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The journal bearings are widely used in the high load rotation machine. The dynamic coefficients of the bearings a critical factor in the fault diagnosis of the system. In this paper, a model-based iteration method is described to identify the bearing dynamic coefficients. The model system is created firstly by the finite element method. Secondly, the principle of the method is proposed combining with the Kalman filter and Tikhonov’s regularisation method, which uses the theoretical dynamic coefficients to estimate the new bearing displacement in the horizontal and vertical directions using single measurement and then recalculate the dynamic coefficients. Finally, to verify this method, numerical simulations are carried out under different rotation speeds. The results show that this method has high confidence intervals in the identification of both stiffness and damping coefficients. This method would provide a new route for the identification of the bearing dynamic coefficients and dynamic analysis for the rotor-bearing system.
Yang Kang; Zizhen Qiu; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. A Model-Based Iteration Method of the Estimation for the Bearings Dynamic Coefficients. Proceedings of the 9th IFToMM International Conference on Rotor Dynamics 2021, 709 -717.
AMA StyleYang Kang, Zizhen Qiu, Hao Zhang, Zhanqun Shi, Fengshou Gu, Andrew Ball. A Model-Based Iteration Method of the Estimation for the Bearings Dynamic Coefficients. Proceedings of the 9th IFToMM International Conference on Rotor Dynamics. 2021; ():709-717.
Chicago/Turabian StyleYang Kang; Zizhen Qiu; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. 2021. "A Model-Based Iteration Method of the Estimation for the Bearings Dynamic Coefficients." Proceedings of the 9th IFToMM International Conference on Rotor Dynamics , no. : 709-717.
Surface roughness effects on the dynamic responses of hydrodynamic journal bearing have attracted many researchers’ attention for developing both more efficient lubrication mechanisms and accurate online diagnostics. This paper develops the model of dimensionless Reynolds equation based on the short bearing lubrication theory, which considers the influence of the surface profiles parameters. Various random surfaces are generated under the constraints of root mean square (RMS) and correlation-length, which are used to discuss the effect of surface characteristics on the pressure distributions of journal bearings. The results based on a series of simulations show that anisotropic surface obviously affects the pressure distributions. RMS and correlation-length of surface profiles act on the amplitude and density of pressures distributions, respectively. These results investigate the effectiveness of the model and the findings are meaningful to detect the early faults of journal bearings.
Jiaojiao Ma; Hao Zhang; Zhanqun Shi; Fulong Liu; Fengshou Gu; Andrew D. Ball. Modelling the Effect of Surface Roughness on Pressure Profiles in Hydrodynamic Journal Bearings. Proceedings of the 9th IFToMM International Conference on Rotor Dynamics 2021, 636 -643.
AMA StyleJiaojiao Ma, Hao Zhang, Zhanqun Shi, Fulong Liu, Fengshou Gu, Andrew D. Ball. Modelling the Effect of Surface Roughness on Pressure Profiles in Hydrodynamic Journal Bearings. Proceedings of the 9th IFToMM International Conference on Rotor Dynamics. 2021; ():636-643.
Chicago/Turabian StyleJiaojiao Ma; Hao Zhang; Zhanqun Shi; Fulong Liu; Fengshou Gu; Andrew D. Ball. 2021. "Modelling the Effect of Surface Roughness on Pressure Profiles in Hydrodynamic Journal Bearings." Proceedings of the 9th IFToMM International Conference on Rotor Dynamics , no. : 636-643.
Vibration analysis is an effective approach to condition monitoring of hydrodynamic journal bearings. However, the analysis is largely based on the understanding of asperity-asperity interactions due to severe wears in the late phase of bearing lifetime. It often provides very tight leading time for maintenance actions. Aiming at developing effective techniques for early wear monitoring, this paper investigates the excitation mechanisms and contributions of Tribofilm-Asperity Interaction (TAI) that occurs in the hydrodynamic lubrication regime of journal bearings. Analytical expressions for the microscopic pressure fluctuations with respect to the surface topography are derived using the perturbation techniques. The Spatial Power Spectral Density (SPSD), a feature of the non-Gaussian roughness surfaces for early wear, is used to analyse the microscopic pressure fluctuations. The effect of the SPSD and operating conditions on the random excitation are evaluated through numerical simulations. The bandwidth of such random excitation depends on the SPSD of dynamic asperities and rotational speeds simultaneously. The excitation intensity increases when the standard deviation or correlation length of the surface parameters increases. These agree well with the measurements for wide bearing conditions including different degrees of wears. This new efficient analysis and insightful findings provide new understanding for characterising noisy vibration signals for early wear monitoring of journal bearings.
Jiaojiao Ma; Hao Zhang; Shan Lou; Fulei Chu; Zhanqun Shi; Fengshou Gu; Andrew D. Ball. Analytical and experimental investigation of vibration characteristics induced by tribofilm-asperity interactions in hydrodynamic journal bearings. Mechanical Systems and Signal Processing 2020, 150, 107227 .
AMA StyleJiaojiao Ma, Hao Zhang, Shan Lou, Fulei Chu, Zhanqun Shi, Fengshou Gu, Andrew D. Ball. Analytical and experimental investigation of vibration characteristics induced by tribofilm-asperity interactions in hydrodynamic journal bearings. Mechanical Systems and Signal Processing. 2020; 150 ():107227.
Chicago/Turabian StyleJiaojiao Ma; Hao Zhang; Shan Lou; Fulei Chu; Zhanqun Shi; Fengshou Gu; Andrew D. Ball. 2020. "Analytical and experimental investigation of vibration characteristics induced by tribofilm-asperity interactions in hydrodynamic journal bearings." Mechanical Systems and Signal Processing 150, no. : 107227.
The displacements at the bearing locations play an important role in identifying the dynamic coefficients of the journal bearing. In this paper, an identification method for the oil-film dynamic coefficients is presented. First of all, a discrete state-space model of the bearing-rotor system is generated. Secondly, Kalman filter technique is used to obtain the displacements of the bearing locations. Then, these estimated responses are adopted to identify the dynamic coefficients. Finally, a comparison is developed between the identified results and the theoretical value. The results showed that the present method has high accurate for the identification of the dynamic coefficients, especially, for the direct dynamic coefficients.
Yang Kang; Jiaojiao Ma; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System. Blockchain Technology and Innovations in Business Processes 2020, 1273 -1284.
AMA StyleYang Kang, Jiaojiao Ma, Hao Zhang, Zhanqun Shi, Fengshou Gu, Andrew Ball. Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System. Blockchain Technology and Innovations in Business Processes. 2020; ():1273-1284.
Chicago/Turabian StyleYang Kang; Jiaojiao Ma; Hao Zhang; Zhanqun Shi; Fengshou Gu; Andrew Ball. 2020. "Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System." Blockchain Technology and Innovations in Business Processes , no. : 1273-1284.
The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.
Zhaozong Meng; Mingxing Zhang; Changxin Guo; Qirui Fan; Hao Zhang; Nan Gao; Zonghua Zhang. Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis. Electronics 2020, 9, 1357 .
AMA StyleZhaozong Meng, Mingxing Zhang, Changxin Guo, Qirui Fan, Hao Zhang, Nan Gao, Zonghua Zhang. Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis. Electronics. 2020; 9 (9):1357.
Chicago/Turabian StyleZhaozong Meng; Mingxing Zhang; Changxin Guo; Qirui Fan; Hao Zhang; Nan Gao; Zonghua Zhang. 2020. "Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis." Electronics 9, no. 9: 1357.
This paper investigates the mechanism and characteristics of Acoustic Emission (AE) generated from the dynamic Fluid-Asperities Shearing (FAS) in the hydrodynamic lubrication (HL) regime. Firstly, a FAS model is derived to take into account the dynamic effect of surface asperities. Then, the influence of surface profiles, lubricants and operating conditions are illustrated on AE characteristics, i.e. magnitudes and frequency bandwidths. It has been found that the correlation length of surface roughness parameters and shear rate are two main factors affecting FAS behaviours and consequently AE characteristics. Finally, the corresponding experiments are carried out based on a rheometer rig, which validate the predictability of the model and new findings, paving the foundation for developing AE based monitoring techniques.
Jiaojiao Ma; Hao Zhang; Zhanqun Shi; Fulei Chu; Fengshou Gu; Andrew D. Ball. Modelling Acoustic Emissions induced by dynamic fluid-asperity shearing in hydrodynamic lubrication regime. Tribology International 2020, 153, 106590 .
AMA StyleJiaojiao Ma, Hao Zhang, Zhanqun Shi, Fulei Chu, Fengshou Gu, Andrew D. Ball. Modelling Acoustic Emissions induced by dynamic fluid-asperity shearing in hydrodynamic lubrication regime. Tribology International. 2020; 153 ():106590.
Chicago/Turabian StyleJiaojiao Ma; Hao Zhang; Zhanqun Shi; Fulei Chu; Fengshou Gu; Andrew D. Ball. 2020. "Modelling Acoustic Emissions induced by dynamic fluid-asperity shearing in hydrodynamic lubrication regime." Tribology International 153, no. : 106590.
The vibration of a planetary gearbox (PG) is complex and mutually modulated, which makes the weak features of incipient fault difficult to detect. To target this problem, a novel method, based on an adaptive order bispectrum slice (AOBS) and the fault characteristics energy ratio (FCER), is proposed. The order bispectrum (OB) method has shown its effectiveness in the feature extraction of bearings and fixed-shaft gearboxes. However, the effectiveness of the PG still needs to be explored. The FCER is developed to sum up the fault information, which is scattered by mutual modulation. In this method, the raw vibration signal is firstly converted to that in the angle domain. Secondly, the characteristic slice of AOBS is extracted. Different from the conventional OB method, the AOBS is extracted by searching for a characteristic carrier frequency adaptively in the sensitive range of signal coupling. Finally, the FCER is summed up and calculated from the fault features that were dispersed in the characteristic slice. Experimental data was processed, using both the AOBS-FCER method, and the method that combines order spectrum analysis with sideband energy ratio (OSA-SER), respectively. Results indicated that the new method is effective in incipient fault feature extraction, compared with the methods of OB and OSA-SER.
Zhaoyang Shen; Zhanqun Shi; Dong Zhen; Hao Zhang; Fengshou Gu. Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis. Sensors 2020, 20, 2433 .
AMA StyleZhaoyang Shen, Zhanqun Shi, Dong Zhen, Hao Zhang, Fengshou Gu. Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis. Sensors. 2020; 20 (8):2433.
Chicago/Turabian StyleZhaoyang Shen; Zhanqun Shi; Dong Zhen; Hao Zhang; Fengshou Gu. 2020. "Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis." Sensors 20, no. 8: 2433.
The dynamic coefficients identification of journal bearings is essential for instability analysis of rotation machinery. Aiming at the measured displacement of a single location, an improvement method associated with the Kalman filter is proposed to estimate the bearing dynamic coefficients. Firstly, a finite element model of the flexible rotor-bearing system was established and then modified by the modal test. Secondly, the model-based identification procedure was derived, in which the displacements of the shaft at bearings locations were estimated by the Kalman filter algorithm to identify the dynamic coefficients. Finally, considering the effect of the different process noise covariance, the corresponding numerical simulations were carried out to validate the preliminary accuracy. Furthermore, experimental tests were conducted to confirm the practicality, where the real stiffness and damping were comprehensively identified under the different operating conditions. The results show that the proposed method is not only highly accurate, but also stable under different measured locations. Compared with the conventional method, this study presents a more than high practicality approach to identify dynamic coefficients, including under the resonance condition. With high efficiency, it can be extended to predict the dynamic behaviour of rotor-bearing systems.
Yang Kang; Zhanqun Shi; Hao Zhang; Dong Zhen; Fengshou Gu. A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter. Sensors 2020, 20, 565 .
AMA StyleYang Kang, Zhanqun Shi, Hao Zhang, Dong Zhen, Fengshou Gu. A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter. Sensors. 2020; 20 (2):565.
Chicago/Turabian StyleYang Kang; Zhanqun Shi; Hao Zhang; Dong Zhen; Fengshou Gu. 2020. "A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter." Sensors 20, no. 2: 565.
Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.
Dong Zhen; Zuolu Wang; Haiyang Li; Hao Zhang; Jie Yang; Fengshou Gu. An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors. Applied Sciences 2019, 9, 3902 .
AMA StyleDong Zhen, Zuolu Wang, Haiyang Li, Hao Zhang, Jie Yang, Fengshou Gu. An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors. Applied Sciences. 2019; 9 (18):3902.
Chicago/Turabian StyleDong Zhen; Zuolu Wang; Haiyang Li; Hao Zhang; Jie Yang; Fengshou Gu. 2019. "An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors." Applied Sciences 9, no. 18: 3902.
To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.
Dong Zhen; Junchao Guo; Yuandong Xu; Hao Zhang; Fengshou Gu. A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors 2019, 19, 3994 .
AMA StyleDong Zhen, Junchao Guo, Yuandong Xu, Hao Zhang, Fengshou Gu. A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis. Sensors. 2019; 19 (18):3994.
Chicago/Turabian StyleDong Zhen; Junchao Guo; Yuandong Xu; Hao Zhang; Fengshou Gu. 2019. "A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis." Sensors 19, no. 18: 3994.
Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlation Subset based Stochastic Subspace Identification (CoS-SSI) is proposed in this paper to monitor the suspension conditions. Because of the widespread of the dynamic contact status between wheel and track, especially under faulty suspension cases, the vibration responses measured online exhibit high nonstationarity and nonlinearity. To take into account these characteristics of signals, the input correlation signals for SSI are clustered into several successive subsets according to their magnitudes, on which SSI is implemented one by one. In this way it yields a magnitude adaptive SSI for more reliable and accurate identification. Experimental studies were conducted on a 1/5th scaled roller rig system to verify the effectiveness of the proposed method for suspension monitoring. The experimental results show that the CoS-SSI outperform the conventional SSI in that it produces more reliable and realistic identification for the nonlinear system. Furthermore, the effectiveness of the CoS-SSI was verified experimentally with two faulty suspension faults induced into the system.
Fulong Liu; Hao Zhang; Xiaocong He; Yunshi Zhao; Fengshou Gu; Andrew D. Ball. Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems. Vehicle System Dynamics 2019, 58, 569 -589.
AMA StyleFulong Liu, Hao Zhang, Xiaocong He, Yunshi Zhao, Fengshou Gu, Andrew D. Ball. Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems. Vehicle System Dynamics. 2019; 58 (4):569-589.
Chicago/Turabian StyleFulong Liu; Hao Zhang; Xiaocong He; Yunshi Zhao; Fengshou Gu; Andrew D. Ball. 2019. "Correlation signal subset-based stochastic subspace identification for an online identification of railway vehicle suspension systems." Vehicle System Dynamics 58, no. 4: 569-589.
Small cracks are common defects in steel and often lead to catastrophic accidents in industrial applications. Various nondestructive testing methods have been investigated for crack detection; however, most current methods focus on qualitative crack identification and image processing. In this study, eddy current pulsed thermography (ECPT) was applied for quantitative crack detection based on derivative analysis of temperature variation. The effects of the incentive parameters on the temperature variation were analyzed in the simulation study. The crack profile and position are identified in the thermal image based on the Canny edge detection algorithm. Then, one or more trajectories are determined through the crack profile in order to determine the crack boundary through its temperature distribution. The slope curve along the trajectory is obtained. Finally, quantitative analysis of the crack sizes was performed by analyzing the features of the slope curves. The experimental verification showed that the crack sizes could be quantitatively detected with errors of less than 1%. Therefore, the proposed ECPT method was demonstrated to be a feasible and effective nondestructive approach for quantitative crack detection.
Zhanqun Shi; Xiaoyu Xu; Jiaojiao Ma; Dong Zhen; Hao Zhang. Quantitative Detection of Cracks in Steel Using Eddy Current Pulsed Thermography. Sensors 2018, 18, 1070 .
AMA StyleZhanqun Shi, Xiaoyu Xu, Jiaojiao Ma, Dong Zhen, Hao Zhang. Quantitative Detection of Cracks in Steel Using Eddy Current Pulsed Thermography. Sensors. 2018; 18 (4):1070.
Chicago/Turabian StyleZhanqun Shi; Xiaoyu Xu; Jiaojiao Ma; Dong Zhen; Hao Zhang. 2018. "Quantitative Detection of Cracks in Steel Using Eddy Current Pulsed Thermography." Sensors 18, no. 4: 1070.