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.

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

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.

Condition Monitoring of a Nuclear Power Plant Check Valve Based on Acoustic Emission and a Neural Network

2005 ◽  
Vol 127 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Min-Rae Lee ◽  
Joon-Hyun Lee ◽  
Jung-Teak Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Condition Monitoring ◽  
Nuclear Power ◽  
Failure Modes ◽  
Check Valve ◽  
Flow Loop ◽  
Artificial Neural

The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes such as disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network.

scholarly journals Fault Detection and Isolation in Electrical Machines using Deep Neural Networks

2019 ◽  
Vol 69 (3) ◽  
pp. 249-253
Author(s):  
M. Sai ◽  
Parth Upadhyay ◽  
Babji Srinivasan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Health Monitoring ◽  
Time Domain ◽  
Deep Neural Networks ◽  
Experimental Studies ◽  
Electrical Machines ◽  
Fault Detection And Isolation ◽  
Challenging Problem ◽  
Original Time

Condition and health monitoring of electrical machines during dynamic loading is a common, yet challenging problem in main battle tanks. Existing methods address this issue by extracting various features which are subsequently used in a classifier to isolate faults. However, this approach relies on the feature set being extracted and therefore most of the time does not provide expected accuracy in identification of faults. In this work, we have used convolution neural network that utilises the original time domain measurements for fault detection and isolation (FDI). Results from experimental studies indicate that the proposed approach can perform FDI with more than 95\% accuracy using commonly available current measurements.

Experimental Study on Intelligent Monitoring of Diamond Grinding Wheel Wear

2008 ◽  
Vol 392-394 ◽  
pp. 714-718 ◽  
Author(s):  
Bo Zhao ◽  
Bao Yu Du ◽  
W.D. Liu
Keyword(s):  
Condition Monitoring ◽  
Time Domain ◽  
Grinding Wheel ◽  
Wheel Wear ◽  
Intelligent Monitoring ◽  
Diamond Grinding Wheel ◽  
Grinding Wheel Wear ◽  
Diamond Grinding ◽  
Characteristic Signal ◽  
Wear Condition

In order to research the relationship between grinding wheel wear and the signal of grinding strength and grinding vibration, the grinding strength signal and grinding vibration signal under different wear condition were carried on digital processing by time-domain, frequency-domain, and wavelet-pocket analysis, and characteristic signal reflecting grinding wheel wear condition was obtained. Grinding wheel wear was monitored by time-domain statistics average value of grinding strength and energy value of three layers wavelet-pocket decomposition frequency band. The method how to set design parameters of neural network is introduced, and their value in condition monitoring network is determined. Mapping model of grinding wheel wear and characteristic signal is established. Recognition effect is satisfied in the experiment of grinding wheel wear condition monitoring. It confirmed the model is reliable and effective. The result shows that the new intelligent monitoring method is effective on monitoring grinding wheel deactivation condition. One new method of diamond grinding wheel wear condition monitoring under precision and ultra-precision grinding is introduced.

scholarly journals Condition Monitoring in Drilling Operation based on Vibration Signals

2019 ◽  
Vol 8 (3) ◽  
pp. 1272-1277
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Time Domain ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Drilling Operation ◽  
Tool Condition ◽  
The Time Domain

Tool condition monitoring is the efficient process for all machining managing operation and the maintenance of machinery operation. Tool condition monitoring implies effective production cost, the rate of tool life, tool quality, dimensional accuracy in terms of tolerance and surface finish in machine shop. Here the machining operation is fully depending on the whims & fancies of the operator. So when a new person operating the machine it makes more troubles in terms to find out the tool wearing point and it make operation difficulty by the operator. To overcome this difficulty a systematic methodology required for machining operation. This paper deals with monitoring the condition on the drilling operation with the help of Accelerometer sensor a physical vibration model 8636C50 having a broad band sensitivity of Sensitivity (±5%) 100.0mV/g and resonant frequency up to 22.0 kHz and performing the drilling operation on EN 24 steel at various operation parameters and analyzing the time domain signal response and frequency domain response graph and implemented analyze the feasibility of proposed methodology for practical applications. Further, the Lab View was used to predict amplitude of work piece vibration which determines the tool condition after various experimental tests. In the time domain, the characteristic parameter during drill wear represent RMS value increase in flank wear and also shows the linear relationship between these two. In the frequency domain, the characteristic parameters during drill failure represent the magnitude of vibration amplitude and the increase in flank wear. Here multilayer Artificial Neural Network (ANN) model, Fuzzy Neural Network and Taguchi Method have been trained with the experimental data using back propagation algorithm. Condition monitoring of drilling is fully depending on the vibration signals. Based on the vibration signal the tool wear point is found out. Experiments results indicated the effect of unconditional drilling operation and detected the tool failure and proper operating condition for drilling machining.

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