A New Time-frequency Domain Simulation Method for Damage Accumulation and life prediction of Composite Thin-wall Structures under Random Cyclic Loadings

2021 ◽  
pp. 114999
Author(s):  
Zengwen Wu ◽  
Zhenya Zhang ◽  
Jianguo Wu ◽  
Jun Liang ◽  
Jingran Ge ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Life Prediction ◽  
Damage Accumulation ◽  
Simulation Method ◽  
Thin Wall ◽  
Time Frequency ◽  
Wall Structures ◽  
New Time

scholarly journals Durability Analysis for Coil Spring Suspension Based on Strain Signal Characterisation

2018 ◽  
Vol 7 (3.17) ◽  
pp. 104
Author(s):  
Chin Chuin Hao ◽  
Shahrum Abdullah ◽  
Ahmad Kamal Ariffin ◽  
Salvinder Singh Karam Singh
Keyword(s):  
Fourier Transform ◽  
Fatigue Life ◽  
Frequency Domain ◽  
Time Domain ◽  
Life Prediction ◽  
Energy Content ◽  
Fatigue Life Prediction ◽  
Coil Spring ◽  
Time Frequency ◽  
Strain Signal

This paper aims to predict the durability of an automobile coil spring by characterising the captured strain data. The load histories collected at coil spring are often presented in time domain but time domain cannot provide sufficient information for fatigue life prediction. The objective of this study was to characterise the strain signal in time domain, frequency domain and time-frequency domain for fatigue life prediction. The signal obtained in time domain was used to predict the fatigue life of the coil spring through Rainflow cycle counting technique and models of strain-life relationships. In frequency domain, fast Fourier transform revealed that the frequency components in the strain signal ranged between 0-5 Hz. The frequencies can be further categorised into two ranges: 0-0.3 Hz and 1-2 Hz. Power spectral density confirmed that the frequencies with high energy content were 0-5 Hz and the total energy content in the signal is 4.0872x103 µɛ2. Short time Fourier transform can identify the local time and frequency properties of the signal but it has a limitation in time-frequency resolutions. Wavelet transform can provide a better time-frequency resolutions and it confirmed that the transients in the time domain had frequency range of 1-2 Hz. In summary, this study revealed different possible approaches of signal processing in fatigue life assessment of automotive components as guidance for the selection of suitable approach based on the type of information needed for the analysis.  

Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

2015 ◽  
Vol 231 (9) ◽  
pp. 1699-1711 ◽  
Author(s):  
Fang Liu ◽  
Yongbin Liu ◽  
Fenglin Chen ◽  
Bing He
Keyword(s):  
Frequency Domain ◽  
Extreme Learning Machine ◽  
Life Prediction ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Time Frequency ◽  
Fast Learning ◽  
Useful Life ◽  
Learning Machine ◽  
Degradation Indicator

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

Optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm

2016 ◽  
Vol 58 (1) ◽  
pp. 75-78 ◽  
Author(s):  
Ali Rıza Yıldız ◽  
Enes Kurtuluş ◽  
Emre Demirci ◽  
Betul Sultan Yıldız ◽  
Selçuk Karagöz
Keyword(s):  
Thin Wall ◽  
Wall Structures ◽  
Gravitational Search ◽  
Nelder Mead Algorithm
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