ANFIS system for prognosis of dynamometer high-speed ball bearing based on frequency domain acoustic emission signals

Abstract Acoustic emission signals generated during high speed cutting of steel are investigated. The data are represented in time-folded form. Several methods from linear and nonlinear data analysis based on time- and frequency-domain are applied to the data and reveal signatures of the observed acoustic emission signal. These investigations are first steps for modeling the cutting process by means of differential equations.

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Ball bearing turbocharger vibration management: application on high speed balancer

Mechanics & Industry ◽

10.1051/meca/2020091 ◽

2020 ◽

Vol 21 (6) ◽

pp. 619

Author(s):

Kostandin Gjika ◽

Antoine Costeux ◽

Gerry LaRue ◽

John Wilson

Keyword(s):

Dynamic Behavior ◽

High Speed ◽

Internal Combustion Engines ◽

Ball Bearing ◽

Squeeze Film ◽

Design And Technology ◽

Squeeze Film Damper ◽

Damping Coefficients ◽

Bearing Loads ◽

Stiffness And Damping

Today's modern internal combustion engines are increasingly focused on downsizing, high fuel efficiency and low emissions, which requires appropriate design and technology of turbocharger bearing systems. Automotive turbochargers operate faster and with strong engine excitation; vibration management is becoming a challenge and manufacturers are increasingly focusing on the design of low vibration and high-performance balancing technology. This paper discusses the synchronous vibration management of the ball bearing cartridge turbocharger on high-speed balancer and it is a continuation of papers [1–3]. In a first step, the synchronous rotordynamics behavior is identified. A prediction code is developed to calculate the static and dynamic performance of “ball bearing cartridge-squeeze film damper”. The dynamic behavior of balls is modeled by a spring with stiffness calculated from Tedric Harris formulas and the damping is considered null. The squeeze film damper model is derived from the Osborne Reynolds equation for incompressible and synchronous fluid loading; the stiffness and damping coefficients are calculated assuming that the bearing is infinitely short, and the oil film pressure is modeled as a cavitated π film model. The stiffness and damping coefficients are integrated on a rotordynamics code and the bearing loads are calculated by converging with the bearing eccentricity ratio. In a second step, a finite element structural dynamics model is built for the system “turbocharger housing-high speed balancer fixture” and validated by experimental frequency response functions. In the last step, the rotating dynamic bearing loads on the squeeze film damper are coupled with transfer functions and the vibration on the housings is predicted. The vibration response under single and multi-plane unbalances correlates very well with test data from turbocharger unbalance masters. The prediction model allows a thorough understanding of ball bearing turbocharger vibration on a high speed balancer, thus optimizing the dynamic behavior of the “turbocharger-high speed balancer” structural system for better rotordynamics performance identification and selection of the appropriate balancing process at the development stage of the turbocharger.

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Study on Frequency-domain Equalization Scheme for High-speed Digital Power Line Carrier Systems

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.138.1529 ◽

2018 ◽

Vol 138 (12) ◽

pp. 1529-1538

Author(s):

Norio Sasaki ◽

Qiang Chen ◽

Fumiyuki Adachi

Keyword(s):

Frequency Domain ◽

High Speed ◽

Power Line ◽

Frequency Domain Equalization ◽

Power Line Carrier ◽

Carrier Systems

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APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2019-85-6-53-63 ◽

2019 ◽

Vol 85 (6) ◽

pp. 53-63 ◽

Cited By ~ 2

Author(s):

I. E. Vasil’ev ◽

Yu. G. Matvienko ◽

A. V. Pankov ◽

A. G. Kalinin

Keyword(s):

Acoustic Emission ◽

Damage Detection ◽

High Speed ◽

Early Stage ◽

Video Data ◽

High Speed Video ◽

Damage Diagnostics ◽

Subsurface Defect ◽

Sensitive Coating ◽

Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

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Recent Patents on the Angular Contact Ball Bearing of High-Speed Motorized Spindle

Recent Patents on Mechanical Engineering ◽

10.2174/2212797613666201208000558 ◽

2020 ◽

Vol 13 ◽

Author(s):

Yudong Bao ◽

Linkai Wu ◽

Yanling Zhao ◽

Chengyi Pan

Keyword(s):

High Speed ◽

Ball Bearing ◽

Rapid Development ◽

Ceramic Materials ◽

Development Trend ◽

Numerical Control ◽

Angular Contact Ball Bearing ◽

Practical Applications ◽

High Speed Cutting ◽

Advantages And Disadvantages

Background:: Angular contact ball bearings are the most popular bearing type used in the high speed spindle for machining centers, The performance of the bearing directly affects the machining efficiency of the machine tool, Obtaining a higher value is the direction of its research and development. Objective:: By analyzing the research achievements and patents of electric spindle angular contact bearings, summarizing the development trend provides a reference for the development of electric spindle bearings. Methods:: Through the analysis of the relevant technology of the electric spindle angular contact ball bearing, the advantages and disadvantages of the angular contact ball bearing are introduced, and the research results are combined with the patent analysis. Results:: With the rapid development of high-speed cutting and numerical control technology and the needs of practical applications, the spindle requires higher and higher speeds for bearings. In order to meet the requirements of use, it is necessary to improve the bearing performance by optimizing the structure size and improving the lubrication conditions. Meanwhile, reasonable processing and assembly methods will also have a beneficial effect on bearing performance. Conclusion:: With the continuous deepening of bearing technology research and the use of new structures and ceramic materials has made the bearing's limit speed repeatedly reach new highs. The future development trend of high-speed bearings for electric spindles is environmental protection, intelligence, high speed, high precision and long life.

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Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797612666190530115739 ◽

2019 ◽

Vol 12 (3) ◽

pp. 248-261

Author(s):

Baomin Wang ◽

Xiao Chang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Temperature Rise ◽

High Speed ◽

Ball Bearing ◽

Influence Factors ◽

Ball Bearings ◽

Ann Model ◽

Angular Contact Ball Bearing ◽

Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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

Applied Sciences ◽

10.3390/app11157045 ◽

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.

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Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

Structural Health Monitoring ◽

10.1177/14759217211036025 ◽

2021 ◽

pp. 147592172110360

Author(s):

Dongming Hou ◽

Hongyuan Qi ◽

Honglin Luo ◽

Cuiping Wang ◽

Jiangtian Yang

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

High Speed ◽

High Speed Train ◽

Signal Attenuation ◽

Transmission Path ◽

Bearing Fault Diagnosis ◽

Operation Conditions ◽

Fault Features ◽

High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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