Vibration Analysis for Damage Detection in Ultra filtration Fibers
J. Roussel, P. Ravier, C. Capdessus, M. Jabloun
PRISME laboratory, University of Orleans, France
Introduction
Ultrafiltration using hollow fibers is a commonly used method for drinking water production because it is
able to remove micro particles up to ten nanometers and pathogens without using chemicals. Fibers are
organized like spaghettis inside ultrafiltration modules (tubes). High pressure water is injected through the
fibers. Part of it runs through the fibers membranes and reach the inside of the module as pure as source
water. However, due to the high water pressure, fibers may be damaged, causing the dirty water to reach the
module and be mixed with the pure water. In this paper, we consider the problem of fiber damage detection
by vibration analysis.
Some integrity tests [GUA10] have been investigated that induce the dismounting of modules, which
requires time and money. Hence an on-line based fault diagnosis is needed. Water analyzing methods [GUA10]
cannot determine which module is defective within the set of modules. On the contrary, vibration analysis can
be carried out separately on each module. This process does not need to stop the production but requires the
integration of equipments into or on the modules.
The diagnosis technique used so far consists in injecting air into the fibers within a water filled module. In
the normal case, the air is stopped by the membrane. In the defect case, the air passes through the fiber,
creating some bubbles inside the module and emitting a recognizable acoustic signature. A qualified technician
can listen to these sounds, but the obvious subjectivity of this test may induce both improper estimation of
damages and detection errors. The purpose of this study is to work out an automated bubble detection
technique.
The paper firstly describes the experimental setup. Secondly, noise and transients are characterized. Finally,
detection curves are shown on a recording sample.
Experimental Setup
Microphone seems to be the natural way to replace human ear but the noisy environment of the modules
makes measurements difficult. In addition, the pressure wave crosses multiple medium interfaces: water to
module wall, module wall to air and air to microphone membrane. Using an accelerometer reduces the number
of interfaces by two: water to wall and wall to accelerometer. For this reason and based on preliminary
experiments, the system has been chosen to be based on vibration analysis rather than sound analysis.
DAQ Card NI PCI-4472
with Labview
Signal Conditioner
ME26C-ICP
Figure 1: Data Acquisition Chain
Accelerometer
8786A5
Ultrafiltration
Module
The data acquisition setup is composed of the following parts (Figure 1):
-
-
A high sensitivity (1V/g) integrated circuit piezoelectric accelerometer (ICP) Kistler 8786A5 with a range
of ±5g and a bandwidth of 6Khz.
A signal conditioner Meri ME2680 with an ICP sensor amplifier card ME26C-ICP. The amplifier has an
adjustable gain between 1 and 20, an adjustable output range up to 10V and a bandwidth of 30 kHz.
A National Instrument data acquisition card NI PCI-4472 composed of eight 24-bit analog to digital
delta-sigma converters at a maximum of 102400 samples per second, with an alias-free bandwidth of
45 kHz.
A computer with Labview environment.
In order to develop detection procedures in the classical binary hypothesis testing way, the noise and the
added transients corresponding to the bubbles have to be characterized.
Noise and Transient Signals Characterization
Figure 2: Noise Power Spectral Density
First, in order to characterize the noise inside a module, some measurements have been made from two
healthy modules. The power spectral density of the records exhibit two pink noises at -60 dB/dec up to 800Hz
and -10dB/dec at higher frequencies (Figure 2). The time-frequency representation shows three components at
constant frequencies equal to 300, 800 and 900 Hz that were shown to be induced by the pumps (Figure 3). A
statistic study performed over the temporal signal shows a Gaussian distribution of this background noise.
Figure 3: Noise Spectrogram
Secondly, we identify phenomena inside a damaged module (Figure 4). Two kinds of transients appear:
repetitive and narrow band mono component transients at a center frequency of 1 kHz ± 200 Hz (Figure 5a),
and multi component transients with a center frequency between 200 Hz and 2 kHz (Figure 5b). Mono
component transients are constituted of one central frequency which may vary. Multi component transients
are constituted of several local central frequencies that are synchronized or not. Note that the sounds
produced by these multi component transients are characteristic of bubble pops.
Frequency (Hz)
2000
1500
1000
500
0
2
4
6
Time (s)
8
10
Figure 4: Spectrogram of a defected module
(a)
Mono component transient
(b)
Figure 5: Spectrogram
Multi component transients
Signal Processing
The observed transients have oscillatory components that produce local high coefficients in the frequency
domain. Due to the nonstationary property of the signals of interest, time-frequency or time/scale
representations are dedicated tools to properly analyze these data.
The Gaussian distribution of the background noise allows us to use higher order moments to detect a
transient into noise. The thesis [RAV98] and the paper [LEI11] propose transients’ detection method using the
wavelet packet transform and the kurtogram. According to [RAV98], the wavelet packet decomposition acts
like a filtering that is adapted to the statistical data content. The algorithm works in the two following steps.
First, a denoising procedure based on a wavelet packet decomposition is achieved. This procedure only keeps
frequency bands that are non Gaussian, which allows to deal with the important low frequency components
due to the pink noise. A denoised version of the data is computed by inverse transforming the selected wavelet
packet decomposition coefficients. Both wavelet packet decomposition and selection are based on a kurtosis
computation criterion. Secondly, kurtosis values are evaluated along the time on the denoised signal yielding a
temporal detection curve. The classical energy detector is also computed for comparison.
On the example shown on Figure 6, this method clearly shows the presence of weak transients in
comparison with the simple energy detector, even after filtering in the 200-2000 Hz frequency band. The
presence of transients is accredited by human expertise corroborated by time-frequency representations.
However, this kind of test is not sufficient and necessitates the creation of annotated database in order to get
statistical results. The work is under progress. Moreover, this method cannot identify the class of the detected
transients. Other methods are under investigation for this classification problem.
Signal (a)
5
0
-5
0
0.05
0.1
0.15
0.2
0.25
Kurtogram (b)
0.3
0.35
0.4
0.45
0.5
0.05
0.1
0.15
0.2
0.25
Energy (c)
0.3
0.35
0.4
0.45
0.5
0.05
0.1
0.15
80
60
40
20
4
3
2
1
0.2
0.25
Energy with filter (d)
0.3
0.35
0.4
0.45
6
4
2
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Figure 6: (a) Time representation of a defected module (b) Kurtosis of the remaining wavelet coefficients (c) Energy of the time
representation (d) Energy of the time representation after 200-2000 Hz filtering.
References
[GUA10] Gua, H., and al. « Low-pressure membrane integrity tests for drinking water treatment: A review »
in Elsevier, 2010, Water Research, Vol. 44, pp. 41-57
[RAV98] Ravier, Philippe « Détection de transitoires par ondelettes adaptées - Critères d’adaptation fondés
sur les statistiques d’ordre supérieur » Thesis Signal Image Parole, INPG, Grenoble, 1998
[LEI11] Lei, Yaguo, and al. «Application of an improved kurtogram method for fault diagnosis» in Elsevier,
2011, Mechanical Systems and Signal Processing, Vol. 25, pp. 1738 - 1749