Evaluation of aspects affecting measurement of three-axis accelerometers

D’Emilia, Giulio; Gaspari, Antonella; Natale, Emanuela

doi:10.1016/j.measurement.2015.08.031

Cited by 19 publications

(8 citation statements)

References 12 publications

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“…Method 3 (D'Emilia et al, 2015(D'Emilia et al, , 2016a is based on the following model, describing the relation between the input accelerations and the output signals as in Eq. 7:…”

Section: Methodsmentioning

confidence: 99%

“…where V = (V i ) is the output array, A = (a i ) is the reference acceleration array, S = (S ij ) is the sensitivity matrix and Q = (q i ) represents the offset array. Then, a total of 12 parameters (main sensitivities, transverse sensitivities and offsets) are evaluated; a linear leastsquares optimization (LSO) is used to estimate them (D'Emilia et al, 2015(D'Emilia et al, , 2016a. Then, this method simultaneously estimates not only the main sensitivities, but also the transverse ones and the offsets.…”

Section: Methodsmentioning

confidence: 99%

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Calibration of tri-axial MEMS accelerometers in the low-frequency range – Part 2: Uncertainty assessment

D’Emilia

Gaspari

Mazzoleni

et al. 2018

J. Sens. Sens. Syst.

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Abstract. A comparison among three methods for the calibration of tri-axial accelerometers, in particular MEMS, is presented in this paper, paying attention to the uncertainty assessment of each method. The first method is performed according to the ISO 16063 standards. Two innovative methods are analysed, both suitable for in-field application. The effects on the whole uncertainty of the following aspects have been evaluated: the test bench performances in realizing the reference motion, the vibration reference sensor, the geometrical parameters and the data processing techniques. The uncertainty contributions due to the offset and the transverse sensitivity are also studied, by calibrating two different types of accelerometers, a piezoelectric one and a capacitive one, to check their effect on the accuracy of the methods under comparison. The reproducibility of methods is demonstrated. Relative uncertainty of methods ranges from 3 to 5 %, depending on the complexity of the model and of the requested operations. The results appear promising for low-cost calibration of new tri-axial accelerometers of MEMS type.

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“…Method 3 (D'Emilia et al, 2015(D'Emilia et al, , 2016a is based on the following model, describing the relation between the input accelerations and the output signals as in Eq. 7:…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Calibration of tri-axial MEMS accelerometers in the low-frequency range – Part 2: Uncertainty assessment

D’Emilia

Gaspari

Mazzoleni

et al. 2018

J. Sens. Sens. Syst.

View full text Add to dashboard Cite

show abstract

“…This range of frequencies has been chosen to respect the Nyquist condition, as the maximum sampling frequency in this prototype is limited by the USART transmission time of 9 ms. However, the selected range is compatible with other studies in the field [37]- [39]. A high accuracy waveform generator has imposed the sinusoidal motion, with a fixed peak-to-peak displacement amplitude (0.5 mm) for all frequencies.…”

Section: Shock Detectionmentioning

confidence: 98%

Validation of a low-cost wireless sensors node for museum environmental monitoring

et al. 2017

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Wireless Sensor Network (WSN) has been adopted in many contexts, such as healthcare or industry. In museum scenarios, WSN has been introduced for environmental monitoring, to control temperature and relative humidity. Nowadays, the development of low-cost micro-scale sensing units, opened to new possibilities for WSN development, enlarging the set of feasible measurements to gaseous pollutant concentration, relative humidity, temperature, light intensity, air flow, or vibration.This work is part of the overarching goal of developing a low-cost and minimally invasive WSN designed for a museum scenario. The proposed WSN node, based on a ZigBee standard, gathers signals provided by: a 9-axis MIMU, a sensor for temperature and relative humidity, and three gas detection boards. In this paper, specifically, we present performances of the WSN node in detecting: structure tilt, vibrations, and daily cycle of humidity, temperature, and gas deposition.

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“…Scheffer et al reported a wear monitoring system in a turning operation using vibration signature and strain elements [6]. Vibration and acoustic emission (AE) signals are the most widely used approaches in order to monitor the condition of rotating machines [7]. The faults can be detected by comparing the signals of a machine running in normal and faulty conditions.…”

Section: Introductionmentioning

confidence: 99%

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

Shantisagar¹,

Jegadeeshwaran²,

Gandhi³

et al. 2019

Structural Durability &Amp; Health Monitoring

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The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

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Evaluation of aspects affecting measurement of three-axis accelerometers

Cited by 19 publications

References 12 publications

Calibration of tri-axial MEMS accelerometers in the low-frequency range – Part 2: Uncertainty assessment

Calibration of tri-axial MEMS accelerometers in the low-frequency range – Part 2: Uncertainty assessment

Validation of a low-cost wireless sensors node for museum environmental monitoring

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

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