Real time high cycle fatigue estimation algorithm and load history monitoring for vehicles by the use of frequency domain methods

Ugras, Rahmi Can; Alkan, Oguz Kaan; Orhan, Serkan; Kutlu, Mustafa; Muğan, Ata

doi:10.1016/j.ymssp.2018.08.043

Cited by 38 publications

(16 citation statements)

References 15 publications

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“…Strain gauge Accelerometer (4) Hence, the local Hölder exponent α at time v was numerically estimated from the gradient of the graph…”

Section: Bumps Extraction Through Local Regularity Analysismentioning

confidence: 99%

“…Nonetheless, the application of time-domain approaches on complex real-life systems can be computationally challenging. This is because such approaches require a huge data size to represent the real loading conditions [4]. Several efforts had been made by researchers to extract fatigue-related features from the loading signals of mechanical components to accelerate the durability analysis [5,6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Chin¹,

Abdullah²,

Singh³

et al. 2020

IJIE

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This paper aims to study the identification of bumps in vibrational signals and develop bump-energybased durability predictive models for a suspension coil spring. The bump energy of the loading signal is affected by high frequency noises and can lead to inaccurate results. Therefore, it is necessary to eliminate high frequency noise during bump identification. Local regularity analysis was employed to determine the singular points in road signals. Bump signals were then reconstructed from these singular points. Subsequently, bump-energy-based models were developed by correlating with the fatigue lives estimated using the Coffin-Manson, Morrow and Smith-Watson-Topper strainlife models. The results show that the bump signals extracted from the road excitations had a frequency band within 0-50 Hz, indicating that the high frequency noises had been successfully removed during extraction of the bumps. The bump-energy-based models predicted a fatigue life ranging from 3.98x10 4 to 4x10 9 cycles within a 95% confidence interval, where the Coffin-Manson-based model showed the highest fatigue life. This is because the Coffin-Manson model did not consider the mean stress effects. When compared with the experimental results, the Coffin-Mansonbased model indicates the highest accuracy, given its highest R 2 of 0.948. The bump-energy-based models developed in this study contributed an accurate durability prediction of coil springs.

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“…Strain gauge Accelerometer (4) Hence, the local Hölder exponent α at time v was numerically estimated from the gradient of the graph…”

Section: Bumps Extraction Through Local Regularity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Chin¹,

Abdullah²,

Singh³

et al. 2020

IJIE

View full text Add to dashboard Cite

show abstract

“…In recent years, signal processing techniques have been shown to predict fatigue failure in mechanical systems. Ugras et al, 6 for example, used the frequency domain method for real‐time fatigue estimation. Su et al 7 characterized fatigue damage by acousto‐ultrasonic waves.…”

Section: Introductionmentioning

confidence: 99%

Application of thermoelectricity in fatigue of metals

Hajshirmohammadi

Khonsari

2021

Fatigue Fract Eng Mat Struct

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An online approach is proposed to predict the fatigue life of metallic specimens under cyclic loading using the concept of thermoelectricity. The working principle of the methodology is described, and experimental results are performed with a fully reversed bending fatigue testing apparatus to evaluate the efficacy of the approach. Results are presented that correlate the measured thermoelastic potential for low‐carbon steel 1018 specimens measured via an infrared camera. It is shown that the application of thermoelectricity provides a practical methodology for early prediction of fatigue.

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“…In recent years, agricultural mechanization in china has been improved to a new higher level, followed by an increase in the application of agricultural vehicles [1]. Vehicle vibration will be caused by soil excitation during its operation, which will cause damage of parts, reduce the service life of vehicles [2], and also cause damage to the driverʼs body [3]. Meanwhile, it will aggravate soil compaction, affect the growth of crop roots [4], and ultimately reduce the yield [5].…”

Section: Introductionmentioning

confidence: 99%

Perceiving Excitation Characteristics from Interactions between Field Road and Vehicle via Vibration Sensing

Cheng

Man

et al. 2021

Journal of Advanced Transportation

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When agricultural vehicles operate in the field, the soft road excitation makes it difficult to measure the vehicle vibration. A camera-accelerator system can solve this issue by utilizing computer vision information; however, the relationship between the field road surface and the vehicle vibration response remains an unsolved problem. This study aims to investigate the correlation of the soft road excitation of different long-wave surfaces with the vehicle vibration response. Vibration equation between the vehicle and soft road surface system was established to produce an effective roughness model of the field soft road surface. In order to simulate the vehicle vibration state under different long-wave road surfaces, the soil rectangular pits with 21 kinds of different spans and depths were applied to the road surfaces, and a tractor vibration test system was built for vibration test. The frequency spectrum analysis was performed for the vibration response and the roughness signals of the road surfaces. The results showed that coefficient (R2) of frequency correlation between the roughness excitation and the original unevenness at the excitation point at the rear end of the rectangular soil pit fell within 0.9641∼0.9969. The main frequency band of the vibration response fell within 0∼3 Hz, and the phenomenon of quadruple frequency existed. The correlation of roughness excitation with quadruple frequency fell within 0.992165∼1. The primary excitation points were located at the rear end of the rectangular soil pit. In addition, it also indicated that when the vehicle was driven without autonomous power, the vehicle vibration frequency mainly depended on the excitation frequency of the field road surface and the frequency at the maximum vehicle vibration intensity was 2 or 3 times of that at the maximum field soft road excitation. These findings may provide a reference for optimal design of vibration reduction and control for agricultural vehicles.

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Real time high cycle fatigue estimation algorithm and load history monitoring for vehicles by the use of frequency domain methods

Cited by 38 publications

References 15 publications

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Bump Energy for Durability Prediction of Coil Spring Based on Local Regularity Analysis

Application of thermoelectricity in fatigue of metals

Perceiving Excitation Characteristics from Interactions between Field Road and Vehicle via Vibration Sensing

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