Mechanical Systems and Signal Processing最新文献

英文中文

Physics-driven cross domain digital twin framework for bearing fault diagnosis in non-stationary conditions

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-19 DOI: 10.1016/j.ymssp.2024.112266

Dandan Peng , Mahsa Yazdanianasr , Alexandre Mauricio , Toby Verwimp , Wim Desmet , Konstantinos Gryllias

Existing methodologies for digital twin-based domain adaptation primarily focus on steady or variable working conditions, frequently encountering limitations in scenarios where operational conditions change over time, such as in the case of wind turbines subjected to fluctuating wind speeds. This paper proposes a novel physics-driven cross domain digital twin framework designed to address the challenges associated with diagnosing bearing faults in non-stationary conditions. The model incorporates a phenomenological bearing model that generates virtual datasets, capturing a diverse range of fault types under non-stationary conditions. Furthermore, it introduces a physics-driven adaptive domain adaptation approach that aims to reduce the disparity between simulated and real-world data. This approach dynamically aligns domain distributions from both global and local perspective, markedly enhancing the accuracy of fault diagnosis under non-stationary conditions using exclusively unlabeled real-world data. The efficacy and robustness of the proposed model are validated through applications on two distinct use cases, involving various bearing types and time-varying working conditions. This study significantly contributes to the field by being among the first to explore digital twin-based domain adaptation in non-stationary conditions.

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引用次数: 0

High resolution imaging of pressurised water supply lines

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-19 DOI: 10.1016/j.ymssp.2025.112455

Saber Nasraoui , Moez Louati , Mohamed Salah Ghidaoui

This paper proposes a computationally efficient, nondestructive, and medium range guided wave imaging of pressurized water supply lines based on the time reversal (TR) technique. A send-receive transducer is inserted in the water column from a single access point and used to measure multi–input–multi–output (MIMO) pressure wave signals that propagate along the waveguide. The resulting high frequency (10 kHz-100 kHz) signals are processed by the TR-MUltiple SIgnal Classification (TR-MUSIC) algorithm to provide high-resolution images the water pipe system. The resulting images reveal the pipe wall inner and outer condition with millimeter resolution. The proposed technique is tested and validated in lab as well as in field scale facilities on pressurized water-filled viscoelastic high-density polyethylene (HDPE) pipes. In particular, we successfully imaged (i) a straight 6.5 m long, water-filled 90 mm HDPE lab pipe containing a small blockage with thickness 7.65 mm and a length 97 mm and (ii) a 36.5 m long section water-filled 160 mm HDPE pipe containing three T-connections and a small blockage with thickness 16 mm and length 95 mm.

{"title":"High resolution imaging of pressurised water supply lines","authors":"Saber Nasraoui , Moez Louati , Mohamed Salah Ghidaoui","doi":"10.1016/j.ymssp.2025.112455","DOIUrl":"10.1016/j.ymssp.2025.112455","url":null,"abstract":"<div><div>This paper proposes a computationally efficient, nondestructive, and medium range guided wave imaging of pressurized water supply lines based on the time reversal (TR) technique. A send-receive transducer is inserted in the water column from a single access point and used to measure multi–input–multi–output (MIMO) pressure wave signals that propagate along the waveguide. The resulting high frequency (10 kHz-100 kHz) signals are processed by the TR-MUltiple SIgnal Classification (TR-MUSIC) algorithm to provide high-resolution images the water pipe system. The resulting images reveal the pipe wall inner and outer condition with millimeter resolution. The proposed technique is tested and validated in lab as well as in field scale facilities on pressurized water-filled viscoelastic high-density polyethylene (HDPE) pipes. In particular, we successfully imaged (i) a straight 6.5 m long, water-filled 90 mm HDPE lab pipe containing a small blockage with thickness 7.65 mm and a length 97 mm and (ii) a 36.5 m long section water-filled 160 mm HDPE pipe containing three T-connections and a small blockage with thickness 16 mm and length 95 mm.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112455"},"PeriodicalIF":7.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

In-process analysis of the dynamic deformation of a bionic lightweight gear

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-19 DOI: 10.1016/j.ymssp.2025.112446

Philipp Thomaneck , Marina Terlau , Ronald Eberl , Axel von Freyberg , Andreas Fischer

Lightweight gears enable the weight reduction of frequently used mechanical engineering parts, but require an in-depth understanding of their mechanical load capacity. Therefore, the dynamic load behavior of a holistic bionic lightweight gear with a weight reduction of 61 % compared to a conventional solid gear is investigated. An in-process measuring system consisting of strain gauges and a telemetry system for recording the strain condition during dynamic tooth meshing is used. Based on finite element simulation data, four gear positions with biaxial strain fields on the gear surface were identified to position and align the strain gauges with high sensitivity. As a result, the sensors are capable of resolving the local material load during the gear revolutions over time, since the experimental results agree with theoretical considerations. For instance, regions of single-tooth contact and double-tooth contact are detectable during meshing, as well as the load due the meshing of a neighboring tooth. Furthermore, the observed gear deformations for the different transmission torques are proven to be elastic, and a biaxial strain measurement is demonstrated and verified by the simulation data. Thus, the in-process deformation behavior of a holistic bionic gear can be monitored over time, opening up structural health monitoring applications in future.

{"title":"In-process analysis of the dynamic deformation of a bionic lightweight gear","authors":"Philipp Thomaneck , Marina Terlau , Ronald Eberl , Axel von Freyberg , Andreas Fischer","doi":"10.1016/j.ymssp.2025.112446","DOIUrl":"10.1016/j.ymssp.2025.112446","url":null,"abstract":"<div><div>Lightweight gears enable the weight reduction of frequently used mechanical engineering parts, but require an in-depth understanding of their mechanical load capacity. Therefore, the dynamic load behavior of a holistic bionic lightweight gear with a weight reduction of 61% compared to a conventional solid gear is investigated. An in-process measuring system consisting of strain gauges and a telemetry system for recording the strain condition during dynamic tooth meshing is used. Based on finite element simulation data, four gear positions with biaxial strain fields on the gear surface were identified to position and align the strain gauges with high sensitivity. As a result, the sensors are capable of resolving the local material load during the gear revolutions over time, since the experimental results agree with theoretical considerations. For instance, regions of single-tooth contact and double-tooth contact are detectable during meshing, as well as the load due the meshing of a neighboring tooth. Furthermore, the observed gear deformations for the different transmission torques are proven to be elastic, and a biaxial strain measurement is demonstrated and verified by the simulation data. Thus, the in-process deformation behavior of a holistic bionic gear can be monitored over time, opening up structural health monitoring applications in future.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112446"},"PeriodicalIF":7.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143436753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of water hammer component frequencies in non-uniform or bifurcated turbine penstock and comparing of analytical results with field data 确定非均匀或分叉水轮机水闸中的水锤分量频率，并将分析结果与现场数据进行比较

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112439

Slawomir Henclik, Adam Adamkowski, Waldemar Janicki

Water hammer (WH) events are usually undesired phenomena which can be especially dangerous in large pipeline installations, like turbine penstocks at hydropower plants. For uniform pipeline with upper reservoir at the beginning, cutting a flow off at the end will produce oscillations of WH pressure wave with a period

T = 4 L / a

and an amplitude depending on various factors, but in general limited by the Joukovsky value. Harmonic components of the pressure wave have their frequencies being odd multiplies of the basic WH frequency. But a straight pipeline of constant parameters is a simplified model and in many installations their detailed parameters like diameter, pipe-wall thickness or even pipe material may be changed along it. For such a case the uniform pipeline solution can be used only as an approximated approach and detailed modeling should require a more precise analysis. A significant is also an answer to the question if and when, a simplified approach of equivalent pipeline can be applied. In this paper, theoretical results of characteristic frequencies determination for a pipeline compound of several serial reaches of varying parameters are found with the separation of variables method. A case of bifurcated pipeline structure is also analyzed. For a specific scenario the amplitudes of component waves are calculated, as well. The problem of pipeline characteristic frequencies determination is examined by a number of scientists, however usually other methods of solution are used. In fact, the current approach can be also applied to any other, more complex pipeline structures. Frequencies of component waves are received as solutions of characteristic equation which is formulated and effectively solved, for the defined boundary conditions, in a specific matrix form. A significant value of this study is also comparison and verification of the theoretical results with field data of transients measured during performance tests in real hydropower plant penstocks of complex structures. Quite a good agreement has been achieved and existed discrepancies are discussed and concluded. Additional analyses are also performed, especially within the effectiveness, properties and limitations of the equivalent pipeline approach.

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引用次数: 0

Real-time temperature monitoring during titanium alloy machining with cutting tools integrating novel thin-film sensors 使用集成新型薄膜传感器的切削工具实时监测钛合金加工过程中的温度

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112444

Bruno Martins , Carlos Patacas , Albano Cavaleiro , Pedro Faia , Filipe Fernandes

This study explores the integration of titanium aluminum nitride (TiAlN) and zirconium aluminum nitride (ZrAlN) thin-film sensors into cutting tools for real-time temperature monitoring during machining of Ti6Al4V titanium alloy. These sensors, integrated into a multilayer coating for electrical and wear shielding, were deposited directly onto the tool surfaces and calibrated for temperatures up to 750 °C. Due to the integration into the multilayer coating, the sensors exhibit different β sensitivities across the temperature range, ranging from 108 to 825 K for TiAlN and from 950 to 6681 K for ZrAlN. The cutting tests conducted under various cutting conditions, such as cutting speed, feed rate, depth of cut, and cooling, revealed the influence of these parameters on the cutting temperature. Our findings indicate that the sensor position in the tool’s rake face is fundamental for measuring the cutting temperature. The study introduces an innovative tool connector for integration and signal retrieval of the cutting tool in a “plug-and-play” fashion, compatible with industry standards. Additionally, implementing wireless data transmission for real-time and in-situ temperature monitoring offers a pathway for integrating smart cutting tools into modern manufacturing environments, aligning with Industry 4.0.

{"title":"Real-time temperature monitoring during titanium alloy machining with cutting tools integrating novel thin-film sensors","authors":"Bruno Martins , Carlos Patacas , Albano Cavaleiro , Pedro Faia , Filipe Fernandes","doi":"10.1016/j.ymssp.2025.112444","DOIUrl":"10.1016/j.ymssp.2025.112444","url":null,"abstract":"<div><div>This study explores the integration of titanium aluminum nitride (TiAlN) and zirconium aluminum nitride (ZrAlN) thin-film sensors into cutting tools for real-time temperature monitoring during machining of Ti6Al4V titanium alloy. These sensors, integrated into a multilayer coating for electrical and wear shielding, were deposited directly onto the tool surfaces and calibrated for temperatures up to 750 °C. Due to the integration into the multilayer coating, the sensors exhibit different β sensitivities across the temperature range, ranging from 108 to 825 K for TiAlN and from 950 to 6681 K for ZrAlN. The cutting tests conducted under various cutting conditions, such as cutting speed, feed rate, depth of cut, and cooling, revealed the influence of these parameters on the cutting temperature. Our findings indicate that the sensor position in the tool’s rake face is fundamental for measuring the cutting temperature. The study introduces an innovative tool connector for integration and signal retrieval of the cutting tool in a “plug-and-play” fashion, compatible with industry standards. Additionally, implementing wireless data transmission for real-time and in-situ temperature monitoring offers a pathway for integrating smart cutting tools into modern manufacturing environments, aligning with Industry 4.0.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112444"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Multi-channel vibration information weighted fusion for fault feature extraction of rotating machinery main bearings

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112476

Luan Xiaochi, Zhao Junhao, Sha Yundong, Liu Xinhang, Lei Zhihao

In response to the issue of insufficient extraction of effective information due to the effect of environmental noise on weak rolling bearing fault signals in aircraft engines, a method for extracting fault features in rotating machinery main bearings using multi-channel vibration information (MCVI) weighted fusion is proposed. This method first utilizes a weighted fusion model for MCVI to integrate data from multiple vibration sensors into a one-dimensional signal. Subsequently, the fused signal is decomposed using the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method. Based on the kurtosis index-correlation coefficient filtering criterion, the impactful components are selected for reconstruction, resulting in a vibration signal rich in bearing fault feature information. Lastly, the weak fault features of bearing faults are identified using the envelope spectrum. Simulation signal identification verification shows that the fault feature energy Q within the envelope spectrum can be increased by 12.4%. The effectiveness of the MCVI weighted fusion method is comprehensively validated based on data from a simulated test bench for intermediate shaft bearings in aero-engines. An analysis of vibration signals from a certain type of aero-engine main bearing demonstrates that the proposed method can effectively extract fault feature information transmitted via complex transmission paths, providing an effective means for processing and diagnosing complex signals related to faults in aero-engine main bearings.

{"title":"Multi-channel vibration information weighted fusion for fault feature extraction of rotating machinery main bearings","authors":"Luan Xiaochi, Zhao Junhao, Sha Yundong, Liu Xinhang, Lei Zhihao","doi":"10.1016/j.ymssp.2025.112476","DOIUrl":"10.1016/j.ymssp.2025.112476","url":null,"abstract":"<div><div>In response to the issue of insufficient extraction of effective information due to the effect of environmental noise on weak rolling bearing fault signals in aircraft engines, a method for extracting fault features in rotating machinery main bearings using multi-channel vibration information (MCVI) weighted fusion is proposed. This method first utilizes a weighted fusion model for MCVI to integrate data from multiple vibration sensors into a one-dimensional signal. Subsequently, the fused signal is decomposed using the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method. Based on the kurtosis index-correlation coefficient filtering criterion, the impactful components are selected for reconstruction, resulting in a vibration signal rich in bearing fault feature information. Lastly, the weak fault features of bearing faults are identified using the envelope spectrum. Simulation signal identification verification shows that the fault feature energy <em>Q</em> within the envelope spectrum can be increased by 12.4%. The effectiveness of the MCVI weighted fusion method is comprehensively validated based on data from a simulated test bench for intermediate shaft bearings in aero-engines. An analysis of vibration signals from a certain type of aero-engine main bearing demonstrates that the proposed method can effectively extract fault feature information transmitted via complex transmission paths, providing an effective means for processing and diagnosing complex signals related to faults in aero-engine main bearings.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112476"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Statistical distribution measures based on amplitude normalization for wind turbine generator bearing condition monitoring under variable speed conditions

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112464

Guangyao Zhang , Yi Wang , Yi Qin , Baoping Tang

Wind turbines (WTs), with the capacity of renewable energy production, have been massively equipped in recent years. To improve the reliability of the WTs and also reduce the operation and maintenance (O&M) costs, condition monitoring based preventative maintenance is of urgent need. For this industrial application demand, health indicator (HI) construction is a promising solution. However, it should be noted that most of the currently available HIs are developed based on the assumption of stationary or quasi-stationary operating conditions, the performances of which in time-varying speed cases, nevertheless, are significantly influenced due to the dynamic interactions. Aiming at this issue, a statistically interpretable HI based on the amplitude normalization is proposed in this paper. In this method, an amplitude normalization strategy is firstly designed to suppress the variable speed induced interferences. Afterwards, a characteristic model is established for the integrated statistical representation of the signal from the distribution perspective. Multiple parameters in this model are estimated by the maximum log-likelihood method. Then the evolution of the established probability distribution during the degradation process is analyzed, the statistic deviation is accordingly estimated and taken as a novel HI to characterize the degradation process of the WT generator bearing. Finally, with the simulated bearing degradation data and the industrial field datasets collected from different WT generator bearings, experimental tests are conducted and indicate that the proposed method is preferable in bearing degradation process characterization under variable speed conditions.

{"title":"Statistical distribution measures based on amplitude normalization for wind turbine generator bearing condition monitoring under variable speed conditions","authors":"Guangyao Zhang , Yi Wang , Yi Qin , Baoping Tang","doi":"10.1016/j.ymssp.2025.112464","DOIUrl":"10.1016/j.ymssp.2025.112464","url":null,"abstract":"<div><div>Wind turbines (WTs), with the capacity of renewable energy production, have been massively equipped in recent years. To improve the reliability of the WTs and also reduce the operation and maintenance (O&M) costs, condition monitoring based preventative maintenance is of urgent need. For this industrial application demand, health indicator (HI) construction is a promising solution. However, it should be noted that most of the currently available HIs are developed based on the assumption of stationary or quasi-stationary operating conditions, the performances of which in time-varying speed cases, nevertheless, are significantly influenced due to the dynamic interactions. Aiming at this issue, a statistically interpretable HI based on the amplitude normalization is proposed in this paper. In this method, an amplitude normalization strategy is firstly designed to suppress the variable speed induced interferences. Afterwards, a characteristic model is established for the integrated statistical representation of the signal from the distribution perspective. Multiple parameters in this model are estimated by the maximum log-likelihood method. Then the evolution of the established probability distribution during the degradation process is analyzed, the statistic deviation is accordingly estimated and taken as a novel HI to characterize the degradation process of the WT generator bearing. Finally, with the simulated bearing degradation data and the industrial field datasets collected from different WT generator bearings, experimental tests are conducted and indicate that the proposed method is preferable in bearing degradation process characterization under variable speed conditions.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112464"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Automatic threshold setting for anomaly detection

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112462

Adam Jablonski , Krzysztof Mendrok

The paper presents automatic method for calculation of threshold value and its use for detection of anomalies in vibration signals for the purpose of condition monitoring. Anomalies are understood as time series (trends) calculated from consecutive spectra from individual vibration signals with significantly different characteristics than remaining trends. In this sense, the paper presents a classification method within anomaly (or novelty) detection; yet, without any black-box techniques. The method is based on the detection of specific gap of histogram of selected statistical indicator. Thus, the method relies on hypothesis that any deterioration of technical condition of rotary machinery results is increase of vibrations. The method is validated on real data. The performance of the method is compared with other typical methods, both wideband, and narrowband.

引用次数: 0

An integrated data processing strategy for pavement modulus prediction using empirical mode decomposition techniques

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112468

Cheng Zhang , Shihui Shen , Hai Huang , Shuai Yu

Data collection for infrastructure health monitoring using embedded sensors is often hindered by noise contamination and inconsistencies in sensor measurements. These challenges are exacerbated by variations in data features across different sensors, complicating the analysis and interpretation process. A comprehensive data processing strategy capable of mitigating noise, harmonizing feature discrepancies, and extracting latent information is essential for enhancing data-based analysis and modeling. This study introduces an integrated data processing strategy combining Empirical Mode Decomposition (EMD) techniques with adaptive Intrinsic Mode Function (IMF) classification to improve the prediction of pavement dynamic modulus. Various EMD methods were applied to decompose signals from wireless embedded sensors, using Maximum Normalized Cross-Correlation (MNCC) and Signal-to-Noise Ratio (SNR) as indices in a K-means clustering process to select effective IMFs. Results show that the ensemble EMD (EEMD) technique effectively captures critical mechanical response information while expanding data dimensionality, leading to enhanced prediction accuracy. Consequently, the integrated EEMD and K-means clustering approach is recommended as a powerful tool for infrastructure data processing and predictive modeling.

{"title":"An integrated data processing strategy for pavement modulus prediction using empirical mode decomposition techniques","authors":"Cheng Zhang , Shihui Shen , Hai Huang , Shuai Yu","doi":"10.1016/j.ymssp.2025.112468","DOIUrl":"10.1016/j.ymssp.2025.112468","url":null,"abstract":"<div><div>Data collection for infrastructure health monitoring using embedded sensors is often hindered by noise contamination and inconsistencies in sensor measurements. These challenges are exacerbated by variations in data features across different sensors, complicating the analysis and interpretation process. A comprehensive data processing strategy capable of mitigating noise, harmonizing feature discrepancies, and extracting latent information is essential for enhancing data-based analysis and modeling. This study introduces an integrated data processing strategy combining Empirical Mode Decomposition (EMD) techniques with adaptive Intrinsic Mode Function (IMF) classification to improve the prediction of pavement dynamic modulus. Various EMD methods were applied to decompose signals from wireless embedded sensors, using Maximum Normalized Cross-Correlation (MNCC) and Signal-to-Noise Ratio (SNR) as indices in a K-means clustering process to select effective IMFs. Results show that the ensemble EMD (EEMD) technique effectively captures critical mechanical response information while expanding data dimensionality, leading to enhanced prediction accuracy. Consequently, the integrated EEMD and K-means clustering approach is recommended as a powerful tool for infrastructure data processing and predictive modeling.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112468"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Moving force identification based on multi-task decomposition and sparse regularization

IF 7.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Mechanical Systems and Signal Processing

Pub Date : 2025-02-18 DOI: 10.1016/j.ymssp.2025.112472

Chudong Pan, Xiaodong Chen, Zeke Xu, Haoming Zeng

High-accuracy and efficient moving force identification (MFI) serves as an indirect approach that has the potential to meet real-time monitoring of vehicle-bridge interaction forces. The parallel computing-oriented method developed based on time-domain segmentation has demonstrated its advantages in the rapid identification of dynamic forces. However, this method has no strategy in place to highlight the global signal feature of dynamic forces. This study inherits a framework of the existing parallel computing-oriented method, attempting to identify the moving forces in a shorter amount of time by using a parallelizable multi-task optimal method. The proposed method establishes multiple MFI tasks based on a finite number of local time ranges. Each MFI task aims to estimate the moving forces happening within its local analysis duration and the corresponding initial vibration state of the structure. The identified equations for multiple tasks are built based on sparse regularization, intending to improve the ill-posed nature of the MFI inverse problems. To ensure that the identified moving force has an overall horizontal trend line, additional constraint conditions are defined mathematically and added to the sparse regularization-based equations, aiming to limit the differences among all the average values of the moving forces that are identified from different tasks, and resulting in a group of constrained identified equations. By relaxing the added constraints, a practical iterative algorithm is proposed for solving the multi-task MFI problem, wherein, the identified processes of different tasks in each iteration can be solved by parallel computing. Numerical and experimental studies verify the feasibility and effectiveness of the proposed method in identifying moving forces. The comparative analysis highlights its advantages in fast computation rather than the existing l₁-norm regularization-based method in the considered cases. Some relative issues are discussed as well.

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