Thick-film acoustic emission sensors for use in structurally integrated condition- monitoring applications

2011 ◽  
Vol 58 (9) ◽  
pp. 1994-2000 ◽  
Author(s):  
A. J. Pickwell ◽  
R. A. Dorey ◽  
D. Mba
Keyword(s):  
Acoustic Emission ◽  
Thick Film ◽  
Condition Monitoring ◽  
Monitoring Applications ◽  
Acoustic Emission Sensors

scholarly journals Structural Health Monitoring Using Fibre Optic Acoustic Emission Sensors

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6369
Author(s):  
James Owen Willberry ◽  
Mayorkinos Papaelias
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Electromagnetic Interference ◽  
Damage Evolution ◽  
High Refractive Index ◽  
Industrial Applications ◽  
Fibre Optic ◽  
Wide Range ◽  
Recent Developments ◽  
Acoustic Emission Sensors

Acoustic emission (AE) is widely used for condition monitoring of critical components and structures. Conventional AE techniques employ wideband or resonant piezoelectric sensors to detect elastic stress waves propagating through various types of structural materials, including composites during damage evolution. Recent developments in fibre optic acoustic emission sensors (FOAES) have enabled new ways of detecting and monitoring damage evolution using AE. An optical fibre consists of a core with a high refractive index and a surrounding cladding. The buffer layer and outer jacket both act as protective polymer layers. Glass optical fibres can be used for manufacturing AE sensors of sufficiently small size to enable their embedding into fibre-reinforced polymer composite materials. The embedding process protects the FOAES against environmental stresses prolonging operational lifetime. The immunity of FOAES to electromagnetic interference makes this type of sensor attractive for condition monitoring purposes across a wide range of challenging operational environments. This paper provides an exhaustive review of recent developments on FOAES including their fundamental operational principles and key industrial applications.

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

An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission

2021 ◽  
Vol 113 (1-2) ◽  
pp. 585-603
Author(s):  
Wenderson N. Lopes ◽  
Pedro O. C. Junior ◽  
Paulo R. Aguiar ◽  
Felipe A. Alexandre ◽  
Fábio R. L. Dotto ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Grinding Wheel ◽  
Short Time Fourier Transform ◽  
Short Time

scholarly journals Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1054
Author(s):  
Guo Bi ◽  
Shan Liu ◽  
Shibo Su ◽  
Zhongxue Wang
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Diamond Wheel ◽  
Feature Representation ◽  
Grinding Wheel ◽  
Time Waveform ◽  
Original Time ◽  
Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

scholarly journals Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

1999 ◽  
Vol 8 (3) ◽  
pp. 096369359900800 ◽  
Author(s):  
P. S. Sreejith ◽  
R. Krishnamurthy
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
The Status ◽  
Emission Studies ◽  
Pcbn Tools ◽  
Cutting Speeds ◽  
Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

On the use of jerk and snap in condition monitoring of machinery – review and case studies

2021 ◽  
Vol 63 (8) ◽  
pp. 457-464
Author(s):  
S Lahdelma
Keyword(s):  
Condition Monitoring ◽  
Large Scale ◽  
Early Stage ◽  
Time Derivative ◽  
Additional Insight ◽  
Rolling Bearings ◽  
Monitoring Applications ◽  
Derivatives Of ◽  
Insight Into ◽  
Peak Value

The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.

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