math>$4times 4$ </tex-math></inline-formula> Mueller matrix (MM) of polarization-sensitive materials. Based on an off-axis digital holography (DH) interferometer, the geometric phase is determined to reconstruct the MM using the Pancharatnam-Berry (PB) phase theory and a division algorithm. Our method retains the advantages of existing DH techniques, requiring only three acquisitions to capture the complete MM. The proposal is validated through the application of a rotating quarter-wave plate (QWP), followed by the measurement of polarization parameters including circular and linear retardance, depolarization, and the birefringent fast-axis angle. The analysis covers various materials, such as plant roots, potato starch granules, and pathological lung cancer tissues.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Variable-Step Multiscale Extended Dispersion Entropy-Based Lempel–Ziv Complexity and Its Application in Fault Diagnosis 基于多元变步多尺度扩展色散熵的Lempel-Ziv复杂度及其在故障诊断中的应用

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-19 DOI: 10.1109/TIM.2025.3580860

Yuxing Li;Xuanming Cheng;Junxian Wu;Yan Yan

{"title":"Multivariate Variable-Step Multiscale Extended Dispersion Entropy-Based Lempel–Ziv Complexity and Its Application in Fault Diagnosis","authors":"Yuxing Li;Xuanming Cheng;Junxian Wu;Yan Yan","doi":"10.1109/TIM.2025.3580860","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580860","url":null,"abstract":"Extended dispersion entropy-based Lempel–Ziv complexity (EDELZC) can measure the irregularity or chaos of single-channel time series, which is one of the ideal tools for extracting fault features from rotating machinery. However, EDELZC is only suitable for single-scale and single-channel time-series analysis, which affects the effective extraction of fault features. To solve this problem, the multivariate embedding and variable-step multiscale techniques are integrated, and the multivariate variable-step multiscale EDELZC (MvVSMEDELZC) is developed, which achieves the characterization of multichannel feature information at different time scales. Moreover, in order to improve the recognition accuracy, the crayfish optimization algorithm (COA) is applied to optimize the parameters of the kernel extreme learning machine (KELM), and a new fault diagnosis method is proposed in combination with MvVSMEDELZC. The simulated signal experiments verify the ability of MvVSMEDELZC to detect dynamic changes in complex signals. The practical rotating machinery fault diagnosis experiments show that compared with other methods, the proposed fault diagnosis method offers superior accuracy and efficiency in identifying the condition of bearings and gears, which indicates its superior performance in properties in diagnosing rotating machinery faults.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Test Optimization Selection for Fault Detection and Isolation Under Multivariable and Multifault Scenarios 多变量多故障场景下故障检测与隔离的试验优化选择

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-19 DOI: 10.1109/TIM.2025.3579828

Xiuli Wang;Dongdong Xie;Defeng He;Yang Li;Hongtian Chen;Haowei Wang

{"title":"Test Optimization Selection for Fault Detection and Isolation Under Multivariable and Multifault Scenarios","authors":"Xiuli Wang;Dongdong Xie;Defeng He;Yang Li;Hongtian Chen;Haowei Wang","doi":"10.1109/TIM.2025.3579828","DOIUrl":"https://doi.org/10.1109/TIM.2025.3579828","url":null,"abstract":"Test optimization selection (TOS) is a crucial technology in testability design, playing a key role in intelligent manufacturing by enhancing product maintainability and reliability while reducing life-cycle costs. As intelligent manufacturing systems demand higher reliability and efficiency, effective TOS methods are essential for ensuring real-time fault diagnosis and predictive maintenance. However, existing TOS methods inadequately account for correlations between test outcomes in metrics modeling and offer limited solutions to the low fault isolation rate (FIR) caused by multiple faults. An innovative TOS approach is developed by considering fault detection rate (FDR) and FIR metrics via the D-vine copula and Bhattacharyya coefficient method, along with an improved binary particle swarm optimization (DVBC-IBPSO) method to minimize the number of required test points. First, the D-vine copula method is introduced to model test metrics, effectively capturing strong correlations between test outcomes. Second, considering the ambiguity group problem induced by multiple faults, a DVBC combined method is developed to quantify the similarity between fault distributions and model the FIR metric. Third, leveraging the constructed test metrics models, an IBPSO algorithm is employed by incorporating a newly designed objective function that selects the most cost-effective test points while ensuring FDR and FIR remain within acceptable thresholds. The proposed method enhances the reliability and efficiency of intelligent manufacturing systems by optimizing fault diagnosis processes and improving overall system health management. Its validity is established through experimental studies on one commonly used critical circuit in industrial systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bionic Seal Whisker Triboelectric Sensor for Underwater Multiobject Wake Perception 水下多目标尾迹感知仿生海豹须摩擦电传感器

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-19 DOI: 10.1109/TIM.2025.3580836

Jianhua Liu;Siyuan Wang;Yuanzheng Li;Ziyue Xi;Hao Jin;Peng Xu;Minyi Xu

{"title":"Bionic Seal Whisker Triboelectric Sensor for Underwater Multiobject Wake Perception","authors":"Jianhua Liu;Siyuan Wang;Yuanzheng Li;Ziyue Xi;Hao Jin;Peng Xu;Minyi Xu","doi":"10.1109/TIM.2025.3580836","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580836","url":null,"abstract":"Existing underwater flow field sensing techniques encounter significant challenges in complex and variable flow environments. Seals possess a highly sensitive whisker sensing system that enables them to perform tasks such as predation and environment sensing. Drawing inspiration from the hydrodynamic tactile function of seal whiskers, this article introduces a bionic whisker triboelectric sensor (BWTS) that integrates whisker-based sensing mechanisms with triboelectric nanogenerator technology. The BWTS features a wavy bionic whisker and a flexible bionic follicle structure embedded with four sensing units. It is verified through simulation and experimental analysis that the BWTS can effectively capture the wake field characteristics of stationary and moving underwater objects under different flow field parameters. The BWTS demonstrates high reliability, achieving correlation coefficients of 0.98–0.99 for the geometrical and kinematic parameters of underwater objects. The error is less than 10%. Additionally, its strong directional recognition and flow field feature sensing capabilities have been validated. As a noncontact underwater flow field sensing technology, BWTS will provide an innovative approach to enhance the sensing capability of underwater vehicles.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144500956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multifault Feature Wasserstein Generative Adversarial Networks for Fault Diagnosis in Unbalanced Data 多故障特征Wasserstein生成对抗网络在非平衡数据中的故障诊断

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-18 DOI: 10.1109/TIM.2025.3580880

Weibo Ren;Zhijian Wang;Zhongxin Chen;Shun Zhao;Lei Dong;Yanfeng Li;Xin Fan

{"title":"Multifault Feature Wasserstein Generative Adversarial Networks for Fault Diagnosis in Unbalanced Data","authors":"Weibo Ren;Zhijian Wang;Zhongxin Chen;Shun Zhao;Lei Dong;Yanfeng Li;Xin Fan","doi":"10.1109/TIM.2025.3580880","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580880","url":null,"abstract":"Due to the limitation of industrial conditions in production, raw sensor data are always shown as an unbalanced dataset, characterized by abundant normal operational data and scarce fault instances. This unbalance can degrade the performance of conventional fault diagnosis methods, leading to reduced accuracy and unstable model training. To address this challenge in bearing fault diagnosis, this article proposes a multifault feature Wasserstein generative adversarial network (MFF-WGAN) to enhance diagnostic precision. First, the framework employs a multiencoder denoising autoencoder (DAE) architecture to mitigate noise interference in raw sensor data. Subsequently, the proposed MFF-WGAN integrates label information into its adversarial loss function to enable simultaneous generation of diverse fault categories, while incorporating interclass feature discrepancies to refine sample quality. Finally, the developed multifault feature Wasserstein generation adversarial network is tested on the Case Western Reserve University bearing dataset and the laboratory bearing dataset. Computational results show that the proposed method can generate high-quality bearing samples with multiple faults effectively, which can obtain a higher diagnosis accuracy of 99.01% and 97.71% compared with the existing methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Distribution Alignment via Mutual Information for Cross-Device Fault Diagnosis 基于互信息的跨设备故障诊断联合分布对齐

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-18 DOI: 10.1109/TIM.2025.3580817

Lexuan Shao;Ningyun Lu;Bin Jiang;Jianhua Lv;Silvio Simani

{"title":"Joint Distribution Alignment via Mutual Information for Cross-Device Fault Diagnosis","authors":"Lexuan Shao;Ningyun Lu;Bin Jiang;Jianhua Lv;Silvio Simani","doi":"10.1109/TIM.2025.3580817","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580817","url":null,"abstract":"Current data-driven fault diagnosis methods suffer from poor transferability. It is challenging to apply a model effective on one device directly to another. Many methods now employ domain adaptation algorithms to align their fault distributions for model transferability. However, most methods focus only on aligning either marginal or pseudo-labels-based conditional distributions, ignoring cases where both label and conditional distributions change, along with the unreliable nature of pseudo-labels. This oversight can lead to transfer failures. To tackle this, this article introduces an information theory-based joint distribution alignment model. The algorithm starts by maximizing mutual information between predicted categories and input samples for conditional alignment without pseudo-label involvement. Simultaneously, the model introduces virtual adversarial training with a penalty term to improve the robustness of prediction results. When label distribution changes, the model uses entropy values to assign data in categories unique to the target domain to “outliers,” thus preventing misalignment of these data. In experiments, this algorithm outperformed other domain adaptation-based methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing Domain Shift in Insulator Defect Data: A Generalization Framework for Cross-Domain Detection of Broken and Self-Blast Insulator Defect 处理绝缘子缺陷数据的域移位：一个破碎和自爆绝缘子缺陷跨域检测的通用框架

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-18 DOI: 10.1109/TIM.2025.3580815

Qingzhen Liu;Yadong Liu;Yingjie Yan;Qian Jiang;Xiuchen Jiang

{"title":"Addressing Domain Shift in Insulator Defect Data: A Generalization Framework for Cross-Domain Detection of Broken and Self-Blast Insulator Defect","authors":"Qingzhen Liu;Yadong Liu;Yingjie Yan;Qian Jiang;Xiuchen Jiang","doi":"10.1109/TIM.2025.3580815","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580815","url":null,"abstract":"Accurate and timely detection of insulator defects is essential for the safety and stability of the power system. However, current detection faces challenges of domain shifts arising from insufficient data that do not encompass most inspection scenarios. To address this challenge, we propose a robust generalization framework for insulator broken and self-blast defect detection involving domain generalization (DG) and domain adaptation (DA) methods. First, we synthesize high-fidelity insulator defect data in 3-D space using domain randomization (DR) techniques to create diverse variations termed DR-Syn. For the DG method, we extract invariant features across domain data using a domain expansion method based on our proposed instance-reweighted image quality assessment (IR-IQA) model and a proposed discrepancy-constrained invariant learning (DCIL) model in the training process. For the DA method, we proposed a digital-twin-aided DR-Syn model that incorporates the target domain background information for specific-domain data generation. Extensive experiments validate the effectiveness of our framework in mitigating domain shift. The basic DR-Syn data can perform better than real-world intradomain data training. The DG method outperforms the real-world data training model in <inline-formula> <tex-math>$textbf {mAP}_{50}$ </tex-math></inline-formula> of 4.2% and 5.3% in intradomain training and 13.9% and 29.3% in cross-domain validation. The DA method achieves additional performance gains of 15.7% and 17.9% enhanced with digital-twin background modeling. Detailed ablation studies verify the validity of our method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Acoustic Signal Denoising Method for Rotating Machinery Based on Virtual Sample Integrating the Deep Neural Network 基于虚拟样本集成深度神经网络的旋转机械声信号去噪方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-06-18 DOI: 10.1109/TIM.2025.3580900

Peng Wu;Yue Shu;Gongye Yu;Yongming Han;Bo Ma

{"title":"The Acoustic Signal Denoising Method for Rotating Machinery Based on Virtual Sample Integrating the Deep Neural Network","authors":"Peng Wu;Yue Shu;Gongye Yu;Yongming Han;Bo Ma","doi":"10.1109/TIM.2025.3580900","DOIUrl":"https://doi.org/10.1109/TIM.2025.3580900","url":null,"abstract":"The clean signal is used as the reference signal in acoustic signal denoising methods based on the supervised deep learning, but the clean signal of the operating state of the rotating machinery is difficult to obtain, which leads to difficulties in constructing the denoising model. Therefore, the novel acoustic signal denoising method based on the clean signal virtual sample (CSVS) integrating the deep neural network (DNN) (CSVS-DNN) is proposed. The frequency band that contains the most fault information is selected based on the sideband characteristics of the modulation signal. Then, the CSVS dataset is generated based on the distribution of amplitude variations of the acoustic signal, the fault characteristic frequencies, and the transmission paths in the mechanical structure and air. Moreover, the generated CSVS dataset and the actual device operation signal are used to construct the acoustic signal denoising model based on the DNN. Finally, the denoising effect is evaluated using the signal-to-noise ratio (SNR) and the prominence degree coefficient (PDC) through the experimental data and the actual industrial data. The analysis results indicate that the average SNR of the proposed method is improved by at least 0.6 dB, and the average PDC is enhanced by at least 0.05 compared to other denoising methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0