Abstract
As an important part of rotating machinery, a healthy rotor is critical to ensuring optimal working conditions of the entire system. Considering that the vibration signal of rotor consists of different frequency components when the failure arises, a novel rotor failure detection method based on singular spectrum decomposition (SSD) is presented. The original vibration signal is adaptively decomposed into a number of singular spectrum components (SSCs) by the SSD method. Then, energy separation algorithm (ESA) is adopted to demodulate each singular spectrum component. Finally, the SSDESA timefrequency spectrum can be obtained and the fault features contained in the SSDESA timefrequency spectrum can be identified to determine the fault types. The effectiveness of SSD for harmonic separation was assessed through tones separation analyses, the results show that SSD is able to separate more harmonic pairs of different amplitude ratios than empirical mode decomposition (EMD). Furthermore, three simulations of multicomponent signals were designed to investigate the use of SSD for signal decomposition. The SSD method was then applied to detect signatures caused by rotor oil film whirl in experimental signals and compared to both EMD and ensemble EMD (EEMD). The simulated analysis results reflect that SSD shows superiority to EMD and EEMD in inhibiting mode mixing and extracting the timevarying frequency components. The experimental analysis results demonstrate that the SSD based rotor failure detection method is an alternative method under both constant and variable speed conditions.
Highlights
 Singular spectrum decomposition (SSD) is used to decompose the vibration signal of the defective rotors.
 A SSDESA timefrequency method is proposed with the combination of SSD and energy separation algorithm (ESA).
 The SSDESA timefrequency can extract the fault features of rotors more accurately compared to the EMDHT and EEMDHT timefrequency methods.
 The proposed method can diagnose rotor faults under both constant and variable speed conditions.
1. Introduction
As a main transmission and bearing mechanism of rotating machinery, the rotor plays a pivotal role in the industrial production [1]. With the improvement of the production efficiency, the rotating machinery is growing faster, enduring heavier load, becoming more automated and more complicated, which increases the failure rates of rotors. Rotor condition monitoring and failure detection are of significance to ensure the production safety and avoid the economic loss [2].
The existing research results show the fault vibration signal of rotor contains abundant information of fault features. Therefore, rotor fault diagnosis methods based on vibration analysis have attracted focused attention and are widely applied in practice [3]. The vibration signals of common rotor failures, such as oil whirl, oil film oscillation, rubbing, and cracks, are composed of the harmonic components that are related to the rotor rotating frequency. In order to separate the harmonic signals successfully, varieties of signal decomposition methods have been applied in rotor failure detection, such as empirical mode decomposition (EMD) [4], ensemble EMD (EEMD) [5], local mean decomposition (LMD) [6], ensemble LMD (ELMD) [7, 8], intrinsic timescale decomposition (ITD) [9], local characteristicscale decomposition (LCD) [10], complete overall LCD (CELCD) [11], differentialbased EMD (DEMD) [12], differentialbased EEMD (DEEMD) [13], etc. While these signal decomposition methods have solved some rotor fault diagnosis problems effectively in the applications, envelope fitting must be performed during the decomposition process to successfully obtain the intrinsic mode functions (IMFs) [14]. Consequently, the problem of mode mixing always exists and the decomposition results can be easily affected by abnormal noise and interferences [15]. In recent years, some other parametricbased signal decomposition methods such as the morphological component analysis (MCA) [16, 17], empirical wavelet transform (EWT) [1820], synchro squeezed wavelet transform (SWT) [21, 22], adaptive local iterative filtering (ALIF) [23, 24], and variational mode decomposition (VMD) [25, 26] have also been put forward to improve the decomposition effects for multicomponent signals and applied to rotor fault diagnosis successfully. However, the problem of parameter selection needs to be solved in order to get a good result and it increases the complexity of fault diagnosis. Moreover, during actual operating processes rotors frequently operate under variable speed conditions and traditional signal decomposition methods lose their effectiveness when analyzing timevarying nonstationary signals.
Considering the above problems, a recently developed signal decomposition method, namely singular spectrum decomposition (SSD) is introduced into rotor fault diagnosis in this work. The SSD algorithm is highly based on singular spectrum Analysis (SSA) [27], which has been proved suitable for analyzing nonstationary time series. The advantages of SSD are that it cannot only set the amount of embedding dimension but also select the principal components to choose for reconstructing a specific component series adaptively. Therefore, SSD is a nonparametric decomposition technique and has qualified precision. The timefrequency spectrum consists of the instantaneous frequency (IF) and instantaneous amplitude (IA) of each component of the rotor vibration signal can reflect the health status of the rotor [28]. Combining the signal decomposition methods with the demodulation techniques to perform the timefrequency analysis has been frequently used for rotor failure detection. In this paper, SSD is utilized to separate the rotor failure vibration signal into several singular spectrum components (SSCs). Then, demodulation analysis as a further process is required to calculate the IF and IA of each SSC. Hilbert transform (HT) [29] and energy separation algorithm (ESA) based on TeagerKaiser energy operator (TKEO) [30] are two of the most wellknown demodulation techniques. HT is a linear integral operator while TKEO is a nonlinear operator which is based on differentiation [31]. The ESA demodulation method based on TKEO can compute the IF and IA without involving integral transforms as in HT. ESA has a promising demodulation performance and low computational complexity [32]. Here, ESA is combined with SSD to get the SSDESA timefrequency spectrum in order to provide the judgments for rotor fault diagnosis.
The subsequent contents are organized as follows. Section 2 outlines the principles of the SSD based failure detection technique. In Section 3, the SSD method is assessed via tone separation analysis. In Section 4, simulated analyses are performed to evaluate SSD. In Section 5, experimental verifications are conducted. Lastly, conclusions are drawn in Section 6.
2. The proposed method for rotor fault diagnosis
2.1. Singular spectrum decomposition
SSD is a newly proposed and creative signal decomposition technique which is originated from SSA. Despite SSA has shown the effectiveness in analyzing and predicting nonstationary time series in the previous studies, previous studies suggest a number of challenges exist in automatically setting the value of the embedding dimension and selecting the principal components to reconstruct a signal component that has physical meanings. An improvement of SSD relative to SSA is that it can choose the SSA fundamental parameters adaptively. The decomposed components, referred to as SSCs, can be iteratively derived. The specific implementation steps of every iteration are as follows.
1) Constructing the trajectory matrix.
Given an input signal $y\left(n\right)$ with the length of $N$, if the embedding dimension is set to $M$, an ($M\times N$) matrix $\mathbf{Y}$ will be constructed and its $i$th row would be achieved as:
Hence, $\mathbf{Y}={\left[{\mathbf{y}}_{1}^{T},{\mathbf{y}}_{2}^{T},\dots ,{\mathbf{y}}_{M}^{T}\right]}^{T}$. For a given series, $y\left(n\right)=\left\{\mathrm{1,2},\mathrm{3,4},5\right\}$, if $M$ is set to 3, the trajectory matrix $\mathbf{Y}$ comes to be:
The selection of the embedding dimension $M$ has an important impact on the analysis results. For SSD, $M$ is adaptively chosen as ${f}_{max}/{f}_{s}$, ${f}_{max}$ is the dominant frequency in the power spectral density (PSD) of $y\left(n\right)$, ${f}_{s}$ represents the sampling frequency.
A modified matrix $\mathbf{X}$ is obtained by adjusting the positions of some elements of $\mathbf{Y}$ shown as follows:
The original can be extracted with the application of the diagonal averaging to the modified matrix. This transform will also be adopted in step (3) to extract a singular spectrum component.
2) Decomposition.
The singular value decomposition is performed on the trajectory matrix $\mathbf{Y}$, defined as:
$\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}+\dots +{\lambda}_{M}{\mathbf{u}}_{M}{{\mathbf{v}}_{M}}^{T}={\mathbf{H}}_{1}+{\mathbf{H}}_{2}+\cdots +{\mathbf{H}}_{M},$
where, $\mathbf{U}\in {\mathbf{R}}^{M\times M}$ and $\mathbf{V}\in {\mathbf{R}}^{N\times N}$, include the left and right singular vectors, individually. Both belong to orthogonal matrices. $\mathbf{D}\in {\mathbf{R}}^{M\times N}$ is the singular values matrix.
3) Grouping and reconstruction.
As shown in Eq. (3), the trajectory matrix $\mathbf{Y}$ can be expressed as the sum of $M$ principle components. The $L$ ($L<M$) principle components, whose left eigenvectors exhibits a domain frequency in the frequency range [${f}_{max}\mathrm{\Delta}f$, ${f}_{max}+\mathrm{\Delta}f$], are selected to reconstruct a wanted singular spectrum component. $\mathrm{\Delta}f$ is estimated through a Gaussian interpolation of the PSD of the input signal.
Assume that the selected $L$ principle components contribute to a new matrix $\mathbf{Z}$. We can get the corresponding estimated component by conducting the diagonal average on the modified matrix obtained from the transform of $\mathbf{Z}$ as described in Eq. (2).
The estimated singular spectrum component is then subtracted from $y\left(n\right)$. The above process is performed on the residual iteratively until a stopping criterion is met.
More details about SSD can be found in [27].
2.2. The ESA demodulation based on TKEO
TKEO is an energy tracking method which is originally developed for speech processing [33], for which a continuous signal $s\left(t\right)$ is defined as:
where $\dot{s}\left(t\right)$ and $\ddot{s}\left(t\right)$ represent the first and second derivatives of $s\left(t\right)$. The corresponding discrete expression of the TKEO is given:
From Eq. (5), only three samples are needed for computing the instant energy. Therefore, TKEO possesses a positive time resolution and is easy to carry out expeditiously. The ESA demodulation based on TKEO was previously developed to estimate the IF and IA of a monocomponent amplitude and frequency modulated (AMFM) signal [34], as follows:
From this, a number of discrete energy separation algorithms have been developed, including the discrete ESA1(DESA1) [34] that will be used here and is given by:
where $y\left(n\right)=s\left(n\right)s\left(n1\right)$ represents the backward asymmetric difference of $s\left(n\right)$.
A linear frequency modulation signal $x\left(t\right)$ shown in the following equation is simulated to evaluate the demodulation performance of DESA1. The sampling frequency is 1024 Hz:
The time waveform of $x\left(t\right)$ is shown in Fig. 1. Both DESA1 and HT are employed to calculate the IA and IF of the linear frequency modulation signal. Fig. 2 and 3 show the estimated results of IA and IF using the two methods. The results demonstrate that the demodulation performance of DESA1 is a little better. Both the IF and IA estimated by HT exhibit a worse end effects problem. The cause for the end effect in HT demodulation is associated with a circular convolution issue [35].
Fig. 1The simulated linear frequency modulation signal
ESA is a good demodulation method, but only fit for analyzing the monocomponent signals. Then, a filter bank method must be performed on the original signal to obtain the monocomponents firstly. For this work, SSD is used as the filter bank method.
Fig. 2The instantaneous amplitudes of the linear frequency modulation signal calculated using DESA1 and HT: a) DESA1, b) HT
a)
b)
Fig. 3The instantaneous frequencies of the linear frequency modulation signal calculated using DESA1 and HT: a) DESA1, b) HT
a)
b)
2.3. The SSDESA timefrequency analysis
Based on the merits of SSD and ESA, a time frequency method called SSDESA is proposed for rotor failure detection. A flowchart of the SSDESA method is shown in Fig. 4. The method can be implemented by followings steps:
1) Decompose the rotor vibration signal into some monocomponents ($n$ SSCs) using SSD.
2) Calculate the IF and IA of each SSC using the DESA1 demodulation.
3) Obtain the SSDESA timefrequency spectrum by combing all the demodulation results of monocomponents.
4) Identify the fault type according to the SSDESA timefrequency spectrum.
Fig. 4The flow chart of the SSDESA timefrequency analysis
3. Tones separation analysis
A massive challenge for the signal decomposition methods is the separation of two different superimposed tones [36]. The following input signal adopted in [36] is used to evaluate the effectiveness of SSD for tones separation:
with ${f}_{2}<{f}_{1}<{f}_{s}$, ${a}_{1}$ and ${a}_{2}$ are the amplitudes of the two pure harmonics. Here, we define a function to represent the amplitude ratio, $r={a}_{1}/{a}_{2}$ Both EMD and SSD are applied to decompose the superimposed tones with varying amplitude ratios $r\in \left\{4,1,1/4\right\}$, and the estimated errors in the extraction are calculated with the same way in [36]. The results are displayed in Fig. 5.
As the results reflect, EMD exhibits large, different regions of confusion, where the two harmonics with close frequency can’t be separated correctly, as reported in [36]. Using SSD, the regions with large estimated errors decrease relative to EMD, indicating SSD can separate more tone pairs than EMD. As the results show, SSD exhibits high decomposition accuracy across most of the domain, except where ${f}_{1}$ and ${f}_{2}$ are approximately equal.
Fig. 5The estimation errors of EMD and SSD in the process of 2 tones separation: a), c) and e) are the estimation error plots of EMD, b), d) and f) are the estimation error plots of SSD. The amplitude ratios are set to r= 4, 1, 1/4
a) EMD, $r=$ 4
b) SSD, $r=$ 4
c) EMD, $r=$ 1
d) SSD, $r=$ 1
e) EMD, $r=$ 1/4
f) SSD, $r=$ 1/4
4. Simulation analysis
Rotor vibration signals are always influenced by abnormal events, including noise and other types of interferences, these abnormal events can cause the problem of mode mixing in traditional signal decomposition methods such as EMD, EEMD. Another problem is that existing methods for rotor fault diagnosis are difficult to deal with the variable speed conditions. Therefore, SSD is investigated on three different multicomponent signals to validate its effectiveness for signal decomposition. Simulations are performed in MATLAB (R2012a) using a desktop computer with a 3.30 GHz Pentium DualCore CPU and 4.0 GB of RAM.
4.1. Simulation 1
In order to show the SSD robustness with respect to noise, the signal ${x}_{1}\left(t\right)$ is given for testing, which is composed of three components, i.e., an AM–FM signal ${x}_{11}\left(t\right)$, an amplitude modulated signal ${x}_{12}\left(t\right)$, and a random noise signal ${x}_{13}\left(t\right)$. The expression of ${x}_{1}\left(t\right)$ is presented as:
where, $N=$ 1024, is the signal length. The sampling frequency ${f}_{s}=$ 1024 Hz.
Fig. 6 shows the three components of ${x}_{1}\left(t\right)$. First, SSD is applied to decompose ${x}_{1}\left(t\right)$. Fig. 7 shows the decomposed results. We can find that SSC_{1} and SSC_{3} are respectively corresponding to ${x}_{11}\left(t\right)$ and ${x}_{12}\left(t\right)$. For comparison, EMD and EEMD are used to decompose ${x}_{1}\left(t\right)$ and the first four decomposed IMFs are shown in Figs. 8 and 9, respectively. The IMFs obtained by using EMD and EEMD exhibit mode mixing in addition to false components, and both methods fail to obtain the original components of ${x}_{1}\left(t\right)$. SSD has a stronger ability to resist the noise than EMD and EEMD.
Fig. 6The components of x1t
Fig. 7The decomposed results of x1t using SSD
Fig. 8The first four decomposed IMFs of x1t using EMD
Fig. 9The first four decomposed IMFs of x1t using EEMD
Three kinds of evaluating indexes including the root mean squared error (RMSE) [37, 38], correlation coefficient (CC) [39] and consuming time for the whole decomposition process [38] are employed to evaluate the decomposition performances of the SSD, EMD and EEMD methods. The smaller RMSE and the greater CC values represent the decomposition results are more accurate. Taking the SSD method as an example, from [38], the RMSE is defined as:
where $m\left(t\right)$ and $SSC\left(t\right)$ respectively denote the real component and the corresponding SSC obtained by SSD. The definition of the correlation coefficient is as follows [39]:
where $COV\left(m\right(t)$, $SSC\left(t\right))$ is the covariance of $m\left(t\right)$ and $SSC\left(t\right)$, $Var\left(m\right(t\left)\right)$ and $Var\left(SSC\right(t\left)\right)$ respectively represent the variance of $m\left(t\right)$ and $SSC\left(t\right)$.
The decomposed components of SSC_{1} and SSC_{3} obtained by SSD, IMF_{1} and IMF_{3} obtained by EMD, IMF_{1} and IMF_{3} obtained by EEMD are selected to compute the RMSE and CC indexes with the corresponding true components of ${x}_{1}\left(t\right)$. To evaluate the calculation efficiency, the total time required for each decomposition method was calculated using the “tictoc” function in MATLAB. Results are shown in Table 1. The RMSE values of the SSD method are clearly smaller than for the EMD and EEMD methods, and the CC values of the SSD method are higher. The EMD method takes the least time to accomplish the decomposition and the consuming time of the SSD method is less than the EEMD method. The evaluation results suggest that the SSD method is the most accurate and has satisfactory efficiency.
Table 1The evaluating indexes for the SSD, EMD and EEMD methods used in simulation 1
Methods  RMSE  Correlation coefficient  Consuming time (s)  
${x}_{11}\left(t\right)$  ${x}_{12}\left(t\right)$  ${x}_{11}\left(t\right)$  ${x}_{12}\left(t\right)$  
SSD  0.1120  0.0946  0.9950  0.9933  0.097530 
EMD  0.5498  0.3329  0.8391  0.7587  0.044772 
EEMD  0.4237  0.3118  0.9059  0.7935  8.183337 
4.2. Simulation 2
The second simulated signal ${x}_{2}\left(t\right)$ is formed by a cosine signal ${x}_{21}\left(t\right)$, a linear frequency modulation signal ${x}_{22}\left(t\right)$ and an interval signal ${x}_{23}\left(t\right)$ as the following equation:
where, $N=$ 1024, is the number of sampling points of ${x}_{2}\left(t\right)$. The sampling frequency ${f}_{s}=$ 1024 Hz.
The three components of ${x}_{2}\left(t\right)$ are given in Fig. 10. SSD, EMD and EEMD are used to separate the mixed signal ${x}_{2}\left(t\right)$ for comparisons. Figs. 1113 show the corresponding separation results respectively. As shown in the results, the SSD method decompose ${x}_{2}\left(t\right)$ into three SSCs, and $SS{C}_{1}$, $SS{C}_{2}$, $SS{C}_{3}$ are respectively corresponding to ${x}_{23}\left(t\right)$, ${x}_{21}\left(t\right)$ and ${x}_{22}\left(t\right)$. EMD and EEMD are failed to separate the components of ${x}_{2}\left(t\right)$. The analysis results indicate that the interference can cause mode mixing when using EMD and EEMD, while SSD has a better performance in antiinterference.
The evaluating indexes, RMSE, CC, and consuming time, were compared for Simulation 2, and are presented in Table 2. The SSD method has the highest precision according to the RMSE and CC values. Moreover, SSD is more efficient than EEMD.
4.3. Simulation 3
To validate the effectiveness of SSD in extracting the timevarying frequency components, we design the third simulated signal ${x}_{3}\left(t\right)$, which comprises two linear frequency modulation signals, namely ${x}_{31}\left(t\right)$, ${x}_{32}\left(t\right)$, and a random noise signal ${x}_{33}\left(t\right)$. The former two represent the timevarying frequency components. Eq. (16) is the specific expression of ${x}_{3}\left(t\right)$:
where, the sampling number $N$ is 512 and the frequency sampling frequency ${f}_{s}$ is 1024 Hz. Fig. 14 illustrates the components of ${x}_{3}\left(t\right)$. For comparison, ${x}_{3}\left(t\right)$ is analyzed by SSD, EMD and EEMD simultaneously. Figs. 1517 show their analysis results respectively. SSC_{1} represents the noise component, while SSC_{2} and SSC_{3} respectively correspond to the components ${x}_{31}\left(t\right)$ and ${x}_{32}\left(t\right)$. The IMF_{2} and IMF3 of EMD and EEMD are similar to ${x}_{31}\left(t\right)$ and ${x}_{32}\left(t\right)$, but they endure serious mode mixing and waveform distortion problems. The results suggest the SSD method is effective in separating timevarying frequency components, making it a suitable alternative method for rotor failure detection under variable speed conditions.
Table 2The evaluating indexes for the SSD, EMD and EEMD methods used in simulation 2
Methods  RMSE  Correlation coefficient  Consuming time (s)  
${x}_{21}\left(t\right)$  ${x}_{22}\left(t\right)$  ${x}_{23}\left(t\right)$  ${x}_{21}\left(t\right)$  ${x}_{22}\left(t\right)$  ${x}_{23}\left(t\right)$  
SSD  0.0075  0.0129  0.0186  0.9999  0.9996  0.9804  0.182506 
EMD  0.3714  0.2909  0.3029  0.8530  0.7291  0.0027  0.103784 
EEMD  0.1919  0.2346  0.2996  0.9671  0.8376  0.0063  8.229770 
Fig. 10The components of x2t
Fig. 11The decomposed results of x2t using SSD
Fig. 12The first four decomposed IMFs of x2t using EMD
Fig. 13The first four decomposed IMFs of x2t using EEMD
Again, a comparison is conducted based on the three evaluating indicators and the results are reflected in Table 3. The contrasting results suggest that the SSD method offers the best decomposition performance and decreases the consuming time compared with EEMD.
Table 3The evaluating indexes for the SSD, EMD and EEMD methods used in simulation 3
Methods  RMSE  Correlation coefficient  Consuming time (s)  
${x}_{31}\left(t\right)$  ${x}_{32}\left(t\right)$  ${x}_{31}\left(t\right)$  ${x}_{32}\left(t\right)$  
SSD  0.0540  0.0730  0.9973  0.9792  0.167045 
EMD  0.3647  01958  0.8648  0.8409  0.035285 
EEMD  0.3055  0.1475  0.9034  0.9094  7.238292 
5. Experimental analysis
The Bently RK4 test rig, shown in Fig. 18, was used to simulate an oil film whirl fault under either constant or variable speed conditions. The power source of the test bench comes from an electric motor and the motor drives the system directly through a flexible coupling.
The main components of the rig include a motor, a speedadjusting controller, two sliding bearings, two discs, and one oil feeding system. A phase sensor is installed near the coupling to monitor the rotating speed and a safety device is designed to ensure a safe contact experiment. Oil film whirl signals are derived from eddy current sensor 1 and sensor 2 by monitoring the shaft vibration. The sampling frequency ${f}_{s}=$ 1280 Hz.
Fig. 14The components of x3t
Fig. 15The decomposed results of x3t using SSD
Fig. 16The first four decomposed IMFs of x3t using EMD
Fig. 17The first four decomposed IMFs of x3t using EEMD
Fig. 18Rotor system test rig
5.1. Oil film whirl fault diagnosis under constant speed condition
The first critical speed of the test rig is about 1900 r/min. Experiment results show the oil film whirl fault arises when the rotation speed is about 16001700 r/min after several tests in this test rig. The oil film whirl will evolve into oil film whip when the rotation speed is about 37003800 r/min. The previous studies denote that the vibration signal oil film whirl fault mainly contains two frequency components, ${f}_{r}$_{}(representing the rotating frequency) and about 1/2 ${f}_{r}$. So, the oil film whirl is also called half frequency whirl. The oil film whirl fault under a constant rotating frequency, i.e., ${f}_{r}=$ 46.7 Hz, is performed and Fig. 19 shows the time waveform. An analysis for the signal in Fig. 19 is conducted with the proposed method. Fig. 20(a) exhibits the decomposition components obtained by SSD, two SSCs are obtained. Both the DESA1 and HT method are used to demodulate the two SSCs. As a result, the SSDESA and the SSDHT timefrequency spectrums are obtained as reflected in Fig. 20(b) and Fig. 20(c) separately. Both the SSDESA and SSDHT timefrequency spectrums suggest that the instantaneous frequencies of the two SSCs are ${f}_{r}$ and 1/2${f}_{r}$ respectively corresponding to the fault features of an oil film whirl fault.
Fig. 19The oil film whirl signal under constant speed condition
Fig. 20The analysis results of the signal in Fig. 19 using the SSD method: a) the decomposed SSCs via SSD, b) the SSDESA timefrequency spectrum, c) the SSDHT timefrequency spectrum
a)
b)
c)
However, the SSDHT timefrequency spectrum exhibits worse end effects compared to the SSDESA timefrequency spectrum. For a further comparison, the EMDHT and EEMDHT timefrequency analyses are performed on the oil film whirl vibration signal. The results are respectively given in Figs. 21 and 22. The IMFs obtained by EMD and EEMD reflect serious mode mixing and false components are also observed. Furthermore, the EMDHT and EEMDHT timefrequency spectrums failed to extract the accurate fault characteristic frequencies.
5.2. Oil film whirl fault diagnosis under variable speed condition
In the actual production process, the rotor must support variable speed conditions. Detecting rotor failures under variable speed conditions is a challenging task. The test bench in Fig. 18 can complete the stepless speed regulation within the scope of 010000 r/min by adjusting the speedadjusting controller. To carry out the experiment for an oil film whirl fault under the variable speed condition, the rotational speed was gradually increased from zero to a maximum speed just under 3800 r/min. The vibration signal was analyzed as the rotational speed was increased to 18002400 r/min and the signal waveform is illustrated in Fig. 23. The vibration signal is also decomposed into two components by SSD as Fig. 24(a) shows. Fig. 24(b) and Fig. 24(c) respectively show the SSDESA and SSDHT timefrequency spectrums. The instantaneous frequencies of the SSCs are ${f}_{r}$ and 1/2 ${f}_{r}$ for each timefrequency spectrum. It is visible that the timefrequency aggregation of the SSDESA method is a little better. Results for the EMDHT and EEMDHT methods are shown in Figs. 25 and 26, respectively. It’s a pity that both methods failed to separate the two potential characteristic frequencies. The proposed method shows an advantage in failure detection under variable speed conditions.
Fig. 21The analysis results of the signal in Fig. 19 using the EMD method: a) the first four IMFs obtained by EMD, b) the EMDHT timefrequency spectrum
a)
a)
Fig. 22The analysis results of the signal in Fig. 19 using the EEMD method: a) the first four IMFs obtained by EEMD, b) the EEMDHT timefrequency spectrum
a)
b)
Fig. 23The oil film whirl signal under variable speed condition
Fig. 24The analysis results of the signal in Fig. 23 using the SSD method: a) the decomposed SSCs using SSD, b) the SSDESA timefrequency spectrum, c) the SSDHT timefrequency spectrum
a)
b)
c)
Fig. 25The analysis results of the signal in Fig. 23 using the EMD method: a) the first four IMFs obtained by EMD, b) the EMDHT timefrequency spectrum
a)
b)
Fig. 26The analysis results of the signal in Fig. 23 using the EEMD method: a) the first four IMFs obtained by EEMD, b) the EEMDHT timefrequency spectrum
a)
b)
6. Conclusions
In this paper, the SSD method is used to separate the rotor failure vibration signal to get satisfactory decomposition results. Moreover, the SSDESA timefrequency spectrum is applied to detect the signatures of rotor failures. Tones separation investigations demonstrate that SSD has a better performance in tones separation compared with EMD. The simulation results suggest SSD is capable of improving the decomposition precision and is superior to both EMD and EEMD in overcoming mode mixing. Two vibration signals representing an oil film whirl fault are experimentally examined under either constant speed or variable speed conditions. The results exhibit that SSD can successfully extract the fault feature components and the potential characteristic information can be identified from the SSDESA timefrequency spectrum. In conclusion, the proposed method is superior to traditional methods.
Acknowledgements
This paper is supported by the National Natural Science Foundation of China (Grant No. 51777074, 51307058), and the Fundamental Research Funds for the Central Universities (Grant No. 2017XS134, 2017MS190).
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