Unlock instant, AI-driven research and patent intelligence for your innovation.

Blind source separation method for single-channel vibration signals

A technology of blind source separation and vibration signal, applied in measuring devices, instruments, measuring ultrasonic/sonic/infrasonic waves, etc., can solve the problems of difficulty in selecting mother wavelets, poor blind source separation effect, ignoring source signal structure, etc., to avoid signal Sparsity constraints, good separation, physical well-defined effects

Inactive Publication Date: 2012-11-28
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these existing algorithms are basically based on the sparse representation of the source signal. When the sparsity of the signal is not good, the effect of blind source separation will be relatively poor.
[0004] The existing single-channel blind source separation methods mainly include the following two types: The first one is the blind separation method based on wavelet decomposition. This method needs to select a suitable mother wavelet. In the case of no prior knowledge of the source signal, select Suitable mother wavelets are difficult
The second is the blind separation method using the intrinsic mode function (EMD component for short), which uses the intrinsic mode function (IMF for short) component obtained by EMD decomposition directly as the input signal for blind source separation. Blind separation, which ignores the structure of the source signal

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Blind source separation method for single-channel vibration signals
  • Blind source separation method for single-channel vibration signals
  • Blind source separation method for single-channel vibration signals

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] Consider three mechanical vibration source signals:

[0048] the s 1 (t)=5sin(2πf 1 t+5)

[0049] the s 2 (t)=2sin(2πf 2 t+10)

[0050] the s 3 (t)=8cos(2πf 3 t)

[0051] In the formula, s 1 (t) is the first vibration source signal, s 2 (t) is the second vibration source signal, s 3 (t) is the third vibration source signal, f 1 = 80Hz, f 2 = 25Hz, f 3 =150Hz, sampling frequency 1000Hz, sampling points 2048, optional one-dimensional vector A 1 =[2.2895, 6.4194, 4.8448], construct single-channel observation signal

[0052] x 1 =A 1 ×[s 1 ,s 2 ,s 3 ] T (6)

[0053] to x 1 Perform EMD decomposition to get three IMF components, x limf =[c 1 , c 2 , c 3 ] T . Randomly select a mixing matrix A, perform signal reconstruction according to formula (7), and generate two mixed observation signals x 2 , x 3 .

[0054] A = 1.53 1.52 ...

Embodiment 2

[0062] The invention is used to blindly separate the single-channel vibration signal of a cab of a faulty vehicle, extract fault features, and analyze fault causes. The fault feature of the car is that the cab bumps up and down at a speed of 30km / h-60km / h. An ICP acceleration sensor is installed on the bottom plate of the cab to collect test data, and the sampling frequency is 5KHz. When sampling, the car was driving on the B-grade road at a speed of about 50Km / h. Acquired single-channel acceleration time-domain signals such as Image 6 shown.

[0063] Time-delay autocorrelation noise reduction processing is performed on the single-channel vibration observation signal to eliminate the interference of noise signals. The denoised signal is decomposed by EMD to obtain 11 IMF components, and a 2×11 matrix A is selected to reconstruct two new vibration acceleration observation signals through the IMF components.

[0064] A = ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a blind source separation method for single-channel vibration signals. Empirical mode decomposition is carried out on single-channel vibration observation signals after noise reduction processing for obtaining intrinsic mode function components, then, the intrinsic mode function components are utilized for reconstructing novel observation signals to carry out source numberestimation, the number of reconstructed observation signals is selected according to the estimated source signal number, blind separation is carried out on the reconstructed observation signals and the original observation signals simultaneously, the independent component is obtained, and the vibration signal feature is extracted, so the blinding source separation of single-channel vibration signals is realized, and the limitation on signal sparsity is avoided.

Description

technical field [0001] The invention relates to a blind source separation technology for mechanically mixed vibration signals, in particular to a blind source separation method for single-channel vibration signals. Background technique [0002] Blind Source Separation (BSS) only separates the source signal from the sensor observation signal, which is a promising signal processing technology. In recent years, more and more blind source separation methods have been applied to vibration signals. processing fields. [0003] According to the algorithm requirements of blind source separation itself, in vibration signal separation, it is generally assumed that the number of observed signals is not less than the number of vibration source signals, but this assumption cannot be realized in engineering. The blind source separation problem in which the number of observed signals is smaller than the number of source signals is usually called underdetermined blind source separation. Un...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G01H17/00
Inventor 李舜酩刘晓伟郭海东
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS