Comparison Between Time-Frequency Techniques to Predict Ball Bearing Faults in Drives Executing Arbitrary Motion Profiles

2008 ◽  
Author(s):  
Marco Cocconcelli ◽  
Cristian Secchi ◽  
Riccardo Rubini ◽  
Cesare Fantuzzi ◽  
Luca Bassi
Keyword(s):  
Velocity Profile ◽  
Cross Correlation ◽  
Fault Prediction ◽  
Variable Velocity ◽  
Outer Race ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Bearing Diagnostics ◽  
Packaging Machine ◽  
Expected Signal

In this paper Wavelet Transform (WT) and Hilbert-Huang Transform (HHT) are used as bearing diagnostics tools in drives executing arbitrary motion profiles. This field is increasingly drawing the attention of the industries because the modern electric motors work as electric cams inducing the shaft to move with a cyclic variable-velocity profile. The literature papers take into account only a constant velocity profile and they are not suitable for such applications. In fact literature methods analyse the signal only in the frequency domain, while in variable-velocity condition the bearing diagnostics should be performed in time domain. Both WT and HHT are time-frequency techniques which describe an input signal as a sum of specific functions. These functions are compared with a signal which simulates the expected vibrations of a bearing with a given fault, e.g. on the outer race. The comparison is done through a cross-correlation between the expected signal and the time-frequency techniques output. WT and HHT are used separately in an industrial case, which consists in bearing fault prediction in an automated packaging machine. In the end of the paper the WT and HHT results are discussed to analyse the different responses.

Characterization of Cavitation During Pump Operation Based on Hilbert-Huang Transform

2018 ◽  
Author(s):  
Hui Sun ◽  
Shouqi Yuan ◽  
Yin Luo ◽  
Bo Gong
Keyword(s):  
Centrifugal Pump ◽  
Longitudinal Vibration ◽  
Process Time ◽  
Current Signal ◽  
Vibration Signals ◽  
Pump Operation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Cavitation Process ◽  
Non Stationary Signal

Cavitation has negative influence on pump operation. In order to detect incipient cavitation effectively, experimental investigation was conducted to through acquisition of current and vibration signals during cavitation process. In this research, a centrifugal pump was modeled for research. The data was analyzed by HHT method. The results show that Torque oscillation resulted from unsteady flow during cavitation process could result in energy variation. Variation regulation of RMS of IMF in current signal is similar to that in axial vibration signal. But RMS of IMF in current signal is more sensitive to cavitation generation. It could be regarded as the indicator of incipient cavitation. RMS variation of IMF in base, radial, longitudinal vibration signals experiences an obvious increasing when cavitation gets severe. Such single variation regulation could be selected as the indicator of cavitation stage recognition. Hilbert-Huang transform is suitable for transient and non-stationary signal process. Time-frequency characteristics could be extracted from results of HHT process to reveal pump operation condition. The contents of current work could provide valuable references for further research on centrifugal pump operation detection.

Vibration Fatigue Test Based on Fiber Bragg Grating Sensors and HHT

2013 ◽  
Vol 328 ◽  
pp. 193-197
Author(s):  
Si Jin Xin ◽  
Zhen Tong
Keyword(s):  
Titanium Alloy ◽  
Fiber Bragg Grating ◽  
Fatigue Test ◽  
Alloy Sheet ◽  
Bragg Grating ◽  
Metal Fatigue ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Vibration Fatigue ◽  
Fbg Sensors

The metal fatigue is an important factor to cause an accident in machine operation, so metal fatigue test is a significant procedure in manufacturing. Fiber Bragg Grating (FBG), as an innovative sensor, has been applied to the measurement of various rotating machines. In this paper, the time-frequency analysis is used to detect the fatigue feature of a titanium alloy measured by FBG sensors. Furthermore, the Hilbert-Huang transform (HHT) is more effective to observe the fatigue limit of the titanium alloy sheet, compared to the Wavelet transform (WT).

scholarly journals Common Crossing Structural Health Analysis with Track-Side Monitoring

2019 ◽  
Vol 21 (3) ◽  
pp. 77-84
Author(s):  
Mykola Sysyn ◽  
Olga Nabochenko ◽  
Franziska Kluge ◽  
Vitalii Kovalchuk ◽  
Andriy Pentsak
Keyword(s):  
Gaussian Process Regression ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Resolution ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Structural Health ◽  
Rolling Surface ◽  
Mode Decomposition ◽  
Inertial Measurements ◽  
Common Crossing

Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of measurement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

2019 ◽  
Vol 233 (5) ◽  
pp. 868-880 ◽  
Author(s):  
Yao Cheng ◽  
Dong Zou
Keyword(s):  
Decomposition Method ◽  
Signal Reconstruction ◽  
Outer Race ◽  
Time Frequency ◽  
Frequency Decomposition ◽  
Local Means ◽  
Residual Noise ◽  
Rolling Element ◽  
Noisy Signals ◽  
Mode Mixing

Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

scholarly journals Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time-Varying Speed Conditions

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Guang-Quan Hou ◽  
Chang-Myung Lee
Keyword(s):  
Research Work ◽  
Defect Size ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Time Varying ◽  
Entry And Exit ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Element Bearing

Fault diagnosis and failure prognostics for rolling element bearing are helpful for preventing equipment failure and predicting the remaining useful life (RUL) to avoid catastrophic failure. Spall size is an important fault feature for RUL prediction, and most research work has focused on estimating the fault size under constant speed conditions. However, estimation of the defect width under time-varying speed conditions is still a challenge. In this paper, a method is proposed to solve this problem. To enhance the entry and exit events, the edited cepstrum is used to remove the determined components. The preprocessed signal is resampled from the time domain to the angular domain to eliminate the effect of speed variation and measure the defect size of a rolling element bearing on outer race. Next, the transient impulse components are extracted by local mean decomposition. The entry and exit points when the roller passes over the defect width on the outer race were identified by further processing the extracted signal with time-frequency analysis based on the continuous wavelet transform. The defect size can be calculated with the angle duration, which is measured from the identified entry and exit points. The proposed method was validated experimentally.

scholarly journals Seismo-Deformation Anomalies Associated with the M6.1 Ludian Earthquake on August 3, 2014

2020 ◽  
Vol 12 (7) ◽  
pp. 1067 ◽  
Author(s):  
Chieh-Hung Chen ◽  
Xiaoning Su ◽  
Kai-Chien Cheng ◽  
Guojie Meng ◽  
Strong Wen ◽  
...  
Keyword(s):  
Temporal Evolution ◽  
Numerical Models ◽  
Material Failure ◽  
Frequency Method ◽  
Pass Filter ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Terminal Stage ◽  
Ludian Earthquake ◽  
Stress Accumulation

A time-frequency method retrieving the acceleration changes in the terminal stage of theM6.1 Ludian earthquake in China is discussed in this article. The non-linear, non-stationaryseismo-demformation was obtained by using the Hilbert–Huang transform and followed by aband-pass filter. We found that the temporal evolution of the residual GNSS-derived orientationexhibits a unique disorder-alignment-disorder sequence days before the earthquake whichcorresponds well with the four stages of an earthquake: elastic strain buildup, crack developments,deformation, and the terminal stage of material failure. The disordering orientations are graduallyaligned with a common direction a few days before the terminal stage. This common direction isconsistent with the most compressive axis derived from the seismological method. In addition, theregion of the stress accumulation, as identified by the size of the disordered orientation, isgenerally consistent with the earthquake preparation zones estimated by using numerical models.

Research of Aircraft Cable Fault Detecting Method Based on Hilbert Huang Transformation

2011 ◽  
Vol 214 ◽  
pp. 138-143
Author(s):  
Tao Jing ◽  
Lu Zhang ◽  
Xu Dong Shi ◽  
Li Wen Wang
Keyword(s):  
Frequency Domain ◽  
Short Circuit ◽  
Reference Signal ◽  
Open Circuit ◽  
Time Domain Reflectometry ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Intermittent Faults ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

Aircraft cable fault diagnosing is considered to be most important for engineering maintenance. Several methods for cables testing have been developed, such as TDR, FDR and TFDR. Time Domain Reflectometry (TDR) relays much on impedance changes on the fault position, which is hard to using in detecting high resistance defects, intermittent defects; Time Frequency Domain Reflectometry (TFDR) method is used to locate intermittent faults, continuous faults and cross-connection faults aircraft wire, however, the algorithm of TFDR is complex. To the "Hard Fault"(short circuit and open circuit), the Hilbert-Huang Transform method is used in determining the optimal bandwidth of the incident reference signal and analyzing the phase and amplitude difference of superimposed signal which from the incident signal and the reflected signal on defects. To the "Fray Fault", Time and Frequency Domain Reflectometry method can be used with the signal processing method with Hilbert-Huang Transform. The experimental results indicate that this method effectively detect all types of aircraft cable fault, particularly for short lengths of cable.

Feature Selection for Fault Detection in Rolling Element Bearings Using Mutual Information

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Karthik Kappaganthu ◽  
C. Nataraj
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Quantitative Measure ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Degree Of Confidence ◽  
Hidden Layer ◽  
Optimal Feature

Rolling element bearings are among the key components in many rotating machineries. It is hence necessary to determine the condition of the bearing with a reasonable degree of confidence. Many techniques have been developed for bearing fault detection. Each of these techniques has its own strengths and weaknesses. In this paper, various features are compared for detecting inner and outer race defects in rolling element bearings. Mutual information between the feature and the defect is used as a quantitative measure of quality. Various time, frequency, and time-frequency domain features are compared and ranked according to their cumulative mutual information content, and an optimal feature set is determined for bearing classification. The performance of this optimal feature set is evaluated using an artificial neural network with one hidden layer. An overall classification accuracy of 97% was obtained over a range of rotating speeds.

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