Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Yao, Houpu; Wen, Jingjing; Ren, Yi; Wu, Bin; Ji, Ze

doi:10.1109/icassp.2019.8682484

Cited by 5 publications

(12 citation statements)

References 17 publications

(22 reference statements)

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“…The inspiration of adopting deep learning to correct faulty shock signals comes from the aphorism of "diligence redeems stupidity" that humans or even biological entities can improve their skills through continuous training and effort. Moreover, recent research in applying deep learning to process time-series signals [15], [24] also proves the concept in related domains, and paves a new avenue towards the goal of this paper. Similarly, the DNN is trained with the collected shock signal datasets.…”

Section: The Proposed Dnn 1) Overviewmentioning

confidence: 61%

A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

Wen

Yao

et al. 2020

IEEE Sensors J.

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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 network is designed and trained on this dataset, aiming to map the faulty signals to their reference counterparts. Experimental results show that, with the help of deep learning, shock signals can be accurately recovered from the faulty measurements. Compared with conventional approaches that require diagnosing and replacing faulty parts, the proposed data-driven method demonstrates a highly promising solution that allows recovering corrupted signals without introducing extra work to upgrade the hardware at almost zero cost. The dataset and code of this work are made publicly available on GitHub at https://github.com/hope-yao/Sensor_Calibration.

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Section: The Proposed Dnn 1) Overviewmentioning

confidence: 61%

A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

Wen

Yao

et al. 2020

IEEE Sensors J.

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“…These damaged accelerometers cannot measure accurate shock signals, which would lead to measuring uncertainties of various unpredictable levels, and, hence, test failures. Additionally, using uncalibrated accelerometers, but without awareness beforehand, would result in the test failure as well [8]. There are mainly two categories of accelerometer faults: the damage of the package shell and the damage of the inner components [9].…”

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%

On Fault Diagnosis for High-G Accelerometers via Data-Driven Models

Wen

Yao²,

et al. 2021

IEEE Sensors J.

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Shock test is a pivotal stage for designing and manufacturing space instruments. As the essential components in shock test systems to measure shock signals accurately, high-g accelerometers are usually exposed to hazardous shock environment and could be subjected to various damages. Owing to that these damages to the accelerometers could result in erroneous measurements which would further lead to shock test failures, accurately diagnosing the fault type of each high-g accelerometer can be vital to ensure the reliability of the shock test experiments. Additionally, in practice, an accelerometer in one malfunction form usually outputs mutable signal waveforms, so that it is difficult to empirically judge the fault type of the accelerometer based on the erroneous readings. Moreover, traditional hardware diagnosis approaches require disassembling the sensor's package shell and manually observing the damage of the elements inner the sensor, which are less efficient and uneconomical. Aiming at these problems, several data-driven approaches are incorporated to diagnose the fault types of high-g accelerometers in this work. Firstly, several high-g accelerometers with most frequent types of damage are collected, and a shock signal dataset is gathered by conducting shock tests on these faulty accelerometers. Then, the obtained dataset is used to train several base classifiers to identify the fault types in a supervised fashion. Lastly, a hybrid ensemble learning model is established by integrating these base classifiers with both heterogeneous and homogeneous models. Experimental results show that these data-driven methods can accurately identify the fault types of high-g accelerometers from their mutable erroneous readings. Index Terms-shock test, high-g accelerometer, fault diagnosis, data-driven methods, ensemble learning.

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“…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. Another representative example is that an uncalibrated accelerometer is used to measure and collect massive shock data, but without awareness of the missing calibration beforehand [9]. Also, it is considerably difficult to repeat these shock tests because of the huge resource waste.…”

Section: Introductionmentioning

confidence: 99%

Self-validating high-g accelerometers through data-driven methods

Wen¹,

Yao²,

Ji³

et al. 2021

Sensors and Actuators A: Physical

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Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration

Cited by 5 publications

References 17 publications

A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

A Deep Learning Approach to Recover High-g Shock Signals From the Faulty Accelerometer

On Fault Diagnosis for High-G Accelerometers via Data-Driven Models

Self-validating high-g accelerometers through data-driven methods

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