A novel Lanczos quaternion singular spectrum analysis method and its application to bevel gear fault diagnosis with multi-channel signals

2022 ◽  
Vol 168 ◽  
pp. 108679
Author(s):  
Yanli Ma ◽  
Junsheng Cheng ◽  
Ping Wang ◽  
Jian Wang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Bevel Gear ◽  
Analysis Method ◽  
Singular Spectrum ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Domain Adaptive Sparse Transformer for Aeroengine Bevel Gear Fault Diagnosis

2021 ◽  
Author(s):  
Yasong Li ◽  
Zheng Zhou ◽  
Chuang Sun ◽  
Ruqiang Yan ◽  
Xuefeng Chen
Keyword(s):  
Fault Diagnosis ◽  
Bevel Gear ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Denoising Fused Wavelets Net for Aeroengine Bevel Gear Fault Diagnosis

2021 ◽  
Author(s):  
Zuogang Shang ◽  
Zhibin Zhao ◽  
Zheng Zhou ◽  
Chuang Sun ◽  
Yu Sun ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bevel Gear ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Roller element bearing fault diagnosis using singular spectrum analysis

2013 ◽  
Vol 35 (1-2) ◽  
pp. 150-166 ◽  
Author(s):  
Bubathi Muruganatham ◽  
M.A. Sanjith ◽  
B. Krishnakumar ◽  
S.A.V. Satya Murty
Keyword(s):  
Fault Diagnosis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Singular Spectrum ◽  
Element Bearing

Gear Fault Diagnosis Based on Time Synchronous Average and Envelope Analysis

2013 ◽  
Vol 303-306 ◽  
pp. 502-505 ◽  
Author(s):  
Yun Li ◽  
Yan Gao ◽  
Jun Guo ◽  
Xian Jun Yu ◽  
Yan Xue Liu
Keyword(s):  
Fault Diagnosis ◽  
Simulation Study ◽  
Spectrum Analysis ◽  
Rotation Frequency ◽  
High Accuracy ◽  
Envelope Analysis ◽  
Gear Mesh ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Envelope Spectrum

This paper proposed a new method of gear fault diagnosis in gearbox. Mainly it stresses on the combination of time synchronous average (TSA) and envelope analysis, using TSA technique to eliminate the random noises in the gearbox. Then, the clear rotation frequency, gear mesh frequency and their harmonics can be obtained through the envelope spectrum analysis. Simulation study indicates that this method can effectively detect the gear faults with a high accuracy.

scholarly journals Window Length Selection of Singular Spectrum Analysis and Application to Precipitation Time Series

2017 ◽  
Vol 19 (2) ◽  
pp. 306-317 ◽  
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Precipitation Data ◽  
Window Length ◽  
Analysis Method ◽  
Precipitation Time Series ◽  
Singular Spectrum ◽  
Periodogram Analysis ◽  
Selection Of

Window length is a very critical tuning parameter in Singular Spectrum Analysis (SSA) technique. For finding the optimal value of window length in SSA application, Periodogram analysis method with SSA for referencing on the selection of window length and confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA. Several potential periods of Florida precipitation data are firstly obtained using periodogram analysis method. The SSA technique is applied to precipitation data with different window length as the period and experiential recommendation to extract the precipitation time series, which determines the leading components for reconstructing the precipitation and forecast respectively. A regressive model linear recurrent formula (LRF) model is used to discover physically evolution with the SSA modes of precipitation variability. Precipitation forecasts are deduced from SSA patterns and compared with observed precipitation. Comparison of forecasting results with observed precipitation indicates that the forecasts with window length of L=60 have the better performance among all. Our findings successfully confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA and presents a detailed physical explanation on the varying conditions of precipitation variables.

scholarly journals Fault diagnosis of rolling bearings using singular spectrum analysis and artificial neural networks

2021 ◽  
Vol 43 (2) ◽  
pp. 183-196
Author(s):  
Quang Thinh Tran ◽  
Kieu Nhi Ngo ◽  
Sy Dzung Nguyen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Frequency Noise ◽  
Singular Spectrum ◽  
Experimental Apparatus ◽  
Artificial Neural ◽  
Set Up

Singular spectrum analysis (SSA) has been employed effectively for analyzing in the time-frequency domain of time series. It can collaborate with data-driven models (DDMs) such as Artificial Neural Networks (ANN) to set up a powerful tool for mechanical fault diagnosis (MFD). However, to take advantage of SSA more effectively for MFD, quantifying the optimal component threshold in SSA should be addressed. Also, to exploit the managed mechanical system adaptively, the variation tendency of its physical parameters needs to be caught online. Here, we present a bearing fault diagnosis method (BFDM) based on ANN and SSA that targets these aspects. First, a multi-feature is built from pure mechanical properties distilled from the vibration signal of the system. Relied on SSA, the measured acceleration signal is analyzed to cancel the high-frequency noise. The remaining components take part in building a multi-feature to establish a database for training the ANN. Optimizing the number of the kept components is then carried out to obtain a dataset called Tr_Da. Based on Tr_Da, we receive the optimal ANN (OANN). In the next period, at each checking time, another database called Test_Da is set up online following the same way of building the Tr_Da. The compared result between the encoded output and the output of the OANN corresponding to the input to be Test_Da provides the bearing(s) health information. An experimental apparatus is built to evaluate the BFDM. The obtained results reflect the positive effects of the method.

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