scholarly journals Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 29 ◽  
Author(s):  
Noushin Mokhtari ◽  
Jonathan Gerald Pelham ◽  
Sebastian Nowoisky ◽  
José-Luis Bote-Garcia ◽  
Clemens Gühmann
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Friction And Wear ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Wear Volume ◽  
Time Frequency ◽  
Ae Signal ◽  
Ae Signals

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

In-Situ Wear Monitoring of Slider and Disk Using Acoustic Emission

2000 ◽  
Vol 123 (1) ◽  
pp. 175-180 ◽  
Author(s):  
Kaoru Matsuoka ◽  
Koji Taniguchi ◽  
Masaru Nakakita
Keyword(s):  
Acoustic Emission ◽  
Wear Volume ◽  
Disk Interface ◽  
Wear Process ◽  
Slip Ring ◽  
Ae Signal ◽  
Wear Monitoring ◽  
Transducer Output ◽  
Ae Signals

The methodology has been developed for both the evaluation and analysis of slider/disk interface phenomena. We have been studying the direct relationships between the acoustic emission (AE) signal and wear of materials. The power in the AE signal is directly related to the power required for material removal in the wear process. This technique has been successfully applied to monitoring the wear of the tri-pad contact slider and the disk. The AE transducers were directly mounted onto both the arm with the slider and the disk in order to measure the slider/disk contact behavior. The AE transducer output from the disk was transmitted by the slip ring and the brush. The predicted wear of the slider and the disk based on the AE signals were computed from the relationship mentioned above. The measured wear of the slider and the disk were obtained by atomic force microscopy (AFM) and an optical surface analyzer (OSA) respectively. According to the experimental results, the predicted wear of both the slider and the disk using AE signals agreed with the wear which was measured. Therefore, wear can be estimated and monitored indirectly in-situ using the AE signals without direct measurements of the wear volume.

Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals

2021 ◽  
pp. 135065012199131
Author(s):  
Surojit Poddar ◽  
Naresh Tandon
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Operating Conditions ◽  
Fault Prediction ◽  
Learning Approach ◽  
Emission Data ◽  
Machine Learning Approach ◽  
Particle Contamination

The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.

The Method of Fault Feature Extraction from Acoustic Emission Signals Using Wigner-Ville Distribution

2011 ◽  
Vol 216 ◽  
pp. 732-737 ◽  
Author(s):  
G.F. Bin ◽  
C.J. Liao ◽  
Xue Jun Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Energy ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Fault Features ◽  
Very High Energy ◽  
Ae Signal ◽  
Experimental Data Analysis ◽  
Ae Signals

Wigner-Ville distribution (WVD) has the characteristics of very high-energy accumulation and excellent time-frequency resolution. It is a good way to extract fault feature of acoustic emission (AE) signals due to mechanical component broken. The characteristics of typical AE signals initiated by damages are analyzed. Based on the extracting principle of AE signals from damaged components, the WVD analysis method of AE signal is developed. WVD method is employed to the fault diagnosis of rolling bearings with AE technique. The fault features reading from experimental data analysis are clear, accurate and intuitionistic, meantime, the validity and accuracy of WVD method proposed are nice from the experimental results. Therefore, WVD method is useful for condition monitoring and fault diagnosis in conjunction with AE technique.

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.

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.

Classification of journal bearing friction states based on acoustic emission signals

2018 ◽  
Vol 85 (6) ◽  
pp. 434-442 ◽  
Author(s):  
Noushin Mokhtari ◽  
Clemens Gühmann
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Journal Bearing ◽  
Prediction Method ◽  
Support Vector ◽  
Mechatronic Systems ◽  
Prediction Time ◽  
Ae Signals ◽  
Bearing Friction

Abstract For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. To increase the sensitivity of the extracted features the AE signals were transformed to the frequency-time-domain using continuous wavelet transform (CWT). Significant frequency bands are determined to separate different friction states more effective. A support vector machine (SVM) is used to classify the signals into three different friction classes. In the end the idea for an RUL prediction method by using the already determined information is given and explained.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

Reciprocating compressor valve damage estimation under varying speeds through the acoustic emission technique

2019 ◽  
Vol 10 (5) ◽  
pp. 621-633
Author(s):  
Hoi-Yin Sim ◽  
Rahizar Ramli ◽  
Ahmad Saifizul
Keyword(s):  
Acoustic Emission ◽  
Regression Analysis ◽  
Flow Rate ◽  
Reciprocating Compressor ◽  
Damage Estimation ◽  
Content Type ◽  
Air Compressor ◽  
Ae Signal ◽  
Valve Opening ◽  
Ae Signals

Purpose The purpose of this paper is to examine the effect of reciprocating compressor speeds and valve conditions on the roor-mean-square (RMS) value of burst acoustic emission (AE) signals associated with the physical motion of valves. The study attempts to explore the potential of AE signal in the estimation of valve damage under varying compressor speeds. Design/methodology/approach This study involves the acquisition of AE signal, valve flow rate, pressure and temperature at the suction valve of an air compressor with speed varrying from 450 to 800 rpm. The AE signals correspond to one compressor cycle obtained from two simulated valve damage conditions, namely, the single leak and double leak conditions are compared to those of the normal valve plate. To examine the effects of valve conditions and speeds on AE RMS values, two-way analysis of variance (ANOVA) is conducted. Finally, regression analysis is performed to investigate the relationship of AE RMS with the speed and valve flow rate for different valve conditions. Findings The results showed that AE RMS values computed from suction valve opening (SVO), suction valve closing (SVC) and discharge valve opening (DVO) events are significantly affected by both valve conditions and speeds. The AE RMS value computed from SVO event showed high linear correlation with speed compared to SVC and DVO events for all valve damage conditions. As this study is conducted at a compressor running at freeload, increasing speed of compressor also results in the increment of flow rate. Thus, the valve flow rate can also be empirically derived from the AE RMS value through the regression method, enabling a better estimation of valve damages. Research limitations/implications The experimental test rig of this study is confined to a small pressure ratio range of 1.38–2.03 (free-loading condition). Besides, the air compressor is assumed to be operated at a constant speed. Originality/value This study employed the statistical methods namely the ANOVA and regression analysis for valve damage estimation at varying compressor speeds. It can enable a plant personnel to make a better prediction on the loss of compressor efficiency and help them to justify the time for valve replacement in future.

scholarly journals Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

Acoustic Emission During Athermal Martensitic Transformation in Steels

1990 ◽  
Vol 112 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Xiangying Liu ◽  
Elijah Kannatey-Asibu
Keyword(s):  
Free Energy ◽  
Acoustic Emission ◽  
Martensitic Transformation ◽  
Signal Intensity ◽  
Temperature Derivative ◽  
Process Characteristics ◽  
Ae Signal ◽  
The Relationship ◽  
Ae Signals ◽  
Start Temperature

A relationship developed earlier between acoustic emission signals and the process of athermal martensitic transformation based on the free energy associated with the process is extended and verified experimentally. The relationship is found to model the process characteristics very well. The intensity of AE signal generated during transformation was found to be proportional to the temperature derivative of the fraction of martensite, the cooling rate, and volume of specimen. The AE signal was also found to be related to the carbon content of the steel. During transformation, the signal intensity was found to increase to a peak, and then tail off near the end of the transformation. Values of the martensite start temperature obtained from plots of the total RMS squared AE signals were also found to correlate well with values from the literature.

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