In the paper a multichannel vibration data processing method is presented in the context of local damage detection in gearboxes. The purpose of the approach is to obtain more reliable information about local damage when using several channels in comparison to results obtained for single channel vibration. The method is a combination of time-frequency representation and Independent Component Analysis (ICA) but applied not to raw time series but to each slice (along to time) from spectrogram. Finally we create new time-frequency map, that after aggregation clearly indicates presence of damage. In the paper we will present details of the method and benefits of using our procedure. We will refer to autocorrelation function of mentioned aggregated new time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). We believe that results are very convincing – detection of cyclic impulses associated to local damage are clearly identifiable. To validate our method we use real vibration data from heavy duty gearbox used in mining industry

Agnieszka Wylomanska

Jacek Wodecki

Jakub Obuchowski

Pawel Stefaniak

Radoslaw Zimroz

English

Vibroengineering PROCEDIA

  © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 2015, VOLUME 6. ISSN 2345-0533 133 
Combination of ICA and time-frequency representations 
of multichannel vibration data for gearbox fault 
detection 
Jacek Wodecki1, Pawel Stefaniak2, Jakub Obuchowski3, Agnieszka Wylomanska4, 
Radoslaw Zimroz5 
1, 2, 3, 5Diagnostics and Vibro-Acoustic Science Laboratory, Wroclaw University of Technology,  
Wroclaw, Poland 
1, 2, 3, 5KGHM CUPRUM Ltd., CBR, Sikorskiego 2-8, 53-659 Wrocław, Poland 
4Hugo Steinhaus Center, Department of Mathematics, Wroclaw University of Technology,  
Wroclaw, Poland 
1Corresponding author 
E-mail: 1jacek.wodecki@pwr.edu.pl, 2pawel.stefaniak@pwr.edu.pl, 3jakub.obuchowski@pwr.edu.pl, 
4agnieszka.wylomanska@pwr.edu.pl, 5radoslaw.zimroz@pwr.edu.pl 
(Accepted 25 September 2015) 
Abstract. In the paper a multichannel vibration data processing method is presented in the context 
of local damage detection in gearboxes. The purpose of the approach is to obtain more reliable 
information about local damage when using several channels in comparison to results obtained 
for single channel vibration. The method is a combination of time-frequency representation and 
Independent Component Analysis (ICA) but applied not to raw time series but to each slice (along 
to time) from spectrogram. Finally we create new time-frequency map, that after aggregation 
clearly indicates presence of damage. In the paper we will present details of the method and 
benefits of using our procedure. We will refer to autocorrelation function of mentioned aggregated 
new time frequency map (1D signal) or simple spectrum (that might be somehow linked to 
classical envelope analysis). We believe that results are very convincing – detection of cyclic 
impulses associated to local damage are clearly identifiable. To validate our method we use real 
vibration data from heavy duty gearbox used in mining industry. 
Keywords: local damage detection, gearbox, belt conveyor, vibration, multichannel data, ICA, 
time-spectrogram. 
1. Introduction 
A problem of fault diagnosis in rotating machines attracts attention of researchers for many 
years. In the literature one can find several comprehensive reviews on damage detection in gears 
and bearings [1-4]. Classical methods incorporate high-order statistics [5], empirical mode 
decomposition [6], wavelet transform [7], time-frequency domain analysis [8-10], bi-frequency 
analysis [1, 11]. Vibration signal from a machinery system is often a mixture of several source 
signals. For instance, a signal acquired on a bearing operating in a belt conveyor driving station 
might be contaminated with vibrations of a gearbox located nearby [12-21], vibration signal from 
a hummer crusher bearing consist of both a signal related to bearing and an impulsive noise related 
to the crushing process [22]. Another example of a multi-source signal is a gearbox with two faults, 
each of different nature [17, 23]. In [24] authors analyze a signal from a gas compressor and 
provide a diagnostic method in case of bearing vibration signal in presence of impulsive noise. In 
this paper we incorporate independent component analysis (ICA) for local damage detection in a 
two-stage gearbox operating in a belt conveyor driving station. The vibration data obtained during 
the experiment represents vibration acceleration of a single gearbox, measured at 3 different 
locations. ICA is performed on time-frequency representations of the signals. The method proved 
that integration of vibration signals from several channels provide a more clear damage indication 
than a single signal does. 
The paper is structured as follows. In section 2 a methodology based on both ICA and 
time-frequency representation is described. Section 3 contains description of the considered 
COMBINATION OF ICA AND TIME-FREQUENCY REPRESENTATIONS OF MULTICHANNEL VIBRATION DATA FOR GEARBOX FAULT DETECTION.  
JACEK WODECKI, PAWEL STEFANIAK, JAKUB OBUCHOWSKI, AGNIESZKA WYLOMANSKA, RADOSLAW ZIMROZ 
134 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 2015, VOLUME 6. ISSN 2345-0533  
machine and results obtained from real data analysis. The last section contains conclusions. 
2. Methodology 
In this section we present the methodology which we use to real vibration signal from heavy 
duty gearbox of belt conveyor used in mining industry. The technique consists of few main steps. 
As it was mentioned, we analyze the multichannel (three dimensional) vibration data. First, the 
decomposition of input channels into time-frequency maps (spectrograms) is performed. The 
spectrogram is an absolute value of the short-time Fourier transform (STFT) defined as follows: 
ܵܶܨܶሺݐ, ݂ሻ = ෍ ݔ௞
ேିଵ
௞ୀ଴
ݓሺݐ െ ݇ሻ݁ଶ௝గ௙௞ே , (1)
where ݓሺݐ െ ߬ሻ is the shifted window and ݔ௞ is the input signal. In the next step of our procedure 
we divide the time-frequency maps into narrow-band slices corresponding to given frequencies. 
As a result of this step we obtain three dimensional sub-signals for each frequency band. Then for 
each of three-dimensional sub-signal we apply the Independent Component Analysis (ICA) which 
returns three independent features. In signal processing, the ICA is a computational method for 
separating a multivariate signal into additive subcomponents (features). This is done by assuming 
that the subcomponents are non-Gaussian signals and that they are statistically independent from 
each other. ICA is a special case of blind source separation [25-33]. 
 
Fig. 1. The scheme of the introduced technique of local damage detection 
In the next step of our analysis for each three-dimensional sub-signal we select one feature, 
mainly this with the greatest kurtosis. As a result we obtain the new time-frequency map which 
COMBINATION OF ICA AND TIME-FREQUENCY REPRESENTATIONS OF MULTICHANNEL VIBRATION DATA FOR GEARBOX FAULT DETECTION.  
JACEK WODECKI, PAWEL STEFANIAK, JAKUB OBUCHOWSKI, AGNIESZKA WYLOMANSKA, RADOSLAW ZIMROZ 
 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 2015, VOLUME 6. ISSN 2345-0533 135 
contains selected features corresponding to given frequencies. On the basis of this map we can 
indicate the informative frequency band. At the end we aggregate the new time-frequency map in 
time domain to extract one-dimensional time series which may contain cyclic components 
resulting from damage. In order to enhance this cyclic behavior we calculate spectrum of the new 
time series and/or its autocorrelation function.  
The scheme of the presented methodology is shown in Fig. 1. 
3. Application to industrial data 
3.1. Machine and experiment description 
The machine considered here is a two-stage gearbox used in drive system for belt conveyor, 
see Fig. 2(a). Data used for validation of the method has been measured with using 3 
accelerometers mounted on the gearbox housing (Fig. 2(b)) and the Brüel&Kjær Pulse system. 
Due to high level of noise and presence of high amplitude components related to mesh frequencies 
informative part of the signal representing damage cannot be seen in the acquired signals. It is 
known that impulsive signal related to damage in gear-wheel on the middle shaft should be 
associated to the fault frequency equals to 4.1 Hz. 
 
a) 
 
b) 
Fig. 2. a) Scheme of the investigated machine, b) location of sensors on gearbox housing 
3.2. Diagnostic data – raw multichannel signal 
Raw multivariate (3D) signal considered here consists of three 2.5 s time-series representing 
gearbox vibration under “normal” operation i.e. during transportation of bulk material. Frequency 
sampling is ௦݂ = 8192 Hz. Raw multichannel signal and its spectrogram representation is shown 
in Figs. 3 and 4, respectively. Both time series and their spectrograms allow to notice some weak 
impulsive/wideband components but it is impossible to distinguished unambiguously. 
Fig. 3. Multichannel input signal 
 
Fig. 4. Spectrograms of the multichannel signal 
0 0.5 1 1.5 2 2.5-50
0
50
Channel A
Time, [s]
Ac
c
0 0.5 1 1.5 2 2.5-100
0
100
Channel B
Time, [s]
Ac
c
0 0.5 1 1.5 2 2.5-50
0
50
Channel C
Time, [s]
Ac
c
COMBINATION OF ICA AND TIME-FREQUENCY REPRESENTATIONS OF MULTICHANNEL VIBRATION DATA FOR GEARBOX FAULT DETECTION.  
JACEK WODECKI, PAWEL STEFANIAK, JAKUB OBUCHOWSKI, AGNIESZKA WYLOMANSKA, RADOSLAW ZIMROZ 
136 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. OCT 2015, VOLUME 6. ISSN 2345-0533  
3.3. Evaluation of algorithm performance with industrial data 
In this section we present the obtained results i.e. performance of the algorithm provided with 
real industrial data. Fig. 5(a) presents output, i.e. new time-frequency map which consists of the 
features extracted by applying ICA to the sub-signals from the initial spectrograms presented in 
Fig. 4. As we observe, the impulses (wideband excitations) are visible much clearly than in the 
spectrograms of multichannel raw signal. Moreover, simple aggregation of 2D map into 1D vector 
by integration of energy for each time instance shows impulsive nature of energy flow. Using 
autocorrelation or simple spectrum one might identify period/frequency of impulse repetition that 
corresponds to fault frequency, see Fig. 5(b). 
 
a) 
 
b) 
Fig. 5. a) Output spectrogram composed from selected ICA features, b) time series extracted from output 
spectrogram, spectrum of integrated time series and autocorrelation function of the integrated time series 
4. Conclusions 
In this paper we have introduced a new method of local damage detection applied to the real 
vibration signal from heavy duty gearbox used in mining industry. This methodology is based on 
the analysis of multichannel time series and its representation in time-frequency domain 
(spectrogram). In order to extract information about the damage we analyze the features obtained 
by applying the ICA to three-dimensional sub-signals corresponding to given frequencies on the 
spectrogram. The introduced technique applied to real signal gives much better results than the 
classical methods based on the analysis of one-dimensional vibration signal. By using the 
methodology we can easier identify the informative frequency band. Moreover, the cyclic 
impulses are more visible and distinguishable. We should mention, the proposed algorithm is 
automatic and can be applied to other vibration signals for which the classical methods do not give 
the desired results. 
Acknowledgements 
This work is partially supported by the Statutory Grant No. S40128 (R. Zimroz), No. S50112 
(G. Zak), No. B50120 (J. Obuchowski), No. B40037 (P. Stefaniak), No. B50137 (P. Stefaniak). 
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Combination of ICA and time-frequency representations of multichannel vibration data for gearbox fault detection

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Combination of ICA and time-frequency representations of multichannel vibration data for gearbox fault detection

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