bluuebunny/crossref_metadata_2025_split huggingface dataset:
A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults
Authors: Hao Wang , Guangming Dong , Jin Chen , Xugang Hu , Zhibing Zhu 
Published: May 2022

Abstract
The vibration signals of gearbox often contain complex components, such as the gear engagement, shafts rotation, bearings, and vibration of the box, and they brings a big challenge to fault diagnosis of gearbox. Furthermore, there are many fault types of gearbox, and the fault characteristics of them are possibly the same. For example, the pass frequency of the missing tooth or root crack in the gear on the input shaft are both equal to the rotation frequency of input shaft. In thus circumstance, traditional diagnosis methods are inapplicable, because we can’t determine which type of faults. In this paper, we proposed a dictionary learning with the shift-invariant dictionary to extract the fault features in a sparse way, and afterwards hidden Markov model (HMM) is utilized to identify the fault type from extractive features. We used the proposed method to diagnose the experimental data from a benchmark two-stage gearbox and there are nine different health conditions introduced to the pinion on the input shaft, and the diagnosis result verifies the good performance of the novel scheme in the fault diagnosis of gearbox.
Early Fault Diagnosis of Gearbox Based on VWPSO-VMD
Authors: Cao Shunxin , Li Hongkun , Zhang Kongliang , Wang Zhaodong 
Published: May 2022

Abstract
Aiming at the problem that the characteristic of the early weak fault of the gearbox is submerged by the background noise under the low-speed operating condition, this paper proposes a gearbox fault diagnosis method based on the variable weighted particle swarm optimization variational mode decomposition (VWPSO-VMD). First, a variable weight particle swarm optimization algorithm (VWPSO) is proposed, and the effectiveness of the VWPSO is verified by the optimization calculation of test functions. Secondly, using fuzzy entropy as the fitness function, the proposed VWPSO is used to optimize the modal number K and the penalty factor α of the variational mode decomposition (VMD). Then, based on the grey relational analysis method (GRA), the optimal intrinsic mode function (IMF) is selected for envelope demodulation analysis. Finally, VWPSO-VMD and empirical mode decomposition (EMD) are used to diagnose the early crack and broken tooth fault signals of the gearbox. The results show that, compared with EMD, VWPSO-VMD can more accurately extract the early fault characteristic frequency of the gearbox. This provides a new method for the extraction of early fault features of the gearbox.
Varying-parameter time-frequency manifold for gearbox fault diagnosis
Authors: Wang Yuqi , Wang Jun , Jiang Xingxing , Huang Weiguo , Shi Juanjuan , Zhu Zhongkui 
Published: May 2022

Abstract
The vibration signals of a faulty gearbox are non-stationary and contaminated by heavy background noise. Time-frequency transform is able to present the non-stationary fault impulsive features in the time-frequency distribution (TFD). However, the time-frequency fault information is still contaminated by the noise. This paper proposes a varying-parameter time-frequency manifold (VPTFM) method with the aim to remove the noise in the TFD for accurate identification of gearbox fault. First, a high-dimensional TFD is constructed by performing short- time Fourier transform (STFT) using some variable window lengths. Then, local tangent space alignment (LTSA) algorithm is carried out on the high-dimensional TFD to extract the manifold of the fault impulsive features with two dimensions, in which Rényi entropy is employed to select the proper neighborhood size for the LTSA by evaluating the first dimensional manifold. Afterwards, a threshold is designed by exploring the characteristics of the amplitudes of the manifold at two dimensions to adaptively remove the noise survived in the first dimensional manifold. Finally, the amplitudes at the frequency possessing the largest energy in the denoised manifold are taken out for spectrum analysis to identify the fault characteristic frequency. The enhanced performance of the proposed method in extraction of fault impulses and removal of background noise is validated by a gearbox experimental vibration signal measuring when the gear has a wearing fault.
Hierarchical Diagnosis Network Based on Easy Transfer Learning and Its Application in Bearing Fault Diagnosis
Authors: Zhu Liangyu , He Shuilong , Ouyang Li , Hu Chaofan , Wang Yanxue 
Published: May 2022

Abstract
Aiming at the problem of inconsistent distribution of rolling bearing vibration data under variable operating conditions, insufficient diagnostic data of the target bearing affects the accuracy of fault diagnosis, and the unknown severity of rolling bearing faults, a hierarchical diagnosis network based on easy transfer learning is presented in this paper and its application in the qualitative and quantitative diagnosis of rolling bearing faults. First, the wavelet transform is used to extract the fault features conducive to identifying the rolling bearing vibration data under various working conditions. Then, input the features extracted from the vibration signals of different fault types into the first layer easy transfer learning fault type recognizer to determine whether the target bearing is faulty and the fault type. After the fault type is determined, the features extracted from the vibration signals of the known fault types and different fault sizes are input into the second layer easy transfer learning fault size recognizer to determine the fault size of the rolling bearing. The proposed method is validated by the bearing data set of Case Western Reserve University and compared with other transfer learning methods that perform the same processing. The experimental results show the effectiveness and superiority of the method.
Domain Adaptation for Intelligent Fault Diagnosis from Relation Perspective
Authors: Tang Lv , Zhang Qing , Xuan Jianping 
Published: May 2022

Abstract
A novel domain adaptation technology for fault diagnosis is introduced to solve the variable working condition problem. As the relationships between things are widespread according to human visual and cognitive logic, the network also learns the relation and shows its sense to classify. Thence, transfer knowledge beyond the layer features are constructed to learn a relation invariant representation. The hierarchical relation and category relation are considered, while the connection between relation alignment and feature alignment is uncovered using exceptional cases. The hierarchical relation captures the behavior of information flow in adjacent layers, and the category relation captures the similarity between bearing faults. Empirical evidence provided by the experiment with variable speed test demonstrates that the proposed model outperforms the existing method.
Attitude data-based deep transfer capsule network for intelligent fault diagnosis of delta 3D printers
Authors: Qin Y X , Hong Y , Long J Y , Yang Z , Huang Y W , Li C 
Published: May 2022

Abstract
In order to improve the quality of printed products and promote the application of 3D printing, it is necessary to carry out health monitoring and fault diagnosis for 3D printers. In this paper, an attitude data-based deep transfer capsule network is proposed for intelligent fault diagnosis of delta 3D printers. Based on the forward kinematic analysis, the attitude data change of the moving platform can reflect the fault information of the printers. To extract fault features from the attitude data with rich directional pose information and complete the cross-domain diagnosis task effectively, the proposed approach consists of a feature encoder with capsule layer, a fault pattern classifier, and a domain discriminator. Through the domain adversarial training, the model can minimize the difference between the source domain and the target domain data distribution, and the trained classifier can obtain better diagnosis performance in the target domain. The experiment result demonstrates the superiority and effectiveness of the proposed method for fault diagnosis problems of delta 3D printers.
Research on surface damage detection of wind turbinebladebased on machine vision
Authors: Wang Yu , Zou Li 
Published: May 2022

Abstract
The research of wind turbine blade surface damage identification is of great significance to reduce the risk of wind turbine operation. Aiming at the limitation of manual inspection based damage detection, this paper takes the wind turbine as the object of identification and detection, takes the advanced machine detection method as the research content. In order to realize the task of identification and detection of turbine blade damage, an image classification system of turbine blade based on convolution neural network is proposed. In this paper, the convolution neural network (CNN) based damage identification system is compared with the support vector machine (SVM) based damage identification system. The experimental results show that the CNN based damage identification system has stronger generalization ability and higher classification accuracy in the case of randomly dividing experimental sample data.
Tooth root crack detection of planet gear in RV reducer based on improved compute order tracking and angular domain synchronous averaging
Authors: Yan Y H , Guo Y , Liu X Q 
Published: May 2022

Abstract
Rotate vector (RV) reducers are widely used as the joints of industrial robots. Due to RV reducers often work in a reciprocating condition, it makes the corresponding vibration for fault detection is incomplete since the incompletely rotation of the output shaft. Thus, traditional methods such as the vibration separation (VS) cannot be used to detect the tooth fault of planet gear in RV reducer. Moreover, few literatures reported the tooth fault detection of RV reducer without disassembly. To address this issue, a method for detecting the planet gear tooth root crack fault of RV reducers is proposed in this paper. It combines the improved compute order tracking (ICOT), the signal extraction and reconstruction of planet gear, and the synchronous averaging (SA). The ICOT is utilized to obtain data blocks of each tooth of the gear on the reference shaft while eliminating the inevitable speed fluctuations. Subsequently, the tooth mapping relationship between the planet gear and sun gear can be calculated by the planetary meshing structure. Then, the data block of each tooth of the planet gear can be extracted from the resampled vibration. Next, data blocks of the planet gear can be constructed into a synthetic signal according to the tooth mapping relationship. Furthermore, the SA is applied to enhance the signal-noise ratio (SNR) of the synthetic signal. Finally, spectral analysis is utilized to expose the fault characteristics. The comparative experiment with the normal and faulty planet gear in an RV reducer test rig validated the effectiveness of the proposed method.
Fault Feature Extraction of Automobile Wheel Hub Bearing Based on RSK and RSSD
Authors: Xue H T , Zhang Z M , Wu M 
Published: May 2022

Abstract
Wheel hub bearing not only bears axial load but also radial load, and then its running status directly affects the performance and safety of the automobile. In particular, complicated and variable driving conditions of the automobile are not only liable to aggravate the occurrence of mechanical fault such as localized wear, but also often cause powerful intermittent interference noise. It is difficult to extract the vibration characteristics of the transient impact and harmonic components of the wheel bearings in the event of a local fault. An extraction method of fault feature based on ration of smooth and kurtosis (RSK) index and resonance-based signal sparse de-composition (RSSD) is proposed for automobile wheel hub bearings, which aims at the minimum value of RSK index, sequentially optimizes the Q-factor of the resonance sparse decomposition, and obtains its optimal value adaptively to achieve the decomposition of low resonance components with transient impact components and high resonance components with harmonic components. The effect of the method has been verified by powerful intermittent interference simulation signals and experiment signals from local fault of wheel hub bearing with periodic dynamic radial loading.
Rotor crack and bearing fault diagnosis based onVMD-ICA
Authors: Wang Rui , Lv Lumei , Yang Funing , Zhang Wei , Hua Chunrong , Dong Dawei 
Published: May 2022

Abstract
The rotor-bearing system is vulnerable to rotor cracks, bearing failures, or even multiple failures at the same time during long-term service, and it is more difficult to diagnose the faults under rotor crack and bearing fault coupling conditions. This paper proposed a signal reconstruction method to classify the rotor crack, the bearing outer ring fault and their coupling faults based on Variational Mode Decomposition (VMD) and Independent Component Analysis (ICA). Firstly, the original vibration signal collected on a rotor-bearing test bench is decomposed with VMD, and several intrinsic mode functions (IMF) are obtained. Then, the correlation kurtosis CK

M
which introduces the characteristics of periodic correlation function is to evaluate the decomposed IMF component, and screen the rotor crack information component. Finally, the rotor crack information component is input into a virtual channel signal and separated with the original vibration signal with ICA, and then the separated two sets of signals are superimposed to obtain a vibration signal that simultaneously highlights the information of the rotor crack and the bearing outer ring fault. In addition, the time domain signal is directly input into a LightGBM to diagnose different faults. The research results show that this method can simultaneously diagnose the rotor crack, the bearing outer ring fault and the coupling fault.
An auto-correlation-based method for structural damage identification using displacement information
Authors: Li Y J , Lei C , Li J T , Yu F J , Wang Z P , Zhang M Y 
Published: May 2022

Abstract
Vibration-based damage identification methods have been received widespread attention these days for its property of global damage identification. This paper presents a damage identification method using the Auto correlation function at Maximum point value Vector (AMV) of vision-based displacement response of the structure. Sensitivity analysis for the normalized AMV to the local stiffness shows there is a sharp change of the damage index around the damage location, which means it is a good indicator for the damage. The influence of the excitation frequency on the detect ability of this proposed method is verified by the numerical simulation and non-contact damage identification experiment of a shear frame.
A method to assess the value of monitoring an SHM system
Authors: Giordano Pier Francesco , Quqa Said , Limongelli Maria Pina 
Published: May 2022

Abstract
Aging structural components, together with the increasing transportation needs and limited budgets, are challenging aspects that typically concern decision-makers and infrastructure owners. Although Structural Health Monitoring (SHM) has been a powerful tool to optimize maintenance-related activities and post-disaster emergency management, the sensor readout and, therefore, the outcome of the monitoring system is susceptible to errors due to malfunctioning. For years, the Value of Information (VoI) has been studied to quantify the long- term benefit of SHM systems against the initial investment in sensing instrumentation without considering the eventuality of faulty sensing nodes. However, these are very common in field applications. This paper proposes a new framework to calculate the benefit of using Sensor Validation Tools (SVTs) before calculating the damage-sensitive features that drive the SHM process. The novel approach extends the traditional Vol to consider multiple “health” states of the SHM system, associate the outcome of the SHM system with the state of both the structure and the SHM system, and quantify the additional value obtained from SVTs.
Crack localization in plate-like metallic structures using vibration modulated guided waves
Authors: Hu H F , Shen G J , Yang D X , Zhang S F , Yang Y M , Guan F J 
Published: May 2022

Abstract
This paper proposes a method for crack localization in plate-like metallic structures with nonlinear vibration modulated guided waves. A spatially distributed piezoceramic transducer array is used to introduce a low-frequency modal vibration and a high-frequency ultrasonic tone burst into a metallic plate, and the responses are also acquired by the array. Due to the semi-broadband property and similar propagating speed, the nonlinear waves generated from the damage and the linear waves from other structural features overlap both in time and frequency in the acquired response signals. It is difficult to separate them by filtering method, which affects the damage localization result. The conventional pulse inversion technique is extended to extract the nonlinear signals, and then a delay-and-sum imaging method based on the Wigner-Ville distribution is applied to the extracted signals to construct a damage image. The proposed method is experimentally investigated to localize a fatigue crack near a drilled hole in an aluminium plate. The results demonstrate that the fatigue crack is localized accurately and is also distinguished from linear scatters by use of the proposed method.
The Instantaneous Spectral Entropy for Real-time, Online Structural Health Monitoring.
Authors: Civera Marco , Surace Cecilia 
Published: May 2022

Abstract
Entropy measurements have been recently proposed for the damage assessment of civil structures and mechanical systems. Here, a quasi-real-time approach, based on Instantaneous Spectral Entropy (ISE), is proposed for the detection of sudden stiffness reduction, breathing cracks, and other kinds of structural changes. The method, validated on an experimental benchmark, is suitable for nonstationary signals originating from nonlinear structures as well.
Adaptive Gaussian mixture model based structural damage monitoring method under time-varying conditions
Authors: Fang Fang , Kang Yongle , Qiu Lei , Yuan Shenfang , Yu Kaixi , Xue Zhaopeng 
Published: May 2022

Abstract
The low reliability of damage monitoring under Time-Varying Conditions (TVCs) is a key challenge for the practical application of Structural Health Monitoring (SHM) technology to aircraft structures. Among the existing SHM methods, Guided Wave (GW) and piezoelectric sensor based SHM technique is a promising method due to its high damage sensitivity and long monitoring range. Nevertheless the reliability problem still should be addressed. To deal with this problem, an adaptive Gaussian Mixture Model (GMM) based structural damage monitoring method is proposed. The probability distribution of GW features under TVCs is represented by the GMM. The center of each Gaussian component in a GMM is determined adaptively first. Then a global optimal GMM which could change with TVCs is constructed by the expectation maximization algorithm. Finally, along with damage monitoring process, the variation of GMMs is measured by a probabilistic similarity based damage index which allows normalized detection of structural damage. The utility of the proposed method is validated in the hole-edge crack monitoring of an aluminum tension sample under varying loading condition.
Fault classification based on computer vision for steel wire ropes
Authors: Pan Feiyang , Ren Liyuan , Zhou Jianguo , Liu Zhiliang 
Published: May 2022

Abstract
As an actual steel material application, steel wire rope (SWR) is widely used in various industrial cases. However, in real cases, SWRs are inevitably damaged by erosion, friction, etc. Therefore, it is of great importance to monitor SWRs’ surface conditions in case of accidents. In recent years, computer vision recognition of SWR’s surface has been extensively studied. Based on it, we propose an image-processing method to detect local flaws of SWRs, which extracts the features from the illumination area on the surface of the SWR. Firstly, a three-feature dimension descriptor is constructed by combining the edge grayscale value gradient of the illumination area and morphological features of the illumination area. Then, the KNN (k-nearest neighbour) algorithm is used as a classification method to distinguish the local flaws. According to the experiment results, our proposed method has a high accuracy which is about 94.35%, and low time cost. It can be used in future local flaw detection.
A High Reliability Damage Imaging Method Under Environmental Temperature Variations
Authors: Wu Junpeng , Cai Jian , Yang Jiyuan , Zhang Jian , Zhou Zhiquan 
Published: May 2022

Abstract
In Lamb wave-based structural health monitoring (SHM), the environmental temperature variations can easily affect Lamb wave monitoring signals and seriously reduce the reliability of final damage detection results. To resolve the temperature effect problem, a temperature compensation method of improved baseline signal stretch (IBSS) is presented and applied for high reliability damage imaging under large environmental temperature variations in this paper. After the basic principle of IBSS is analyzed, the realization of IBSS is discussed. Then, a IBSS-based high reliability damage imaging method under temperature variation situation is developed. An experimental study is finally arranged.
Vision-based vibration mode shape extraction for identifying cracks in a cantilever beam
Authors: Deng Rongfeng , Lin Yubin , Li Miaoshuo , Gu Fengshou , Ball Andrew 
Published: May 2022

Abstract
Taking significant merits of high spatial resolution of image sensing, the vibration mode shape of a cantilever beam (CB) is obtained from the free vibration process for local crack identification and structural health monitoring. This vision-based vibration measurement uses a cost-effective rolling shutter camera and a computationally efficient singular value decomposition (SVD). It shows that vibration modes of a continuous structure such as a CB are mainly determined by spatial resolution and less rely on temporal resolution. Moreover, images recorded by the camera maintain the critical information about mode profiles though distortions are introduced into the image. Decomposing the images by SVD allows the vibration modes to be extracted with good degree of accuracy. Both simulation study and experimental verification shows that this novel approach allows different sizes of cracks and locations to be identified.