Wavelet analysis of shock and vibration on the truck bed

Nei, Daisuke; Nakamura, Nobutaka; Roy, Poritosh; Orikasa, Takahiro; Ishikawa, Yutaka; Kitazawa, Hideaki; Shiina, Takeo

doi:10.1002/pts.836

Cited by 20 publications

(21 citation statements)

References 13 publications

(9 reference statements)

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“…In reality, vehicle vibration is nonstationary and non-Gaussian due to variations in vehicle speed and road conditions, among other things. 10,11 For the purposes of this article, the testing of packages using broadband random vibration synthesized from a single-level PDS will be called the established method.…”

Section: Current Packaging Vibration Testingmentioning

confidence: 99%

Correlation Study Using Scuffing Damage to Investigate Improved Simulation Techniques for Packaging Vibration Testing

et al. 2012

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Vibration testing of packaging is a critical part of the distribution packaging analysis process. The accuracy of simulated vibration is important for packaging optimization. Because of this, several researchers have developed improved simulation methods to produce more realistic vibration tests. Correlation studies are required to verify these methods, ideally using actual packaged products in transit. Unfortunately, cost, time and complexity issues make carrying out studies with actual product difficult.This article uses a specially designed and proven test rig, which simulates the damage mechanism of scuffing, to carry out a correlation study. The study compares the level of damage produced when performing simulations using a range of improved techniques in comparison with the time-history reproduction of a journey (used as a benchmark) and the established method using the average power density spectrum to create a Gaussian simulation signal.The level of scuffing damage produced varied between the different simulation methods, with the modulated root mean square (RMS) technique and the accelerated power density spectrum (with a time compression of 5 and a k equal to 2) best reproducing the level of damage observed from the benchmark time replication test.

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Section: Current Packaging Vibration Testingmentioning

confidence: 99%

Correlation Study Using Scuffing Damage to Investigate Improved Simulation Techniques for Packaging Vibration Testing

et al. 2012

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“…Studies have focussed on understanding the characteristics of shocks that occur during road and rail distribution and proposing the development of methods to simulate such characteristics in BRV tests (shock on random testing or real-time high level on time-compressed BRV). [15][16][17][18] These newer developments have demonstrated that conventional BRV tests are not true simulations of the distribution vibration environment and have raised (unanswered) questions over the validity of the single level BRV test. However, the single level test remains the method defi ned by most packaging performance standards.…”

Section: Introductionmentioning

confidence: 97%

On the time compression (test acceleration) of broadband random vibration tests

Shires

2010

Packag Technol Sci

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Many broadband random vibration tests are time compressed. This is done by increasing test intensity according to the Basquin model of cyclic fatigue. Conventionally, the test level is accelerated from the root mean acceleration and an assumed power constant (k = 2) is applied. Using conventional analysis the potential error in test severity can be very large if k is incorrect. The Miner-Palmgren hypothesis of accumulated fatigue is used to re-assess the potential error in test severity accounting for the non-stationarity found in road distribution. This shows a substantially reduced sensitivity to the value of k depending on the distribution of actual vibration intensities around the time-compressed test intensity. Using an example of a leaf-sprung vehicle, the conventional level of time compression is shown to have low sensitivity to errors in k, whereas for an example of an air-ride vehicle a lower level of time compression is needed to reduce error sensitivity.

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“…In order to detect the shocks superimposed in a non-stationary random signal, the predictors come from a range of relevant analysis techniques used to characterize both the shock and the non-stationary vehicle vibration such as moving RMS values, 6,16,21-23 moving crest factor, 7,18-23 the Hilbert-Huang transform (HHT) [10][11][12] and the discrete wavelet transform (DWT). 8,[13][14][15] Moving root mean square. The moving RMS can be used to characterize the non-stationary nature of vehicle vibration signals.…”

Section: Predictorsmentioning

confidence: 99%

“…The non-stationary random component of the road vehicle excitation has been investigated by many researchers. [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] Limited research has been undertaken into the characterization of shocks during road transportation despite being as important as the random component when simulating vehicle vibration. Accurate simulations should include representative distributions of shock occurrences and amplitudes.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Machine Learning to Detect Shocks in Road Vehicle Vibration Signals

2016

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The characterization of transportation hazards is paramount for protective packaging validation. It is used to estimate and simulate the loads and stresses occurring during transport that are essential to optimize packaging and ensure that products will resist the transportation environment with the minimum amount of protective material. Characterizing road transportation vibrations is rather complex because of the nature of the dynamic motion produced by vehicles. For instance, different levels of vibration are induced to freight depending on the vehicle speed and the road surface; which often results in non-stationary random vibration. Road aberrations (such as cracks, potholes and speed bumps) also produce transient vibrations (shocks) that can damage products. Because shocks and random vibrations cannot be analysed with the same statistical tools, the shocks have to be separated from the underlying vibrations. Both of these dynamic loads have to be characterized separately because they have different damaging effects. This task is challenging because both types of vibration are recorded on a vehicle within the same vibration signal.This paper proposes to use machine learning to identify shocks present in acceleration signals measured on road vehicles. In this paper, a machine learning algorithm is trained to identify shocks buried within road vehicle vibration signals. These signals are artificially generated using non-stationary random vibration and shock impulses that reproduce typical vehicle dynamic behaviour. The results show that the machine learning algorithm is considerably more accurate and reliable in identifying shocks than the more common approaches based on the crest factor. TPR, true positive rate; FPR, false positive rate; AUC, area under the ROC curve. USING MACHINE LEARNING TO DETECT SHOCKS IN VIBRATION SIGNAL

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Wavelet analysis of shock and vibration on the truck bed

Cited by 20 publications

References 13 publications

Correlation Study Using Scuffing Damage to Investigate Improved Simulation Techniques for Packaging Vibration Testing

Correlation Study Using Scuffing Damage to Investigate Improved Simulation Techniques for Packaging Vibration Testing

On the time compression (test acceleration) of broadband random vibration tests

On the Use of Machine Learning to Detect Shocks in Road Vehicle Vibration Signals

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