2020
DOI: 10.1109/jsen.2019.2949241
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A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

Abstract: A deep learning based approach is proposed to accurately recover shock signals measured from a damaged high-g accelerometer without modifying the hardware. We first conducted shock tests and collected a large dataset of shock signals with different levels of acceleration by using an efficient experimental apparatus. The training data is composed of a pair of signals simultaneously obtained from a faulty accelerometer and a high-end accelerometer (served as the ground truth). A customized autoencoder neural net… Show more

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Cited by 8 publications
(10 citation statements)
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“…Shock environments are described by acceleration-time signals generally, which can be measured by accelerometers [6]. However, high-g accelerometers sometimes can get damaged during the shock tests due to the severe impact environment [7]. These damaged accelerometers cannot measure accurate shock signals, which would lead to measuring uncertainties of various unpredictable levels, and, hence, test failures.…”
Section: Introductionmentioning
confidence: 99%
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“…Shock environments are described by acceleration-time signals generally, which can be measured by accelerometers [6]. However, high-g accelerometers sometimes can get damaged during the shock tests due to the severe impact environment [7]. These damaged accelerometers cannot measure accurate shock signals, which would lead to measuring uncertainties of various unpredictable levels, and, hence, test failures.…”
Section: Introductionmentioning
confidence: 99%
“…The major damage types of accelerometer's inner components include cantilever fractures, wire bond shearing, solder joint loss, chip cracks, [10], [11] etc. Correspondingly, inner component damages will cause the waveform variation of the accelerometer's outputs, such as the peak truncation [7], noise pollution [8], and baseline drift [12]. Therefore, it would be of great value to be able to automatically diagnose the accelerometer's fault type through its readings.…”
Section: Introductionmentioning
confidence: 99%
“…Under many circumstances, however, the acquired shock data is unique, and repeating the pyroshock test can be extremely difficult or costly [7]. A typical example is that when the to-be-tested electronic device and the accelerometer are both damaged at the same time during the high-g shock test [8]. Unfortunately, it is quite costly to repeat this destructive test, and, meanwhile, the faulty accelerometer cannot provide accurate measurements to help analyzing the damage causes of the defective electronic device.…”
Section: Introductionmentioning
confidence: 99%
“…These successes also inspired us to apply this powerful tool into the self-validation of high-g accelerometers. In our previous work, two deep neural networks (DNNs) were developed to recover shock signals of two forms of fault respectively [8,9]. In addition, we also proposed an ensemble learning-based model to identify the fault types of high-g accelerometers by integrating several data-driven-based sub-classifiers [27].…”
Section: Introductionmentioning
confidence: 99%
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