scholarly journals MEMS Acoustic Emission Sensors

2020 ◽  
Vol 10 (24) ◽  
pp. 8966
Author(s):  
Didem Ozevin
Keyword(s):  
Acoustic Emission ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Frequency Bandwidth ◽  
Micro Electro Mechanical Systems ◽  
Design Variables ◽  
Transduction Mechanisms ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Constitutive Properties

This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook.

Investigation of Degradation Signature for Hard Disk Drives Using Vibration and Acoustic Emission Sensors

2009 ◽  
Author(s):  
Ibrahim Zeid ◽  
Sagar Kamarthi ◽  
Yogesh Bagul
Keyword(s):  
Acoustic Emission ◽  
Computer System ◽  
Signal To Noise Ratio ◽  
Hard Disk Drives ◽  
Hard Disk ◽  
Disk Drive ◽  
Remaining Useful Life ◽  
Acoustic Emission Sensors ◽  
Sensor Signals ◽  
Useful Life

The hard disk drive (HDD) is a critical component of any computer system. The performance of a computer system largely depends on the performance and health of its HDD. This paper investigates degradation signatures for the estimation of remaining useful life and the assessment of health of a HDD. Most of the mechanical faults in a HDD results in head-disk collision or friction. As a HDD ages, it may experience gradual damage to the head and scratches on the disk. One can expect that changes in the condition of head and disk may result in comparable changes in the characteristics of HDD vibration and acoustic emission signals. Based on this premise, this research conducted experiments on HDDs subject to accelerated deterioration. HDDs are monitored through vibration and acoustic emission sensors. Extracting features from these sensor signals, HDD degradation signatures are created. The results indicate that though degradation signatures exhibit a gradual trend with HDD aging, accurate assessment remaining useful life and health are not possible using these degradation signatures. Poor signal to noise ratio is the main impediment in this approach. The conclusion is that the best vibration and acoustic sensors available for this application are neither sensitive nor selective enough to capture the changes in the head and the disk of an aging HDD.

AE Fault Diagnosis of Rolling Bearing Based on Base

2011 ◽  
Vol 143-144 ◽  
pp. 664-668
Author(s):  
D.L. Yang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Dimensionality Reduction ◽  
Rolling Bearing ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Acoustic Emission Test ◽  
Fault Source ◽  
Original Feature

In the acoustic emission fault diagnosis, the acoustic emission sensors was installed on the bearing pedestal where near from the fault source so that can collected stronger fault AE signal, however ,sometimes, it is inconvenience for AE sensor installation. This paper proposed that install the AE sensor on the base for collect the fault AE signal, but the signal was weak, so carried on EMD first, and selected the former 8 IMF to construct the original feature, than carried on KPCA for dimensionality reduction and get the optimized feature. In this paper, taking bearing acoustic emission test for example, by compared the base fault feature with the bearing pedestal fault feature, verified that the method that the AE sensor install on the base is feasible.

Acoustic Emission Sensors using Silicon Mechanical Resonators

2015 ◽  
Vol 135 (12) ◽  
pp. 484-489
Author(s):  
Kengo Takata ◽  
Takashi Sasaki ◽  
Mitsutomo Nishizawa ◽  
Hiroshi Saito ◽  
Shinsuke Yamazaki ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Mechanical Resonators ◽  
Acoustic Emission Sensors

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

scholarly journals The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Dispersive noise removal in t-x space: Application to Arctic data

Geophysics ◽  
1988 ◽  
Vol 53 (3) ◽  
pp. 346-358 ◽  
Author(s):  
Greg Beresford‐Smith ◽  
Rolf N. Rango
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Analysis ◽  
Noise Removal ◽  
North Slope ◽  
Frequency Bandwidth ◽  
The North ◽  
North Slope Of Alaska ◽  
Seismic Records ◽  
Time Offset ◽  
Undersampled Data

Strongly dispersive noise from surface waves can be attenuated on seismic records by Flexfil, a new prestack process which uses wavelet spreading rather than velocity as the criterion for noise discrimination. The process comprises three steps: trace‐by‐trace compression to collapse the noise to a narrow fan in time‐offset (t-x) space; muting of the noise in this narrow fan; and inverse compression to recompress the reflection signals. The process will work on spatially undersampled data. The compression is accomplished by a frequency‐domain, linear operator which is independent of trace offset. This operator is the basis of a robust method of dispersion estimation. A flexural ice wave occurs on data recorded on floating ice in the near offshore of the North Slope of Alaska. It is both highly dispersed and of broad frequency bandwidth. Application of Flexfil to these data can increase the signal‐to‐noise ratio up to 20 dB. A noise analysis obtained from a microspread record is ideal to use for dispersion estimation. Production seismic records can also be used for dispersion estimation, with less accurate results. The method applied to field data examples from Alaska demonstrates significant improvement in data quality, especially in the shallow section.

Acoustic Emission Analysis of Nanoindentation-Induced Fracture Events

2004 ◽  
Vol 841 ◽  
Author(s):  
Pawel Dyjak ◽  
Raman P. Singh
Keyword(s):  
Acoustic Emission ◽  
Brittle Materials ◽  
Signal Amplitude ◽  
Crack Arrest ◽  
Quantitative Information ◽  
Local Failure ◽  
Emission Analysis ◽  
Specimen Holder ◽  
Cube Corner ◽  
Ae Signal

ABSTRACTMonitoring of acoustic emission (AE) activity was employed to characterize the initiation and progression of local failure processes during nanoindentation-induced fracture. Specimens of various brittle materials were loaded with a cube-corner indenter and AE activity was monitored during the entire loading and unloading event using an AE transducer mounted inside the specimen holder. As observed from the nanoindentation and AE response, there were fundamental differences in the fracture behavior of the various materials. Post-failure observations were used to identify particular features in the AE signal (amplitude, frequency, rise-time) that correspond to specific types of fracture events. Furthermore, analysis of the parametric and transient AE data was used to establish the crack-initiation threshold, crack-arrest threshold, and energy dissipation during failure. It was demonstrated that the monitoring of AE signals yields both qualitative and quantitative information regarding highly local failure events in brittle materials.

Study on Acoustic Emission of 1-3 Orthotropic Cement Based Piezoelectric Sensors

2012 ◽  
Vol 487 ◽  
pp. 471-475 ◽  
Author(s):  
Shi Hui Xie ◽  
Mi Mi Li ◽  
Mei Juan Zhou ◽  
Min Sun ◽  
Shi Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Volume Fraction ◽  
Signal Strength ◽  
Signal Wave ◽  
Functional Material ◽  
Wave Shape ◽  
Sensing Element ◽  
Piezoelectric Composites ◽  
Ae Signal ◽  
Amplitude Decrease

1-3 orthotropic cement based piezoelectric composites were fabricated by cut-filling and arrange-filling technique, using PZT-51 ceramic as functional material and cement as passive matrix. 1-3 orthotropic cement based piezoelectric composites were prepared into Acoustic Emission (AE) sensors, the attenuation of AE signal on the concrete and the response of different sensors on the concrete with increasing distance were researched. The results showed that the signal strength received by sensing element increases with the increasing PZT volume fraction; signal peaks and amplitude decrease gradually when the testing distance increases; signal strength received on the ceramic title is stronger than on the concrete; the attenuation of signal wave shape received on the concrete is much slower when compared with ceramic title.

scholarly journals Application of Fiber Bragg Grating Acoustic Emission Sensors in Thin Polymer-Bonded Explosives

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3778 ◽  
Author(s):  
Tao Fu ◽  
Peng Wei ◽  
Xiaole Han ◽  
Qingbo Liu
Keyword(s):  
Acoustic Emission ◽  
Fiber Bragg Grating ◽  
Bragg Grating ◽  
Location Method ◽  
Thin Polymer ◽  
Time Coefficient ◽  
Polymer Bonded Explosives ◽  
Acoustic Emission Sensors

Fiber Bragg grating (FBG) acoustic emission (AE) sensors have been used in many applications. In this paper, based on an FBG AE sensor, the sensing principle of the interaction between the AE wave and the sensor is introduced. Then, the directionality of the FBG AE sensor on the surface of a thin polymer-bonded explosive (PBX) material is studied. Finally, the time coefficient location method is proposed to correct the AE time detected by the FBG AE sensor, thereby improving the accuracy of location experiments.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

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