Pub Date : 2025-02-07DOI: 10.1016/j.ymssp.2025.112423
Haris Ali Khan , Shahab Uddin , Sharjeel Salik , Ali Javaid , Talha Ali Khan , Zia Ul Islam
This research is focused on developing a novel Lock-In Thermography-based scanning system to detect subsurface defects in aircraft rivets. The proposed system included a customized thermographic device consisting of a thermal camera, heating source, synchronized control circuitry, and post-processing software utilizing the Discrete Wavelet Transform technique, augmented by spatial gradient and mean filtering methods. To validate the developed system, a two-pronged hierarchical approach was adopted. The detection scheme for looseness and internal cracks in rivets was first developed and refined in a lab environment. After successful trials on lab samples, the scheme was further employed in different areas of actual aircraft. A total of 751 rivets were scanned for looseness and internal cracks, and a very promising detection rate was observed, confirmed through visual inspection and microscopic analysis. The developed setup can be further extended to other sub-surface defects such as corrosion and other structural non-homogeneities.
{"title":"A Lock-In thermography based post-processing scheme for the detection of sub-surface rivet-related defects in aircraft structures","authors":"Haris Ali Khan , Shahab Uddin , Sharjeel Salik , Ali Javaid , Talha Ali Khan , Zia Ul Islam","doi":"10.1016/j.ymssp.2025.112423","DOIUrl":"10.1016/j.ymssp.2025.112423","url":null,"abstract":"<div><div>This research is focused on developing a novel Lock-In Thermography-based scanning system to detect subsurface defects in aircraft rivets. The proposed system included a customized thermographic device consisting of a thermal camera, heating source, synchronized control circuitry, and post-processing software utilizing the Discrete Wavelet Transform technique, augmented by spatial gradient and mean filtering methods. To validate the developed system, a two-pronged hierarchical approach was adopted. The detection scheme for looseness and internal cracks in rivets was first developed and refined in a lab environment. After successful trials on lab samples, the scheme was further employed in different areas of actual aircraft. A total of 751 rivets were scanned for looseness and internal cracks, and a very promising detection rate was observed, confirmed through visual inspection and microscopic analysis. The developed setup can be further extended to other sub-surface defects such as corrosion and other structural non-homogeneities.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112423"},"PeriodicalIF":7.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280510","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}
Pub Date : 2025-02-07DOI: 10.1016/j.ymssp.2025.112410
Yao Sun , Yirong Sun , Yiming Huang , Siqian Gong , Mingsheng Sun , Ming Liu
The cutting-edge trajectory of micro end mills directly affects machining stability, surface quality and tool wear involved in micro milling process. However, the size effect and geometric characteristics of micro end mills make its cutting-edge trace diverge markedly from the traditional milling cutter paths. The cutting-edge trajectory prediction system model is specifically developed for a two-edged micro end mill in high-speed rotation consisting of input layer, model layer and output layer in this study. The input layer comprises the static and dynamic tool runout parameters through measurement and calculation, and the micro-milling force model considering the undeformed cutting thickness and tool runout offset. The model layer encompasses micro milling material removal model and tool path model that account for tool runout and cutting thickness. Finally, the tool path, slot width, micro-milling forces and surface roughness can be obtained from the output layer. Besides, the influence laws of runout parameters on the micro-milling force, surface roughness, and slot width of micro-milled polycarbonate were revealed. The developed cutting-edge trajectory predicted system model is very useful for optimizing tool conditions, compensating machining errors and reducing residual burrs on workpieces.
{"title":"Study on developing predicted system model of cutting-edge trajectory for micro-milling process based on tool runout error, chip thickness and force signal","authors":"Yao Sun , Yirong Sun , Yiming Huang , Siqian Gong , Mingsheng Sun , Ming Liu","doi":"10.1016/j.ymssp.2025.112410","DOIUrl":"10.1016/j.ymssp.2025.112410","url":null,"abstract":"<div><div>The cutting-edge trajectory of micro end mills directly affects machining stability, surface quality and tool wear involved in micro milling process. However, the size effect and geometric characteristics of micro end mills make its cutting-edge trace diverge markedly from the traditional milling cutter paths. The cutting-edge trajectory prediction system model is specifically developed for a two-edged micro end mill in high-speed rotation consisting of input layer, model layer and output layer in this study. The input layer comprises the static and dynamic tool runout parameters through measurement and calculation, and the micro-milling force model considering the undeformed cutting thickness and tool runout offset. The model layer encompasses micro milling material removal model and tool path model that account for tool runout and cutting thickness. Finally, the tool path, slot width, micro-milling forces and surface roughness can be obtained from the output layer. Besides, the influence laws of runout parameters on the micro-milling force, surface roughness, and slot width of micro-milled polycarbonate were revealed. The developed cutting-edge trajectory predicted system model is very useful for optimizing tool conditions, compensating machining errors and reducing residual burrs on workpieces.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112410"},"PeriodicalIF":7.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143217594","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}
Pub Date : 2025-02-07DOI: 10.1016/j.ymssp.2025.112431
Tianmei Li, Zhenyu Cai, Zhaoju Zeng, Zhengxin Zhang, Xiaosheng Si
Remaining useful life (RUL) prediction has been extensively recognized for its fundamental and significant value in enhancing safety, improving reliability, and reducing cost for industrial devices. The advancement of condition monitoring (CM) for degrading devices stimulates the development and prosperity of data-driven prognosis approach for RUL prediction, among which the stochastic-data-driven methods have garnered much favor of researchers for its capability to characterize the uncertainty within the predicted RUL through a probability distribution. However, despite their urgent desirability for supporting efficient decision making on management activities, analytical RUL probability distributions remain challenging to obtain, except in exceptionally limited cases. To overcome the difficulty in deriving the RUL probability distribution, a novel direct RUL prediction approach based on an inverse degradation modeling framework has been presented in this paper. A semi-parametric inverse degradation model integrating population degrading characteristics via a parametric model and the unit-to-unit variability through a non-parametric model has been constructed. A two-stage approach, which identifies unknown parameters based on historical CM data in off-line stage and updates the model when new CM data of individual device are available in on-line stage, has been developed to achieve RUL prediction for an in-service degrading device. The proposed approach has been illustrated and validated by a case study of milling cutters.
{"title":"Remaining useful life prediction for stochastic deteriorating Devices: A direct approach via inverse degradation modeling","authors":"Tianmei Li, Zhenyu Cai, Zhaoju Zeng, Zhengxin Zhang, Xiaosheng Si","doi":"10.1016/j.ymssp.2025.112431","DOIUrl":"10.1016/j.ymssp.2025.112431","url":null,"abstract":"<div><div>Remaining useful life (RUL) prediction has been extensively recognized for its fundamental and significant value in enhancing safety, improving reliability, and reducing cost for industrial devices. The advancement of condition monitoring (CM) for degrading devices stimulates the development and prosperity of data-driven prognosis approach for RUL prediction, among which the stochastic-data-driven methods have garnered much favor of researchers for its capability to characterize the uncertainty within the predicted RUL through a probability distribution. However, despite their urgent desirability for supporting efficient decision making on management activities, analytical RUL probability distributions remain challenging to obtain, except in exceptionally limited cases. To overcome the difficulty in deriving the RUL probability distribution, a novel direct RUL prediction approach based on an inverse degradation modeling framework has been presented in this paper. A semi-parametric inverse degradation model integrating population degrading characteristics via a parametric model and the unit-to-unit variability through a non-parametric model has been constructed. A two-stage approach, which identifies unknown parameters based on historical CM data in off-line stage and updates the model when new CM data of individual device are available in on-line stage, has been developed to achieve RUL prediction for an in-service degrading device. The proposed approach has been illustrated and validated by a case study of milling cutters.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112431"},"PeriodicalIF":7.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143217595","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}
Pub Date : 2025-02-06DOI: 10.1016/j.ymssp.2025.112426
Pengcheng Li , Haiheng Zhang , Xin Zhao , Huaqing Jin , Jun Ding , Daolin Xu
Ocean waves represent a vast and renewable resource that is prevalent across the globe. However, the relentless erosion of marine equipment and coastal structures poses an ongoing challenge to safety. The integration of a floating breakwater with a wave energy converter (FB-WEC) offers a dual solution that addresses both wave protection and energy harnessing. The attenuation of low-frequency ocean waves and their subsequent energy capture is a critical issue within the field of ocean engineering. The introduction of additional nonlinear stiffness can significantly enhance the low-frequency response of FB-WECs without the need to enlarge their physical dimensions. To address the complex nonlinear fluid–structure interactions inherent in nonlinear FB-WECs, a hybrid time–frequency domain approach has been developed. This method is based on the concept of harmonic decomposition and enables the rapid computation of the FB-WEC’s motion response while facilitating the concurrent acquisition of wave data. An innovative umbrella-type bistable mechanism (U-BM) has been conceived and implemented in the FB-WEC design. A prototype has been fabricated, and its performance was tested through wave flume experiments. The results of these experiments have validated the numerical simulations, confirming that the U-BM FB-WEC is proficient at responding to low-amplitude wave excitations. Under conditions of comparable wave height, the U-BM FB-WEC consistently delivers over 50% more power output in the low-frequency band compared to its linear counterpart. This advancement marks a significant stride in the field of wave energy conversion, promising more efficient energy capture and a more sustainable future for marine environments and coastal communities.
{"title":"Mixed time-frequency-domain method for nonlinear hybrid floating breakwater-WEC","authors":"Pengcheng Li , Haiheng Zhang , Xin Zhao , Huaqing Jin , Jun Ding , Daolin Xu","doi":"10.1016/j.ymssp.2025.112426","DOIUrl":"10.1016/j.ymssp.2025.112426","url":null,"abstract":"<div><div>Ocean waves represent a vast and renewable resource that is prevalent across the globe. However, the relentless erosion of marine equipment and coastal structures poses an ongoing challenge to safety. The integration of a floating breakwater with a wave energy converter (FB-WEC) offers a dual solution that addresses both wave protection and energy harnessing. The attenuation of low-frequency ocean waves and their subsequent energy capture is a critical issue within the field of ocean engineering. The introduction of additional nonlinear stiffness can significantly enhance the low-frequency response of FB-WECs without the need to enlarge their physical dimensions. To address the complex nonlinear fluid–structure interactions inherent in nonlinear FB-WECs, a hybrid time–frequency domain approach has been developed. This method is based on the concept of harmonic decomposition and enables the rapid computation of the FB-WEC’s motion response while facilitating the concurrent acquisition of wave data. An innovative umbrella-type bistable mechanism (U-BM) has been conceived and implemented in the FB-WEC design. A prototype has been fabricated, and its performance was tested through wave flume experiments. The results of these experiments have validated the numerical simulations, confirming that the U-BM FB-WEC is proficient at responding to low-amplitude wave excitations. Under conditions of comparable wave height, the U-BM FB-WEC consistently delivers over 50% more power output in the low-frequency band compared to its linear counterpart. This advancement marks a significant stride in the field of wave energy conversion, promising more efficient energy capture and a more sustainable future for marine environments and coastal communities.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112426"},"PeriodicalIF":7.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280507","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}
Pub Date : 2025-02-06DOI: 10.1016/j.ymssp.2025.112430
Fengshan Sun , Binghua Cao , Mengbao Fan , Lin Liu
Accurate terahertz (THz) thickness measurement of topcoat in thermal barrier coatings remains a challenge due to the change of refractive index from uneven microstructures and temperature variations. Here, a novel physics-based deep learning framework with original sparse features is proposed to measure the topcoat thickness in an accurate and low-cost manner. Firstly, the pores in the topcoat causes the THz dispersion to broaden the echoes. To decrease the effect of this factor on thickness measurements, the first three peaks are proposed to replace the entire THz signal as the sparse input features of physics-based deep learning framework. Secondly, an analytical model of THz signals considering the roughness is constructed to generate the simulated signals as the training dataset, followed by setting a wide range of refractive index to compensate the effect of uneven microstructure and temperature variations on thickness measurements. Then, a weight constraint layer is presented to assign the appropriate weights for the first three peaks based on their distortion levels to decrease the difference between the simulated training set and experimental test set. In this way, this layer is embedded into the developed deep learning framework to achieve accurate and low-cost thickness measurement of topcoat in an analytical model driven manner. Finally, the experimental and simulated results demonstrate that our method can accurately estimate the topcoat thickness, and it is superior to seven established approaches in accuracy. Meanwhile, the physics-based deep learning framework is cost-effective due to the model driven manner.
{"title":"Physics-based deep learning framework for Terahertz thickness measurement of thermal barrier coatings with variable refractive index","authors":"Fengshan Sun , Binghua Cao , Mengbao Fan , Lin Liu","doi":"10.1016/j.ymssp.2025.112430","DOIUrl":"10.1016/j.ymssp.2025.112430","url":null,"abstract":"<div><div>Accurate terahertz (THz) thickness measurement of topcoat in thermal barrier coatings remains a challenge due to the change of refractive index from uneven microstructures and temperature variations. Here, a novel physics-based deep learning framework with original sparse features is proposed to measure the topcoat thickness in an accurate and low-cost manner. Firstly, the pores in the topcoat causes the THz dispersion to broaden the echoes. To decrease the effect of this factor on thickness measurements, the first three peaks are proposed to replace the entire THz signal as the sparse input features of physics-based deep learning framework. Secondly, an analytical model of THz signals considering the roughness is constructed to generate the simulated signals as the training dataset, followed by setting a wide range of refractive index to compensate the effect of uneven microstructure and temperature variations on thickness measurements. Then, a weight constraint layer is presented to assign the appropriate weights for the first three peaks based on their distortion levels to decrease the difference between the simulated training set and experimental test set. In this way, this layer is embedded into the developed deep learning framework to achieve accurate and low-cost thickness measurement of topcoat in an analytical model driven manner. Finally, the experimental and simulated results demonstrate that our method can accurately estimate the topcoat thickness, and it is superior to seven established approaches in accuracy. Meanwhile, the physics-based deep learning framework is cost-effective due to the model driven manner.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112430"},"PeriodicalIF":7.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280509","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}
In recent years, the machine learning (ML)-based percussion method has gained considerable attention as a cost-effective and user-friendly non-destructive testing (NDT) technique. However, traditional ML classification methods fail to identify previously unseen fault levels that are not included in the training dataset, thereby limiting their practical applicability. This paper proposes a zero-shot pipeline fault detection method based on a multi-attribute learning model to identify unseen fault classes without requiring their direct signal samples during training. In this method, each fault category is represented by a six-dimensional attribute vector that characterizes its unique properties. During the attribute learning phase, a multi-attribute learning model is constructed by integrating a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM) to predict the fault attributes. Fault recognition is subsequently achieved using a Euclidean distance-based classifier, which categorizes the predicted attribute vectors based on their similarity to predefined attribute representations. The results demonstrate that when the test set originates from previously unseen pipelines, the proposed method significantly outperforms other approaches in terms of classification performance, exhibiting superior adaptability and robustness. Importantly, it effectively identifies unseen fault severity, overcoming the limitations of traditional methods. In conclusion, the proposed method offers an innovative solution to the problem of data scarcity in fault diagnosis, with promising potential for application in complex industrial environments.
{"title":"Zero-shot pipeline fault detection using percussion method and multi-attribute learning model","authors":"Longguang Peng , Wenjie Huang , Jicheng Zhang , Guofeng Du","doi":"10.1016/j.ymssp.2025.112427","DOIUrl":"10.1016/j.ymssp.2025.112427","url":null,"abstract":"<div><div>In recent years, the machine learning (ML)-based percussion method has gained considerable attention as a cost-effective and user-friendly non-destructive testing (NDT) technique. However, traditional ML classification methods fail to identify previously unseen fault levels that are not included in the training dataset, thereby limiting their practical applicability. This paper proposes a zero-shot pipeline fault detection method based on a multi-attribute learning model to identify unseen fault classes without requiring their direct signal samples during training. In this method, each fault category is represented by a six-dimensional attribute vector that characterizes its unique properties. During the attribute learning phase, a multi-attribute learning model is constructed by integrating a one-dimensional convolutional neural network (1D-CNN) with a bidirectional long short-term memory network (BiLSTM) to predict the fault attributes. Fault recognition is subsequently achieved using a Euclidean distance-based classifier, which categorizes the predicted attribute vectors based on their similarity to predefined attribute representations. The results demonstrate that when the test set originates from previously unseen pipelines, the proposed method significantly outperforms other approaches in terms of classification performance, exhibiting superior adaptability and robustness. Importantly, it effectively identifies unseen fault severity, overcoming the limitations of traditional methods. In conclusion, the proposed method offers an innovative solution to the problem of data scarcity in fault diagnosis, with promising potential for application in complex industrial environments.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112427"},"PeriodicalIF":7.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280508","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}
Pub Date : 2025-02-05DOI: 10.1016/j.ymssp.2025.112396
Arvid Trapp, Peter Wolfsteiner
In various technical applications, assessing the impact of non-Gaussian and non-stationary processes on responses of dynamic systems is crucial. While simulating time-domain realizations offers an effective solution for linear dynamic systems, this method proves time-consuming for finite element (FE) models, which may contain thousands to millions of degrees-of-freedom (DOF). Given the central role of kurtosis in describing non-Gaussianity — owing to its concise, parametric-free and easily interpretable nature — this paper introduces a highly efficient approach for deriving response kurtosis and other related statistical descriptions. This approach is based on the modal solution of dynamic systems, which allows to reduce DOF and response analyses to a minimum in the modal domain. This computational advantage enables fast assessments of non-Gaussian effects for entire FE models. Our approach is illustrated using a simple FE model that has found regular use in the field of random vibration fatigue.
{"title":"Fast assessment of non-Gaussian inputs in structural dynamics exploiting modal solutions","authors":"Arvid Trapp, Peter Wolfsteiner","doi":"10.1016/j.ymssp.2025.112396","DOIUrl":"10.1016/j.ymssp.2025.112396","url":null,"abstract":"<div><div>In various technical applications, assessing the impact of non-Gaussian and non-stationary processes on responses of dynamic systems is crucial. While simulating time-domain realizations offers an effective solution for linear dynamic systems, this method proves time-consuming for finite element (FE) models, which may contain thousands to millions of degrees-of-freedom (DOF). Given the central role of kurtosis in describing non-Gaussianity — owing to its concise, parametric-free and easily interpretable nature — this paper introduces a highly efficient approach for deriving response kurtosis and other related statistical descriptions. This approach is based on the modal solution of dynamic systems, which allows to reduce DOF and response analyses to a minimum in the modal domain. This computational advantage enables fast assessments of non-Gaussian effects for entire FE models. Our approach is illustrated using a simple FE model that has found regular use in the field of random vibration fatigue.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112396"},"PeriodicalIF":7.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280638","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}
Pub Date : 2025-02-05DOI: 10.1016/j.ymssp.2025.112418
Quankun Li , Heyu Hu , Mingfu Liao , Xingjian Jing
For diagnosing rub-impact faults in rotor systems, numerous advanced methods leveraging nonlinear vibration features such as Frequency Response Function (FRF), Output Frequency Response (OFR), and Transmissibility Function (TF) have been developed and implemented. Addressing limitations in existing methods, such as the need for reference data from healthy rotors, neglect of nonlinear supports, and focus on single-disk rub-impact faults, this paper introduces a novel systematic approach using nonlinear TF-based indexes. Initially, a comprehensive nonlinear rotor dynamic model is established, incorporating unbalance forces, rub-impact forces, and nonlinear support forces. The nonlinear TF is then defined through nonlinear output spectra. By exciting the rotor system four times with varying unbalance force magnitudes and focusing on a single-disk rotor sub-model, two fault features based on nonlinear TFs and rub-impact fault forces are identified. This innovative approach, featuring sensitive fault indexes and detailed operational procedures, is validated through extensive numerical studies and experimental comparisons on a lab rotor system with multi-disk rub-impact faults and nonlinear supports. The study presents a groundbreaking and effective method for detecting and localizing multi-disk rub-impact faults in rotor systems, even with nonlinear supports.
{"title":"A nonlinear transmissibility function-based diagnosis approach for multi-disks rub-impact faults in rotor systems with nonlinear supports","authors":"Quankun Li , Heyu Hu , Mingfu Liao , Xingjian Jing","doi":"10.1016/j.ymssp.2025.112418","DOIUrl":"10.1016/j.ymssp.2025.112418","url":null,"abstract":"<div><div>For diagnosing rub-impact faults in rotor systems, numerous advanced methods leveraging nonlinear vibration features such as Frequency Response Function (FRF), Output Frequency Response (OFR), and Transmissibility Function (TF) have been developed and implemented. Addressing limitations in existing methods, such as the need for reference data from healthy rotors, neglect of nonlinear supports, and focus on single-disk rub-impact faults, this paper introduces a novel systematic approach using nonlinear TF-based indexes. Initially, a comprehensive nonlinear rotor dynamic model is established, incorporating unbalance forces, rub-impact forces, and nonlinear support forces. The nonlinear TF is then defined through nonlinear output spectra. By exciting the rotor system four times with varying unbalance force magnitudes and focusing on a single-disk rotor sub-model, two fault features based on nonlinear TFs and rub-impact fault forces are identified. This innovative approach, featuring sensitive fault indexes and detailed operational procedures, is validated through extensive numerical studies and experimental comparisons on a lab rotor system with multi-disk rub-impact faults and nonlinear supports. The study presents a groundbreaking and effective method for detecting and localizing multi-disk rub-impact faults in rotor systems, even with nonlinear supports.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112418"},"PeriodicalIF":7.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143280506","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}
Pub Date : 2025-02-04DOI: 10.1016/j.ymssp.2025.112416
Jiahui Wang , Jing Liu , Guang Pan
Improving low-frequency isolation performance and suppressing resonance transmission under the time-varying excitation are essential requirements for suppressing the vibration of underwater vehicles. Based on the vibration energy dispersion characteristics of time-varying systems in the frequency domain, the author proposed a time-varying isolation platform (TVIP) in previous research to broaden the low-frequency isolation range and suppress resonance. The time-varying distribution of isolators makes it difficult to separate spatial and temporal variables, so previous work only verified the isolation effect of TVIP through numerical solutions without delving into the mechanism of suppressing resonance. The existing time-varying structural dynamics calculation methods are difficult to solve this problem. This manuscript proposes a time-domain segmented computation (TDPC) method for analyzing the time-varying vibration characteristics of TVIP. The mechanism of suppressing resonance energy transfer in TVIP has been analyzed, which involves the disruption of the steady-state establishment process and the interaction between free modes. By comparing with finite element (FE) simulation results, it has been verified that this calculation method is effective for solving the response of the TVIP. The proposed time-domain piecewise calculation (TDPC) method not only simulates the dynamic evolution process of forced and free vibrations, but also greatly reduces computation time. The experimental and calculation results both confirm that the time-varying isolation distribution changes the inherent characteristics of the isolation system and reduces resonance energy transfer. The resonance frequency variation leads to the scatter of vibration energy in frequency domain. The influences of the isolator movement speed and path on the resonance peak are discussed. The vibration suppression effect of the TVIP under the multifrequency excitations is discussed. The results indicate that increasing the movement speed and resonance frequency variation range is beneficial for suppressing resonance transmission. The TVIP under multifrequency excitation can suppress low-frequency resonance and suit time-varying excitation situations.
{"title":"A time-domain piecewise calculation method of a time-varying isolation platform","authors":"Jiahui Wang , Jing Liu , Guang Pan","doi":"10.1016/j.ymssp.2025.112416","DOIUrl":"10.1016/j.ymssp.2025.112416","url":null,"abstract":"<div><div>Improving low-frequency isolation performance and suppressing resonance transmission under the time-varying excitation are essential requirements for suppressing the vibration of underwater vehicles. Based on the vibration energy dispersion characteristics of time-varying systems in the frequency domain, the author proposed a time-varying isolation platform (TVIP) in previous research to broaden the low-frequency isolation range and suppress resonance. The time-varying distribution of isolators makes it difficult to separate spatial and temporal variables, so previous work only verified the isolation effect of TVIP through numerical solutions without delving into the mechanism of suppressing resonance. The existing time-varying structural dynamics calculation methods are difficult to solve this problem. This manuscript proposes a time-domain segmented computation (TDPC) method for analyzing the time-varying vibration characteristics of TVIP. The mechanism of suppressing resonance energy transfer in TVIP has been analyzed, which involves the disruption of the steady-state establishment process and the interaction between free modes. By comparing with finite element (FE) simulation results, it has been verified that this calculation method is effective for solving the response of the TVIP. The proposed time-domain piecewise calculation (TDPC) method not only simulates the dynamic evolution process of forced and free vibrations, but also greatly reduces computation time. The experimental and calculation results both confirm that the time-varying isolation distribution changes the inherent characteristics of the isolation system and reduces resonance energy transfer. The resonance frequency variation leads to the scatter of vibration energy in frequency domain. The influences of the isolator movement speed and path on the resonance peak are discussed. The vibration suppression effect of the TVIP under the multifrequency excitations is discussed. The results indicate that increasing the movement speed and resonance frequency variation range is beneficial for suppressing resonance transmission. The TVIP under multifrequency excitation can suppress low-frequency resonance and suit time-varying excitation situations.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"228 ","pages":"Article 112416"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161823","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}
Pub Date : 2025-02-04DOI: 10.1016/j.ymssp.2025.112375
Georgios I. Florakis, Konstantinos A. Kapasakalis, Evangelos J. Sapountzakis
This study investigates the frequency-domain optimization of base isolated multi-story buildings equipped with novel negative stiffness (NS) vibration control systems (VCS). The examined VCS include the KDamper, the Extended KDamper (EKD) and the Enhanced KDamper (ENKD). Previous research has demonstrated the effectiveness of these devices for seismic protection, by implementing them as standalone base absorbers employing single-objective, time-domain optimization methods. This study explores the application of the VCS as supplementary components of base isolated structures, utilizing multi-objective optimization approaches in the frequency-domain. The objective is to achieve robust designs that balance trade-offs between competing objectives, while optimizing performance across a wide frequency range yielding a more holistic methodology, as compared to time-domain approaches. The optimization framework employs the non-dominated sorting genetic algorithm type II (NSGA-II). Two distinct frequency-domain optimization approaches are considered. The first minimizes transfer functions associated with key, opposing dynamic responses, such as the base displacements and top-floor accelerations of the multi-story structure. The second approach minimizes the root mean square (RMS) values of these responses. The level of intrusiveness of the VCS on the examined building is also investigated, by quantitative assessing how alterations in the base frequency affect the dynamic responses. Numerical applications confirm that the optimized KDamper-based devices improve the building’s dynamic performance, resulting in significantly low base displacement levels. A comparative analysis is also conducted between the two proposed optimization approaches in terms of resulting design variables and dynamic responses.
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