Mechanical Systems and Signal Processing最新文献

{"title":"Determination of water hammer component frequencies in non-uniform or bifurcated turbine penstock and comparing of analytical results with field data","authors":"Slawomir Henclik,&nbsp;Adam Adamkowski,&nbsp;Waldemar Janicki","doi":"10.1016/j.ymssp.2025.112439","DOIUrl":"10.1016/j.ymssp.2025.112439","url":null,"abstract":"<div><div>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 <span><math><mrow><mi>T</mi><mo>=</mo><mn>4</mn><mi>L</mi><mo>/</mo><mi>a</mi></mrow></math></span> 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.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112439"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430087","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}

{"title":"Real-time temperature monitoring during titanium alloy machining with cutting tools integrating novel thin-film sensors","authors":"Bruno Martins ,&nbsp;Carlos Patacas ,&nbsp;Albano Cavaleiro ,&nbsp;Pedro Faia ,&nbsp;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}

{"title":"Multi-channel vibration information weighted fusion for fault feature extraction of rotating machinery main bearings","authors":"Luan Xiaochi,&nbsp;Zhao Junhao,&nbsp;Sha Yundong,&nbsp;Liu Xinhang,&nbsp;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}

{"title":"Statistical distribution measures based on amplitude normalization for wind turbine generator bearing condition monitoring under variable speed conditions","authors":"Guangyao Zhang ,&nbsp;Yi Wang ,&nbsp;Yi Qin ,&nbsp;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&amp;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}

{"title":"Automatic threshold setting for anomaly detection","authors":"Adam Jablonski ,&nbsp;Krzysztof Mendrok","doi":"10.1016/j.ymssp.2025.112462","DOIUrl":"10.1016/j.ymssp.2025.112462","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112462"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430077","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}

{"title":"An integrated data processing strategy for pavement modulus prediction using empirical mode decomposition techniques","authors":"Cheng Zhang ,&nbsp;Shihui Shen ,&nbsp;Hai Huang ,&nbsp;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}

{"title":"Moving force identification based on multi-task decomposition and sparse regularization","authors":"Chudong Pan,&nbsp;Xiaodong Chen,&nbsp;Zeke Xu,&nbsp;Haoming Zeng","doi":"10.1016/j.ymssp.2025.112472","DOIUrl":"10.1016/j.ymssp.2025.112472","url":null,"abstract":"<div><div>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 <em>l</em>₁-norm regularization-based method in the considered cases. Some relative issues are discussed as well.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112472"},"PeriodicalIF":7.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430143","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}

{"title":"An adaptive source-free unsupervised domain adaptation method for mechanical fault detection","authors":"Jianing Liu ,&nbsp;Hongrui Cao ,&nbsp;Jaspreet Singh Dhupia ,&nbsp;Madhurjya Dev Choudhury ,&nbsp;Yang Fu ,&nbsp;Siwen Chen ,&nbsp;Jinhui Li ,&nbsp;Bin Yv","doi":"10.1016/j.ymssp.2025.112475","DOIUrl":"10.1016/j.ymssp.2025.112475","url":null,"abstract":"<div><div>Cross-machine fault detection is crucial due to the challenges of data labeling. While domain adaptation methods facilitate diagnosis across rotating machines, they often require data sharing, which is impractical due to privacy concerns and large data transmission. Although domain generalization and source-free unsupervised domain adaptation (SFUDA) methods address privacy issues, most fail to consider dynamic distribution shifts within and between domains, limiting their effectiveness. To overcome this challenge, an adaptive SFUDA method named AI3M is proposed. The AI3M pre-trains a source model with intra- and inter-domain information maximization loss to reduce distribution shifts within and between domains, and then adapts the model with a target-guided adaptation strategy to minimize the dynamic gap between different machines. Experiments on datasets from 11 wind turbines across 8 wind farms show that the proposed method outperforms state-of-the-art DG and SFUDA approaches, achieving superior cross-machine fault detection performance.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112475"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430144","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}

{"title":"A framework for detecting high-performance cardiac arrhythmias using deep inference engine on FPGA and higher-order spectral distribution","authors":"S. Karthikeyani,&nbsp;S. Sasipriya,&nbsp;M. Ramkumar","doi":"10.1016/j.ymssp.2025.112445","DOIUrl":"10.1016/j.ymssp.2025.112445","url":null,"abstract":"<div><div>Cardiac arrhythmias (CA) are critical health conditions. In such a case, highly accurate detection leads to better management and, therefore, better treatment. Here, this paper presents a novel high-performance detection framework for cardiac arrhythmias based on advanced signal processing algorithms with deep learning on a Field Programmable Gate Array (FPGA) towards achieving real-time performances and even higher accuracy. ECG signals are initially analyzed by using compressive sensing theory to obtain sparsity, and from that, the adaptive compressive sensing framework is created. This compressive sensing framework adapts the sensing matrix step by step through compression via the Hybrid Reptile Search Algorithm integrated with the Garra Rufa Algorithm (Hyb-RSA-GRA). The adapted sensing matrix renders signal reconstruction efficient through Bayesian Regularization-Backpropagation Neural Network (BRBNN). The new arrhythmia detection framework employs the possibility of higher-order spectral distribution (HoSD) in extracting finer patterns from ECGs that describe arrhythmia. The task of classification uses a pre-trained Graph Convolutional Neural Network (GCNN) acting as a Deep Inference Engine on the FPGA to support real-time, robust identification of the type of arrhythmias such as N (normal beat), S (supraventricular ectopic beat), V (ventricular ectopic beat), F (fusion beat), and U (unidentified beat). The proposed FPGA implementation reveals better performance with high accuracy, sensitivity, specificity, precision, recall, and F1-score with optimized power dissipation, resource utilization, and delay metrics. Furthermore, the compressive sensing framework guarantees low MSE, reduced RMSE, high SNR, and an improved reconstruction probability. All the above results demonstrate the capability of the framework in accurate prediction and hardware efficiency, hence making it a robust solution for cardiac arrhythmia detection.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112445"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422023","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}

{"title":"An unsupervised multi-level fusion domain adaptation method for transfer diagnosis under time-varying working conditions","authors":"Cuiying Lin ,&nbsp;Yun Kong ,&nbsp;Qinkai Han ,&nbsp;Xiantao Zhang ,&nbsp;Junyu Qi ,&nbsp;Meng Rao ,&nbsp;Mingming Dong ,&nbsp;Hui Liu ,&nbsp;Ming J. Zuo ,&nbsp;Fulei Chu","doi":"10.1016/j.ymssp.2025.112458","DOIUrl":"10.1016/j.ymssp.2025.112458","url":null,"abstract":"<div><div>Unsupervised multi-source domain adaptation can overcome the limitations associated with insufficient information diversity in single-source domain adaptation for intelligent transfer diagnosis. However, the challenges of time-varying working conditions in practical industrial applications, limitation in single-level information fusion along with lack of multi-level information fusion restrict effective applications of unsupervised multi-source domain adaptation in transfer diagnosis. To address these challenges, this research presents a novel unsupervised multi-level fusion domain adaptation methodology for transfer diagnostics under time-varying working conditions, which employs a multi-level fusion domain adaptation network (MLFDAN). Firstly, a multi-sensor data enhancement and fusion module is proposed by combining continuous wavelet transform with an RGB information fusion, which integrates time–frequency and spatial information from multi-sensors. Then, a squeeze and excitation feature fusion module is designed for feature fusion across both time–frequency and spatial domains, which effectively emphasizes domain-invariant features while suppressing less relevant ones. Subsequently, an adaptive collaborative decision module is developed, which employs a weighted fusion strategy to address strong conflicts among multi-subnet predictions and utilizes consensus-based fusion strategy when multi-subnet predictions align, thus ensuring reliable and robust diagnostics decisions. Finally, a promising MLFDAN framework for transfer diagnosis is proposed by incorporating a dual-component domain adaptation approach that integrates a domain discriminator and multi-kernel maximum mean discrepancy. Numerous experiment results show that the presented MLFDAN methodology effectively adapts to transfer diagnosis scenarios from steady to time-varying working conditions, achieving impressive performances and outperforming several prominent unsupervised transfer diagnosis methodologies.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112458"},"PeriodicalIF":7.9,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430145","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}