Time-domain extrapolation method for tractor drive shaft loads in stationary operating conditions

2021 ◽  
Vol 210 ◽  
pp. 143-155
Author(s):  
Zihan Yang ◽  
Zhenghe Song ◽  
Xueyan Zhao ◽  
Xingxiang Zhou
Keyword(s):  
Time Domain ◽  
Operating Conditions ◽  
Drive Shaft ◽  
Extrapolation Method

A Multiple Harmonic Open-Loop Controller for Hydro/Aerodynamic Force Measurements in Rotating Machinery Using Magnetic Bearings

2002 ◽  
Vol 124 (4) ◽  
pp. 827-834 ◽  
Author(s):  
D. O. Baun ◽  
E. H. Maslen ◽  
C. R. Knospe ◽  
R. D. Flack
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Open Loop ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Rotor Vibration ◽  
Aerodynamic Forces ◽  
Analog Input ◽  
Input And Output ◽  
The Time Domain

Inherent in the construction of many experimental apparatus designed to measure the hydro/aerodynamic forces of rotating machinery are features that contribute undesirable parasitic forces to the measured or test forces. Typically, these parasitic forces are due to seals, drive couplings, and hydraulic and/or inertial unbalance. To obtain accurate and sensitive measurement of the hydro/aerodynamic forces in these situations, it is necessary to subtract the parasitic forces from the test forces. In general, both the test forces and the parasitic forces will be dependent on the system operating conditions including the specific motion of the rotor. Therefore, to properly remove the parasitic forces the vibration orbits and operating conditions must be the same in tests for determining the hydro/aerodynamic forces and tests for determining the parasitic forces. This, in turn, necessitates a means by which the test rotor’s motion can be accurately controlled to an arbitrarily defined trajectory. Here in, an interrupt-driven multiple harmonic open-loop controller was developed and implemented on a laboratory centrifugal pump rotor supported in magnetic bearings (active load cells) for this purpose. This allowed the simultaneous control of subharmonic, synchronous, and superharmonic rotor vibration frequencies with each frequency independently forced to some user defined orbital path. The open-loop controller was implemented on a standard PC using commercially available analog input and output cards. All analog input and output functions, transformation of the position signals from the time domain to the frequency domain, and transformation of the open-loop control signals from the frequency domain to the time domain were performed in an interrupt service routine. Rotor vibration was attenuated to the noise floor, vibration amplitude ≈0.2 μm, or forced to a user specified orbital trajectory. Between the whirl frequencies of 14 and 2 times running speed, the orbit semi-major and semi-minor axis magnitudes were controlled to within 0.5% of the requested axis magnitudes. The ellipse angles and amplitude phase angles of the imposed orbits were within 0.3 deg and 1.0 deg, respectively, of their requested counterparts.

A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

2021 ◽  
Author(s):  
Yongzhi Qu ◽  
Gregory W. Vogl ◽  
Zechao Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Time Domain ◽  
Frequency Response Function ◽  
Frequency Component ◽  
Operating Conditions ◽  
Input Output ◽  
The Fourier Transform ◽  
The Time Domain

Abstract The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.

Thermal Conductivity of Single-Crystal Silicon Microbridges Measured by Electrical Resistance Thermometry and Time-Domain Thermoreflectance

2012 ◽  
Author(s):  
Timothy S. English ◽  
Leslie M. Phinney ◽  
Patrick E. Hopkins ◽  
Justin R. Serrano
Keyword(s):  
Thermal Conductivity ◽  
Single Crystal ◽  
Time Domain ◽  
Electrical Resistance ◽  
Microelectromechanical Systems ◽  
Single Crystal Silicon ◽  
Operating Conditions ◽  
Equation Modeling ◽  
Crystal Silicon ◽  
Resistance Thermometry

Accurate thermal conductivity values are essential to the modeling, design, and thermal management of microelectromechanical systems (MEMS) and devices. However, the experimental technique best suited to measure thermal conductivity, as well as thermal conductivity itself, varies with the device materials, fabrication conditions, geometry, and operating conditions. In this study, the thermal conductivity of boron doped single-crystal silicon-on-insulator (SOI) microbridges is measured over the temperature range from 77 to 350 K. The microbridges are 4.6 mm long, 125 μm tall, and two widths, 50 or 85 μm. Measurements on the 85 μm wide microbridges are made using both steady-state electrical resistance thermometry and optical time-domain thermoreflectance. A thermal conductivity of ∼ 77 W/mK is measured for both microbridge widths at room temperature, where both experimental techniques agree. However, a discrepancy at lower temperatures is attributed to differences in the interaction volumes and in turn, material properties, probed by each technique. This finding is qualitatively explained through Boltzmann transport equation modeling under the relaxation time approximation.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

Nonlinear Analysis of an Oscillating Water Column Wave Energy Device in Frequency Domain via Statistical Linearization

2019 ◽  
Author(s):  
Leandro S. P. da Silva ◽  
Celso P. Pesce ◽  
Helio M. Morishita ◽  
Rodolfo T. Gonçalves
Keyword(s):  
System Dynamics ◽  
Frequency Domain ◽  
Water Column ◽  
Wave Energy ◽  
Time Domain ◽  
Computational Cost ◽  
Operating Conditions ◽  
Statistical Linearization ◽  
Large Displacements ◽  
Oscillating Water Column

Abstract Wave energy converters (WECs) are often subject to large displacements during operating conditions. Hence, nonlinearities present in numerical methods to estimate the performance of WECs must be considered for realistic predictions. These large displacements occur when the device operates on resonant conditions, which results in maximum energy conversion. The system dynamics are usually simulated via time domain models in order to being able to capture nonlinearities. However, a high computational cost is associated with those simulations. Alternatively, the present work treats the nonlinearities in the frequency domain via Statistical Linearization (SL). The SL results are compared to the Power Spectrum Density (PSD) of time domain simulations to verify the reliability of the proposed method. In this regard, the work initiates with the derivation of the governing equations of the air-chamber and the Oscillating Water Column (OWC). Then, the SL technique is presented and applied. The SL results show a satisfactory agreement for the system dynamics, mean surface elevation, mean pressure, and mean power compared to time domain simulations. Also, the SL technique produces a rapid estimation of the response, which is an effective approach for the evaluation of numerous environmental conditions and design, and further optimization procedures.

Time domain load extrapolation method for CNC machine tools based on GRA-POT model

2019 ◽  
Vol 103 (9-12) ◽  
pp. 3799-3812 ◽  
Author(s):  
Jialong He ◽  
Xinyue Zhao ◽  
Guofa Li ◽  
Chuanhai Chen ◽  
Zhaojun Yang ◽  
...  
Keyword(s):  
Time Domain ◽  
Machine Tools ◽  
Extrapolation Method ◽  
Cnc Machine Tools ◽  
Cnc Machine

scholarly journals Journal Bearing Fault Detection Based on Daubechies Wavelet

2017 ◽  
Vol 42 (3) ◽  
pp. 401-414 ◽  
Author(s):  
Babu T. Narendiranath ◽  
H.S. Himamshu ◽  
Kumar N. Prabin ◽  
Prabha D. Rama ◽  
C. Nishant
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Operating Conditions ◽  
Centrifugal Pumps ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Vibration Data ◽  
Target Values

AbstractJournal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.

scholarly journals Estimating efficiency of forecasting technical conditions of ship propulsion systems

2021 ◽  
Vol 2021 (4) ◽  
pp. 27-33
Author(s):  
Guriy Alekseevich Kushner ◽  
Victor Andreevich Mamontov
Keyword(s):  
Service Life ◽  
Operating Conditions ◽  
Technical State ◽  
Extrapolation Method ◽  
Normal Operation ◽  
Propeller Shaft ◽  
Shaft Line ◽  
Operating Modes ◽  
Forecasting System ◽  
Vibration Parameters

The article considers an approach to assessing the effectiveness of the most common methods of predicting the technical conditions and failure with reference to the ship shafting. There have been analyzed the main factors in operation of the ship shaft line, which cause the change in its technical state. It has been found that a special feature of some loads acting on the propeller shaft is their stochastic or changing nature over time, which hampers predicting the technical state of the shafting and its units. The features of stochastic and extrapolation forecasting methods have been analyzed. The possibility of using statistical methods in conditions of mass standard production of shafting units with a relatively short regulated service life is estimated. An extrapolation method is proposed for predicting the maximum permissible clearance of stern tube bearings. The case of accumulating samples of measuring results of the propeller shaft sagging in the given time intervals is considered, the approximating functions are constructed. The criteria for the reliability of the results of extrapolation methods for predicting the wear of stern tube bearings are determined. There have been developed the proposals for adapting the causal method as an alternative to the extrapolation method. A schematic diagram of a system for the ship shafting failure predicting has been developed using the registration and analysis of vibration parameters, which serves as the basis for constructing a regression model of damage accumulation. The proposed forecasting system allows studying the actual operating conditions of the shafting, defining the actual external loads and the regularities of their occurrence, measuring deformations and stresses, and determining quantitative indicators of the reliability of the shafting during normal operation and special operating modes, for example, with vibration resonance. The theoretical basis of the algorithm for calculating and registering loads affecting the service life of shafts is proposed.

Finite Element Modelling of Rotating Tires in the Time Domain

2002 ◽  
Vol 30 (1) ◽  
pp. 19-33 ◽  
Author(s):  
O. A. Olatunbosun ◽  
A. M. Burke
Keyword(s):  
Finite Element ◽  
Dynamic Behavior ◽  
Time Domain ◽  
Equations Of Motion ◽  
Operating Conditions ◽  
Element Analysis ◽  
Successful Model ◽  
Linear Effects ◽  
Tire Rotation ◽  
The Time Domain

Abstract Finite element analysis presents an opportunity for a detailed study of the dynamic behavior of a rotating tire under real operating conditions providing a better understanding of the influence of tire construction and material detail on tire dynamic behavior in such areas as ride, handling and noise and vibration transmission. Modelling issues that need to be considered include non-linear effects due to tire inflation and hub loading, tire/road contact and time domain solution of the equations of motion. In this paper techniques and strategies for tire rotation modelling are presented and discussed as a guide to the creation of a successful model.

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