Mechanical Noise Identification Using Time-Frequency Representations

2001 ◽  
Author(s):  
Min-chun Pan
Keyword(s):  
Computation Time ◽  
Fan Noise ◽  
Time Frequency ◽  
Mechanical Noise ◽  
Machine Element ◽  
Electrical Vehicles ◽  
Original Scheme ◽  
Dynamic Signature ◽  
Noise Identification ◽  
Velocity Transmission

Abstract Three computation schemes of time-frequency representations (TFRs) have been developed and implemented to identify different components of mechanical noise originated from the transmission system of electrical vehicles. This study explores the close relationships between three TFRs, i.e. the spectrogram based on windowed Fourier transform (WFT), the Wigner-Ville distribution (WVD), and the smoothed WVD (SWVD). One main purpose is to pursue the efficiency of computing the SWVD of a dynamic signature. The revised scheme can tremendously reduce the computation time to a scale of around 1/90, compared with the original scheme. To assess the validation of these TFR schemes, firstly, four synthetic signals are designed and processed. Secondly, the developed TFRs are applied to distinguish different spectral components of transmission noise, and identify their sources. This study takes an electrical scooter with a continuous velocity transmission (CVT) system as a test bench. The CVT-belt noise, helical-gear whine noise, and fan noise can be clearly identified via the processing of the TFRs. These obtained conclusions can be used as references for machine element modification to improve annoying noise.

Combustion mechanical noise breakdown – turbocharger noise identification on a V8 engine

2019 ◽  
pp. 171-187
Author(s):  
Karl Janssens ◽  
Fabio Bianciardi ◽  
Konstantinos Gryllias ◽  
Simone Delvecchio ◽  
Claudio Manna
Keyword(s):  
Mechanical Noise ◽  
Noise Identification

Hybrid calculations of the three‐dimensional electromagnetic response of buried conductors

Geophysics ◽  
1987 ◽  
Vol 52 (3) ◽  
pp. 301-306 ◽  
Author(s):  
P. K. Gupta ◽  
L. A. Bennett ◽  
A. P. Raiche
Keyword(s):  
Integral Equation ◽  
Finite Element ◽  
Direct Method ◽  
Three Dimensional ◽  
Computation Time ◽  
Initial Guess ◽  
Electromagnetic Response ◽  
Two Dimensional ◽  
Convolution Integrals ◽  
Original Scheme

The hybrid method for computing the electromagnetic response of a three‐dimensional conductor in a layered, conducting half‐space consists of solving a finite‐element problem in a localized region containing the conductor, and using integral‐equation methods to obtain the fields outside that region. The original scheme obtains the boundary values by iterating between the integral‐equation solution and the finite‐element solution, after making an initial guess based on primary values from the field. A two‐dimensional interpolation scheme is then used to speed the evaluation of the [Formula: see text] to [Formula: see text] Green’s function convolution integrals required by most problems. The two algorithms presented are modifications of the original scheme. Both contain a search routine to identify a set of unique points where the convolution integral evaluations are required. By replacing the two‐dimensional interpolation with a one‐dimensional interpolation and reading the convolution integrals from a reference table, computation time was reduced by up to 70 percent and accuracy was improved. The first algorithm retains the iterative technique for enforcing consistency between the integral‐equation and finite‐element solutions on the boundary of the region. The second algorithm solves the coupled integral‐equation/finite‐element system directly. For some models, the direct method has reduced the computation time to 10 percent of that required by the original scheme. In practice the direct scheme is also more stable.

scholarly journals Identification of Bearing Faults using Wavelet Transform

2019 ◽  
Vol 8 (4) ◽  
pp. 9829-9833
Keyword(s):  
Condition Monitoring ◽  
Specific Problem ◽  
Wavelet Packet ◽  
Outer Race ◽  
Time Frequency ◽  
Machine Element ◽  
Rolling Element ◽  
Increasing Demand ◽  
Application Specific ◽  
Inner Race

This research is concerned with description of a scheme for bearing’s localized defect detection based on wavelet packet transform (WPT). WPT provides a high resolution time-frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element on the defect, are detected. The objective of this work is to emphasis on the outer race defect, inner race defect and ball defect. In modern industrial scenario, there is increasing demand for automatic condition monitoring that reduce the gap between digital model and actual product. With reliable condition monitoring, faults such as machine element failures could be identified in their early-stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories.

Analysis and Improvement of Fan Blades Noise

2014 ◽  
Vol 568-570 ◽  
pp. 1706-1709
Author(s):  
Rong Jie Wang ◽  
Hong Wei Chen
Keyword(s):  
Circumferential Velocity ◽  
Aerodynamic Noise ◽  
Radius Of Curvature ◽  
Fan Noise ◽  
Blade Number ◽  
Mechanical Noise ◽  
Blade Control ◽  
Fan Blade ◽  
Main Factor

Fan blades noise is mechanical noise and aerodynamic noise, and the aerodynamic noise is the main noise. Fan's speed, blade number, angle, radius of curvature is the main factor affecting the fan blades noise. Key of control fan noise, should be placed in fan design, Reduce the circumferential velocity, leaned blade, increase the radius of curvature is favorable measures of reducing the noise of the fan blade. Control of noise from the source, will achieve better results.

Methods for Restricting Maximum Exposure Rate in Computerized Adaptative Testing

Methodology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juan Ramon Barrada ◽  
Julio Olea ◽  
Vicente Ponsoda
Keyword(s):  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Computation Time ◽  
Adaptive Testing ◽  
Exposure Rate ◽  
Control Parameters ◽  
The Impact ◽  
Two Alternatives ◽  
Selection Of ◽  
Maximum Exposure

Abstract. The Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method ( Revuelta & Ponsoda, 1998 ), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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