Article
Engineering, Mechanical
Dongming Hou, Hongyuan Qi, Defa Li, Cuiping Wang, Defu Han, Honglin Luo, Chang Peng
Summary: This study explores the AE detection mechanism of the high-speed train wheel set bearings (HSTWSB) state, establishes an AE mathematical model, thoroughly analyzes and models the influences of different damage types, providing theoretical support for bearing condition monitoring with AE.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Vignesh V. Shanbhag, Thomas J. J. Meyer, Leo W. Caspers, Rune Schlanbusch
Summary: This study aims to monitor degradation of multiple components simultaneously in hydraulic cylinders using acoustic emissions. The experiments showed good repeatability in identifying piston rod seal wear, bearing wear, and fluid leakage initiation using acoustic emission features.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Article
Engineering, Mechanical
F. Koenig, C. Sous, A. Ouald Chaib, G. Jacobs
Summary: The study aims to monitor and classify the multi-variant wear behavior of sliding bearings using acoustic emission (AE) technique and deep learning based on convolutional neural networks. It successfully achieved high accuracy and sensitivity in detecting three-body abrasion due to particle contamination.
TRIBOLOGY INTERNATIONAL
(2021)
Article
Engineering, Chemical
Oliver Mey, Andre Schneider, Olaf Enge-Rosenblatt, Dirk Mayer, Christian Schmidt, Samuel Klein, Hans-Georg Herrmann
Summary: Early detection and classification of damage is crucial for predictive maintenance in manufacturing systems and industrial facilities. By integrating vibration and acoustic emission sensors, along with using a test rig containing artificial damages for data acquisition, it was shown that an improvement in damage classification can be achieved through the proposed algorithm.
Article
Engineering, Electrical & Electronic
Linjiang Tang, Xiaoqin Liu, Xing Wu, Zhihai Wang
Summary: This paper proposes an AE event filtering method for low-speed bearing defect localization, which combines improved denoising and continuous wavelet transform to successfully filter noise events and achieve accurate localization of bearing damage sources.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Tomasz Nowakowski, Franciszek Tomaszewski, Pawel Komorski, Grzegorz M. Szymanski
Summary: The article presents the genesis of a method to diagnose a tram transmission based on acoustic signals from the track position. The influence of a damaged gearbox on acoustic phenomena near the tram line, specifically changes in psychoacoustic indicators, was demonstrated. Nonstationary acoustic signals were analyzed using empirical mode decomposition. The developed quantitative measure served as a classifier in decision trees. An effective tree was selected based on calculated diagnostic indicators, and an algorithm to diagnose the tram transmission without mounting equipment on the vehicle was developed.
Article
Engineering, Mechanical
P. Revill, A. Clarke, R. Pullin, G. Dennis
Summary: Self-lubricating composite bearing liners used in aerospace systems require periodic replacement, and research is focusing on developing smart bearings that can identify critical damage to improve asset availability and reduce maintenance costs. This study demonstrates the feasibility of monitoring wear processes using Acoustic Emission (AE) in a laboratory setting, and introduces a new method of analyzing AE signals for integration into a Structural Health Monitoring (SHM) system for asset management.
Article
Chemistry, Analytical
Guo Bi, Shan Liu, Shibo Su, Zhongxue Wang
Summary: Acoustic emission (AE) phenomenon is directly related to the interaction of tool and material, making it the most sensitive variable among various process variables. Feature representation is crucial for AE-based monitoring, determining the accuracy of the monitoring system.
Article
Engineering, Mechanical
Philipp Renhart, Michael Maier, Christopher Strablegg, Florian Summer, Florian Grun, Andreas Eder
Summary: The measurement of acoustic emission data in experiments provides valuable details about tribological contact, but long-term measurements result in large datasets and blind spots. The study presents effective postprocessing methods and a feature-based data acquisition method, identifying a two-stage wear mechanism for bearings.
Article
Engineering, Electrical & Electronic
Yanfei Zhang, Yunhao Li, Lingfei Kong, Qingbo Niu, Yu Bai
Summary: In this article, an improved analysis approach called IDBSCAN based on kurtosis and sample entropy is proposed for accurate monitoring of spindle operation condition. The method utilizes wavelet noise reduction and cluster analysis to identify bearing operation state, achieving high accuracy in experimental validation.
Article
Computer Science, Artificial Intelligence
Mohsen Motahari-Nezhad, Seyed Mohammad Jafari
Summary: This paper discusses the estimation of the remaining useful life of angular contact ball bearings using time-domain signal processing methods, introducing 60 time-domain features for fault detection and utilizing the IDE method for feature dimensionality reduction. The KNN algorithm is used for bearing classification based on selected features, with results showing high precision in fault detection. The study validates the performance of the KNN classifier with performance indices, highlighting the importance of features such as kurtosis in achieving high accuracy, precision, and specificity in bearing classification.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Khandaker Noman, Yongbo Li, Zhike Peng, Shun Wang
Summary: This research achieves sparsity index guided bearing health monitoring by oscillation based decomposition of vibration signals, aiming at the limitations of conventional bearing health monitoring indices under varying speed operating condition. The low oscillatory component of a vibration signal is separated with the help of tunable Q factor wavelet transform (TQWT), and the health of rolling element bearing is monitored by quantifying the extracted signal component using four prominent sparsity indices.
IEEE SENSORS JOURNAL
(2022)
Article
Acoustics
Chia-Hsuan Shen
Summary: Surface grinding is crucial in achieving the desired workpiece quality by maintaining proper wheel condition, and machine learning and AE detection show potential for developing an effective monitoring system. Through experiments, it was found that using a higher frequency AE sensor in combination with FFT can achieve the best classification results. It was also determined that a window length as short as 1 ms can capture enough signal information, and the choice of sampling instance does not significantly impact classification outcomes.
Article
Construction & Building Technology
Fengbo Ma, Xiaoxiang Cheng, Xiangyi Zhu, Gang Wu, De-Cheng Feng, Shitong Hou, Xuecheng Kang
Summary: This paper discusses the importance and challenges of bearing replacement in high-speed railway bridges. It proposes an innovative method for bearing replacement and conducts implementation and monitoring. The results show that this method is a promising practice and can guide similar projects in the future.
JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES
(2022)
Article
Engineering, Mechanical
Chao Liu, Cheng He, Tianyu Han, Haoran Sun, Songtao Hu, Xi Shi
Summary: The distinctive symptom of machine fault signal is cyclic transients. The ratio of cyclic content (RCC) and the form factor indexes have been proven successful in characterizing the non-stationarity and impulsiveness of the machine fault signal. However, the original versions rely on estimating higher-order moments with poorer estimation variance properties. This work proposes a low-variance version of RCC and form factor indexes, which fluctuate less in the machine's normal stage and provides a novel perspective on the black-box CNN model in intelligent fault diagnosis.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Industrial
Yifan Zhou, Yiming Guo, Tian Ran Lin, Lin Ma
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2018)
Article
Engineering, Multidisciplinary
Kun Yu, Tian Ran Lin, Jiwen Tan, Hui Ma
Article
Engineering, Multidisciplinary
Kun Yu, Tian Ran Lin, Jiwen Tan
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2020)
Article
Engineering, Electrical & Electronic
Kun Yu, Tian Ran Lin, Hui Ma, Hongfei Li, Jin Zeng
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Mechanical
Gang Yu, Tian Ran Lin
Summary: A new high-resolution time-frequency analysis method STET is proposed for machine fault diagnosis in this paper. The limitations of two post-processing techniques are discussed, and STET is introduced to overcome these limitations, with results confirming its effectiveness in analyzing noise contaminated signals and bearing defect signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Mechanical
Kun Yu, Tian Ran Lin, Hui Ma, Xiang Li, Xu Li
Summary: A three-stage semi-supervised learning method is proposed in this study for intelligent bearing fault diagnosis under limited labeled data, using data augmentation and metric learning to enhance classifier performance. Experimental results demonstrate that the proposed method outperforms existing diagnostic methods in bearing fault diagnosis under limited labeled samples.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Multidisciplinary
Kun Yu, Qiang Fu, Hui Ma, Tian Ran Lin, Xiang Li
Summary: This study proposes a simulation data-driven domain adaptation method for intelligent fault diagnosis of mechanical equipment. By using diagnostic knowledge learned from simulation data, the healthy mode identification of mechanical equipment can be achieved in the actual field.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Automation & Control Systems
Gang Yu, Tianran Lin, Zhonghua Wang, Yueyang Li
Summary: A concentrated TFA method based on TSST is proposed in this article to accurately capture the impulse features in CM signals for fault diagnosis. An iteration procedure is introduced to address the blurry time frequency representation problem of TSST, providing algorithmic support for signal reconstructions. Results confirm that the proposed method has better performance in dealing with impulsive-like signals compared to other TFA methods.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Multidisciplinary
Yueyang Li, Zhenjin Shi, Tian Ran Lin, Gang Yu
Summary: In this study, a new TFA post-processing tool called local maximum high order time iterative synchrosqueezing (LHTIS) method is proposed to improve the performance of time-frequency analysis. By reassigning the signal and maximizing the local TF coefficients, the TF resolution and anti-noise property can be enhanced.
Article
Engineering, Mechanical
Kang Xi Sang, Jie Shang, Tian Ran Lin
Summary: This study presents an automated fault diagnosis technique for varying speed bearings using Synchroextracting Transform (SET) and deep residual network (DRN). The results show that the proposed technique can yield accurate fault diagnosis results for non-stationary signals and strong background noise.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES
(2023)
Article
Mechanics
Kai Zhang, Tian Ran Lin, Hui Guo, Baocheng Zhang
Summary: This study presents an analytical solution using a double finite sine integral transform method for predicting the sound radiation of a ribbed rectangular plate structure under different boundary conditions. The effects of ribs on the radiated sound power and directivity of the plate structure are investigated, and the impact of periodic ribs on sound radiation is also explored. The insights gained from this study can inspire noise design for structures such as marine platforms and high-speed rail carriages.
Article
Acoustics
Kai Zhang, Jie Pan, Tian Ran Lin, Hui Guo, Baocheng Zhang
Summary: This study presents an analytical solution for the vibro-acoustic analysis of a cavity coupled with a ribbed panel excited by an internal point sound source. The solution is validated by comparing it with results obtained from finite element analysis, showing good agreement. The model is then used to investigate sound transmission through single or multiple ribbed panels separated by air gaps. The results demonstrate that rib enhancement effectively reduces energy transmission controlled by panel control modes, while attenuation of energy transmission to the panels is more effective when multiple ribbed panels with air gaps are used. The depth of the air gaps also plays a role in sound attenuation, with larger air gaps leading to better sound attenuation.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Haoran Dong, Gang Yu, Tianran Lin, Yueyang Li
Summary: This article presents a signal processing method based on wavelet transform for time-reassigned analysis, which accurately describes the time-frequency characteristics of transient signals.
Article
Acoustics
Zhe Zhang, Xinying Wang, Zhong Yuan Liu, Qiang Fan, Tian Ran Lin
Summary: This paper presents a new design of a perforated plate-type acoustic metamaterial (PAM) that can achieve designated sound insulation while allowing air ventilation and avoiding the influence of membrane pre-tension. The study analyzes the sound insulation mechanism of a typical perforated membrane-type acoustic metamaterial and confirms that the sound transmission loss peaks are due to strong wave interference. An impedance analysis using an electro-acoustic analogy further explores the sound insulation mechanism and validates the strong sound interference as the cause of the peaks. Experimental tests and finite element simulations show that the new perforated PAM design can provide good broadband sound transmission loss at low frequencies, and a practical application in reducing noise propagation from a commercial refrigerator's compressor compartment demonstrates the effectiveness of the design in terms of sound reduction and air ventilation.
JOURNAL OF SOUND AND VIBRATION
(2023)
Proceedings Paper
Engineering, Manufacturing
Yajun Shang, Tianran Lin
Summary: In this study, a technique for rolling element bearing fault diagnosis is proposed, which is based on a second-order cyclic autocorrelation and a deep auto-encoder. It aims to address the challenges of strong noise interference and weak fault characteristics in signals acquired in practical industrial environments. The proposed technique utilizes a second-order cyclic autocorrelation algorithm to estimate the statistical features of vibration signals, highlighting the periodic impulse characteristics caused by bearing defects and suppressing random noise. A deep auto-encoder is then used to process the preprocessed feature set, combining the advantages of both denoising auto-encoder and constriction auto-encoder. Experimental data acquired from a rolling element bearing test rig is used to verify the effectiveness of the proposed technique, demonstrating higher diagnosis accuracy compared to commonly employed techniques.
PROCEEDINGS OF TEPEN 2022
(2023)
Article
Engineering, Mechanical
Xuanen Kan, Yanjun Lu, Fan Zhang, Weipeng Hu
Summary: A blade disk system is crucial for the energy conversion efficiency of turbomachinery, but differences between blades can result in localized vibration. This study develops an approximate symplectic method to simulate vibration localization in a mistuned bladed disk system and reveals the influences of initial positive pressure, contact angle, and surface roughness on the strength of vibration localization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zimeng Liu, Cheng Chang, Haodong Hu, Hui Ma, Kaigang Yuan, Xin Li, Xiaojian Zhao, Zhike Peng
Summary: Considering the calculation efficiency and accuracy of meshing characteristics of gear pair with tooth root crack fault, a parametric model of cracked spur gear is established by simplifying the crack propagation path. The LTCA method is used to calculate the time-varying meshing stiffness and transmission error, and the results are verified by finite element method. The study also proposes a crack area share index to measure the degree of crack fault and determines the application range of simplified crack propagation path.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Rongjian Sun, Conggan Ma, Nic Zhang, Chuyo Kaku, Yu Zhang, Qirui Hou
Summary: This paper proposes a novel forward calculation method (FCM) for calculating anisotropic material parameters (AMPs) of the motor stator assembly, considering structural discontinuities and composite material properties. The method is based on multi-scale theory and decouples the multi-scale equations to describe the equivalence and equivalence preconditions of AMPs of two scale models. The effectiveness of this method is verified by modal experiments.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Zhang, Jiangcen Ke
Summary: This research introduces an intelligent scheduling system framework to optimize the ship lock schedule of the Three Gorges Hub. By analyzing navigational rules, operational characteristics, and existing problems, a mixed-integer nonlinear programming model is formulated with multiple objectives and constraints, and a hybrid intelligent algorithm is constructed for optimization.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Jingjing He, Xizhong Wu, Xuefei Guan
Summary: A sensitivity and reliability enhanced ultrasonic method has been developed in this study to monitor and predict stress loss in pre-stressed multi-layer structures. The method leverages the potential breathing effect of porous cushion materials in the structures to increase the sensitivity of the signal feature to stress loss. Experimental investigations show that the proposed method offers improved accuracy, reliability, and sensitivity to stress change.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Benyamin Hosseiny, Jalal Amini, Hossein Aghababaei
Summary: This paper presents a method for monitoring sub-second or sub-minute displacements using GBSAR signals, which employs spectral estimation to achieve multi-dimensional target detection. It improves the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xianze Li, Hao Su, Ling Xiang, Qingtao Yao, Aijun Hu
Summary: This paper proposes a novel method for bearing fault identification, which can accurately identify faults with few samples under complex working conditions. The method is based on a Transformer meta-learning model, and the final result is determined by the weighted voting of multiple models.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaomeng Li, Yi Wang, Guangyao Zhang, Baoping Tang, Yi Qin
Summary: Inspired by chaos fractal theory and slowly varying damage dynamics theory, this paper proposes a new health monitoring indicator for vibration signals of rotating machinery, which can effectively monitor the mechanical condition under both cyclo-stationary and variable operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Hao Wang, Songye Zhu
Summary: This paper extends the latching mechanism to vibration control to improve energy dissipation efficiency. An innovative semi-active latched mass damper (LMD) is proposed, and different latching control strategies are tested and evaluated. The latching control can optimize the phase lag between control force and structural response, and provide an innovative solution to improve damper effectiveness and develop adaptive semi-active dampers.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Menghao Ping, Xinyu Jia, Costas Papadimitriou, Xu Han, Chao Jiang, Wang-Ji Yan
Summary: Identification of non-Gaussian processes is a challenging task in engineering problems. This article presents an improved orthogonal series expansion method to convert the identification of non-Gaussian processes into a finite number of non-Gaussian coefficients. The uncertainty of these coefficients is quantified using polynomial chaos expansion. The proposed method is applicable to both stationary and nonstationary non-Gaussian processes and has been validated through simulated data and real-world applications.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Lei Li, Wei Yang, Dongfa Li, Jianxin Han, Wenming Zhang
Summary: The frequency locking phenomenon induced by modal coupling can effectively overcome the dependence of peak frequency on driving strength in nonlinear resonant systems and improve the stability of peak frequency. This study proposes the double frequencies locking phenomenon in a three degrees of freedom (3-DOF) magnetic coupled resonant system driven by piezoelectricity. Experimental and theoretical investigations confirm the occurrence of first frequency locking and the subsequent switching to second frequency locking with the increase of driving force. Furthermore, a mass sensing scheme for double analytes is proposed based on the double frequencies locking phenomenon.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Kai Ma, Jingtao Du, Yang Liu, Ximing Chen
Summary: This study explores the feasibility of using nonlinear energy sinks (NES) as replacements for traditional linear tuned mass dampers (TMD) in practical engineering applications, specifically in diesel engine crankshafts. The results show that NES provides better vibration attenuation for the crankshaft compared to TMD under different operating conditions.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Wentao Xu, Li Cheng, Shuaihao Lei, Lei Yu, Weixuan Jiao
Summary: In this study, a high-precision hydraulic mechanical stand and a vertical mixed-flow pumping station device were used to conduct research on cavitation signals of mixed-flow pumps. By analyzing the water pressure pulsation signal, it was found that the power spectrum density method is more sensitive and capable of extracting characteristics compared to traditional time-frequency domain analysis. This has significant implications for the identification and prevention of cavitation in mixed-flow pump machinery.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Xiaodong Chen, Kang Tai, Huifeng Tan, Zhimin Xie
Summary: This paper addresses the issue of parasitic motion in microgripper jaws and its impact on clamping accuracy, and proposes a symmetrically stressed parallelogram mechanism as a solution. Through mechanical modeling and experimental validation, the effectiveness of this method is demonstrated.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)
Article
Engineering, Mechanical
Zhifeng Shi, Gang Zhang, Jing Liu, Xinbin Li, Yajun Xu, Changfeng Yan
Summary: This study provides useful guidance for early bearing fault detection and diagnosis by investigating the effects of crack inclination and propagation direction on the vibration characteristics of bearings.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2024)