Extracting symptoms of bearing faults from noise using a non-linear neural filter

2002 ◽  
Vol 216 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Y Shao ◽  
K Nezu
Keyword(s):  
Adaptive Filter ◽  
Signal To Noise Ratio ◽  
Effective Means ◽  
Failure Detection ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Non Linear ◽  
Detection Capabilities ◽  
Noise Ratio

Improving the signal-to-noise ratio is an important feature for the early detection of faults in bearings subject to large amounts of environmental noises. A method is proposed for improving the signal-to-noise ratio by adaptive neural filtering (ANF). A comparison of failure detection capabilities of a linear adaptive filter using the least mean square (LMS) algorithm and a non-linear adaptive filter using the ANF algorithm in conditions of large amounts of environmental noise is made. Experimental results show that an adaptive filter using a neural filtering algorithm is an effective means for extracting the symptoms of a bearing fault under such conditions.

Non-linear filtering of rare events with large signal-to-noise ratio

1987 ◽  
Vol 24 (04) ◽  
pp. 929-948
Author(s):  
A. J. Heunis
Keyword(s):  
Conditional Distribution ◽  
Signal To Noise Ratio ◽  
Sample Path ◽  
Rare Event ◽  
Dirac Measure ◽  
Linear Filtering ◽  
Large Signal ◽  
Signal To Noise ◽  
Non Linear ◽  
Noise Ratio

The theory of robust non-linear filtering in Clark (1978) and Davis (1980), (1982) is used to evaluate the limiting conditional distribution of a diffusion, given an observation of a ‘rare-event' sample-path of the diffusion, as the signal-to-noise ratio and the diffusion noise-intensity converge to infinity and zero respectively. Under mild conditions it is shown that the limiting conditional distribution is a Dirac measure concentrated at a trajectory which solves a variational problem parametrised by the sample-path of the observed signal.

scholarly journals Joint Estimation of Linear and Non-linear Signal-to-Noise Ratio based on Neural Networks

2018 ◽  
Author(s):  
F. J. Vaquero Caballero ◽  
David Ives ◽  
Qunbi Zhuge ◽  
Maurice O’Sullivan ◽  
Seb J. Savory
Keyword(s):  
Neural Networks ◽  
Signal To Noise Ratio ◽  
Joint Estimation ◽  
Signal To Noise ◽  
Non Linear ◽  
Linear Signal ◽  
Noise Ratio

Measurement of Large Harmonic Vibration Amplitudes

2003 ◽  
Author(s):  
Almantas Mozuras ◽  
Evgueni Podzharov
Keyword(s):  
Narrow Range ◽  
Signal To Noise Ratio ◽  
Standing Waves ◽  
Measurement Process ◽  
Harmonic Vibration ◽  
Vibration Measurement ◽  
Signal To Noise ◽  
Linear Features ◽  
Non Linear ◽  
Noise Ratio

The greater the linearity of a transducer, the more suitable it is for vibration measurement. However, purely linear transducers are not available. It is very common to only use a transducer in a narrow range, where its converting characteristic is close to linear. Unfortunately, only a low signal level can be thus obtained. Therefore, the use of linear features in the measurement process causes non-linear distortions as well as a low signal-to-noise ratio. In this paper, a method for measurement of harmonic vibration amplitudes is presented that allows one to eliminate the above drawbacks. This method allows the extension of the range available for vibration measurement. It could be used, for example, in measurement of standing waves, calibration of devices, etc. This method also gives the ability to obtain maps of the vibration amplitudes with increased surface resolution.

Non-linear filtering of rare events with large signal-to-noise ratio

1987 ◽  
Vol 24 (4) ◽  
pp. 929-948 ◽  
Author(s):  
A. J. Heunis
Keyword(s):  
Conditional Distribution ◽  
Signal To Noise Ratio ◽  
Sample Path ◽  
Rare Event ◽  
Dirac Measure ◽  
Linear Filtering ◽  
Large Signal ◽  
Signal To Noise ◽  
Non Linear ◽  
Noise Ratio

The theory of robust non-linear filtering in Clark (1978) and Davis (1980), (1982) is used to evaluate the limiting conditional distribution of a diffusion, given an observation of a ‘rare-event' sample-path of the diffusion, as the signal-to-noise ratio and the diffusion noise-intensity converge to infinity and zero respectively. Under mild conditions it is shown that the limiting conditional distribution is a Dirac measure concentrated at a trajectory which solves a variational problem parametrised by the sample-path of the observed signal.

scholarly journals Enhancing Signal to Noise Ratio in linear and non-linear excitation microscopy

2009 ◽  
Vol 96 (3) ◽  
pp. 638a
Author(s):  
Emiliano Ronzitti ◽  
Valentina Caorsi ◽  
Francesca Cella ◽  
Alberto Diaspro
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Non Linear ◽  
Noise Ratio

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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