scholarly journals Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 557
Author(s):  
Shuiguang Tong ◽  
Junjie Li ◽  
Feiyun Cong ◽  
Zilong Fu ◽  
Zheming Tong

Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately.

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.


2009 ◽  
Vol 419-420 ◽  
pp. 149-152 ◽  
Author(s):  
Li Dong Jiang ◽  
Shan Chang ◽  
Guang Hao Dai ◽  
Zhen Rong Zhu

The abnormal noise was found in a marine planetary gearbox during the experiment. Then, the load test of the gearbox was done on a gearbox test rig and the vibration signal was measured and collected. The fault of the gearbox was analyzed by the time domain and frequency domain analysis. The trouble part was diagnosed and treated. The method used in this paper combined the theory analysis with engineering application. Simultaneously, it has provided a properly feasible method and valuable reference for the fault diagnosis of planetary gearbox.


2021 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Chao Jiang ◽  
Lin Liu ◽  
Xiaoxing Qin ◽  
Suhong Zhou ◽  
Kai Liu

The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.


Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2638
Author(s):  
Xianhua Chen ◽  
Xingkai Yang ◽  
Ming J. Zuo ◽  
Zhigang Tian

Planetary gearbox systems are critical mechanical components in heavy machinery such as wind turbines. They may suffer from various failure modes, due to the harsh working environment. Dynamic modeling is a useful method to support early fault detection for enhancing reliability and reducing maintenance costs. However, reported studies have not considered the sun gear tooth crack and bearing clearance simultaneously to analyze their combined effect on vibration characteristics of planetary gearboxes. In this paper, a dynamic model is developed for planetary gearboxes considering the clearance of planet gear, sun gear, and carrier bearings, as well as sun gear tooth crack levels. Bearing forces are calculated considering bearing clearance, and the dynamic model equations are updated accordingly. The results reveal that the combination of bearing clearances can affect the vibration response with sun gear tooth crack by increasing the kurtosis. It is found that the effect of planet gear bearing clearance is very small, while the sun gear and carrier bearing clearance has clear impact on the vibration responses. These findings suggest that the incorporation of bearing clearance is important for planetary gearbox dynamic modeling.


Author(s):  
Ali Abolfathi ◽  
Dan J O’Boy ◽  
Stephen J Walsh ◽  
Amy M Dowsett ◽  
Stephen A Fisher

A large number of plastic clips are used in an automotive vehicle to connect the trim to the structure. These are small clips with very small masses compared to the structural elements that they connect together; however, the uncertainty in their properties can affect the dynamic response. The uncertainty arises out of their material and manufacturing tolerances and more importantly the boundary conditions. A test rig has been developed that can model the mounting condition of the clips. This allows measurement of the range of their effective stiffness and damping. Initially, the boundary condition at the structure side is replicated. The variability is found to be 7% for stiffness and 8% for damping. In order to simulate the connection of the trim side, a mount is built using a 3D printer. The variability due to the boundary condition on both sides was as large as 40% for stiffness and 36% for damping. A Monte Carlo simulation is used in order to assess the effect of the uncertainty of the clips’ properties on the vibration transfer functions of a door assembly. A simplified connection model is used in this study where only the axial degree of freedom is considered in connecting the trim to the door structure. The uncertainty in the clip stiffness and damping results in a variability in the vibration transfer function which is frequency dependent and can be as high as 10% at the resonant peaks with higher values at some other frequencies. It is shown that the effect of the uncertainty in the clips effective damping is negligible and the variability in the dynamic response is mainly due to the uncertainty in the clip’s stiffness. Furthermore, it is shown that the variability would reduce either by increasing or decreasing the effective stiffness of the clips.


Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.


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