86 research outputs found

    Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution

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    Detecting periodic impulse signal (PIS) is the core of bearing fault diagnosis. Earlier fault detected, earlier maintenance actions can be implemented. On the other hand, remaining useful life (RUL) prediction provides important information when the maintenance should be conducted. However, good degradation features are the prerequisite for effective RUL prediction. Therefore, this paper mainly concerns earlier fault detection and degradation feature extraction for bearing. Maximum correlated kurtosis deconvolution (MCKD) can enhance PIS produced by bearing fault. Whereas, it only achieve good effect when bearing has severe fault. On the contrary, PIS produced by bearing weak fault is always masked by heavy noise and cannot be enhanced by MCKD. In order to resolve this problem, a revised empirical mode decomposition (EMD) algorithm is used to denoise bearing fault signal before MCKD processing. In revised EMD algorithm, a new recovering algorithm is used to resolve mode mixing problem existed in traditional EMD and it is superior to ensemble EMD. For degradation analysis, correlated kurtosis (CK) value is used as degradation indicator to reflect health condition of bearing. Except of theory analysis, simulated bearing fault data, injected bearing fault data, real bearing fault data and bearing degradation data are used to verify the proposed method. Simulated bearing fault data is used to explain the existed problems. Then, injected bearing fault data and real bearing fault data are used to demonstrate the effectiveness of proposed method for fault diagnosis. Finally, bearing degradation data is used to verify the degradation feature CK extracted based on proposed method. All these case studies show the effectiveness of proposed fault diagnosis and degradation tracking method

    Rolling element bearings localized fault diagnosis using signal differencing and median filtration

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    With the increase complexity of bearings’ processing algorithms and the growing trend of using computationally demanding algorithms, it is advantageous to provide analysts with a simple to use and implement algorithm. In this spirit, this paper combines simple functions to provide machine condition analysts with the capacity to diagnose bearing faults without all the complexity and jargon that comes with existing methods. The paper proposes a simplified surveillance and diagnostic algorithm for diagnosing localized faults in rolling element bearings using measured raw vibration signals. The proposed algorithm is based on analyzing the frequency content obtained from applying a median filter on the squared derivative signal (first or higher derivatives) of the vibration signal. The combination of signal differencing and median filters provides a squared envelope signal, which can be used directly to diagnose faults. Signal differencing gives a measure of jerk forces and lifts the high frequency content of the signal. To select the optimum order of differentiation, Kurtosis and maximum correlated kurtosis (MCK) are proposed. Median filter usage represents a better alternative of normal low pass filtration. This completely suppresses impulses with large magnitudes, which may interfere with the diagnosis. The length of the median filter (odd number 3, 5, 7 etc.) is selected as such to include the first 10 harmonics of the defect frequency. Simulated signals are used to demonstrate the efficiency of the proposed algorithm and give insights into the choices of the differentiation and smoothening orders. The proposed processing algorithm gives a first measure (surveillance) for detecting localized faults in rolling element bearings in a very simple way and can be employed in online learning and diagnosis systems. Results obtained from applying the algorithm on complex vibration signals from two types of gearboxes are compared with a well-established semi-automated technique with good correspondence

    Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution

    Get PDF
    Detecting periodic impulse signal (PIS) is the core of bearing fault diagnosis. Earlier fault detected, earlier maintenance actions can be implemented. On the other hand, remaining useful life (RUL) prediction provides important information when the maintenance should be conducted. However, good degradation features are the prerequisite for effective RUL prediction. Therefore, this paper mainly concerns earlier fault detection and degradation feature extraction for bearing. Maximum correlated kurtosis deconvolution (MCKD) can enhance PIS produced by bearing fault. Whereas, it only achieve good effect when bearing has severe fault. On the contrary, PIS produced by bearing weak fault is always masked by heavy noise and cannot be enhanced by MCKD. In order to resolve this problem, a revised empirical mode decomposition (EMD) algorithm is used to denoise bearing fault signal before MCKD processing. In revised EMD algorithm, a new recovering algorithm is used to resolve mode mixing problem existed in traditional EMD and it is superior to ensemble EMD. For degradation analysis, correlated kurtosis (CK) value is used as degradation indicator to reflect health condition of bearing. Except of theory analysis, simulated bearing fault data, injected bearing fault data, real bearing fault data and bearing degradation data are used to verify the proposed method. Simulated bearing fault data is used to explain the existed problems. Then, injected bearing fault data and real bearing fault data are used to demonstrate the effectiveness of proposed method for fault diagnosis. Finally, bearing degradation data is used to verify the degradation feature CK extracted based on proposed method. All these case studies show the effectiveness of proposed fault diagnosis and degradation tracking method

    Investigation assembly state of spindle bearing based on improved maximum correlated kurtosis deconvolution and support vector machine

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    Aiming at the skew and misalignment between the inner and outer rings of the spindle bearing during the installation process, a new method for detecting the spindle bearing assembly state is proposed combined an improved maximum correlated kurtosis deconvolution (IMCKD) and support vector machine (SVM). The vibration and impulse signals induced by bearing assembly skew or dislocation are quite weak. In this paper, the collected vibration signals were preprocessed by IMCKD algorithm with optimized parameters between the normal and skewed state of the outer rings at first, which aims to deconvolve periodic impulse characteristic and enhance the periodic impulse component from a new assembly spindle vibration signal. Then, wavelet packet decomposition method was adopted to extract the characteristic energy value of different frequency bands, and the eigenvector of the assembly state of the spindle bearing was calculated. Furthermore, the support vector machine classification method is implemented to analyze the eigenvectors under different assembly states. Finally, the proposed method was verified by experimental results. The results show that the IMCKD and SVM methods can effectively detect the bearing assembly skew state of the spindle with an accuracy of more than 95 %

    A new bearing fault diagnosis scheme using MED-morphological filter and ridge demodulation analysis

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    For rolling bearing diagnosis, the major challenge of signal processing technique is to extract the quasi-periodic impulses which generated by rolling bearing fault, especially when rolling bearing operated in the condition of heavy noise. This paper proposed a new bearing fault diagnosis scheme. First, the Minimum Entropy Deconvolution (MED) is taken to obtain the impulse excitations from the bearing vibration signal. Then, two kinds of morphological filter, named average filter(AVG) and difference filter (DIF), are used as the assisted filtering unit to reduce the random noise in original signal and integrate the positive and negative impulse excitations in MED filtered signal, respectively. At last, the STFT based ridge demodulation analysis is applied to the purified signal, and the bearing fault is easily identified by spectral analysis of the demodulated signal. Two simulated signal are analyzed to test the performance of the proposed scheme. In the first case, the periodic impulse signal adding with random noise is analyzed. The result shows that MED-AVG-DIA is the best scheme for impulse feature extraction. In the second case, the pure impulse signal which filtered by MED is analyzed. The result shows that STFT based ridge demodulation analysis can achieve better demodulation effect than other demodulation methods. The proposed fault diagnosis scheme has been further verified by simulation signal and measured vibration signals of defective bearing. The result shown that the proposed scheme is feasible and effective for the fault diagnosis of rolling bearing

    Fast Computation of the Autogram for the Detection of Transient Faults

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    Structures and machines maintenance is a hot topic, as their failure can be both expensive and dangerous. Condition-based maintenance regimes are ever more desired so that cost-effective, reliable, and damage-responsive diagnostics techniques are needed. Among the others, Vibration Monitoring using accelerometers is a very little invasive technique that can in principle detect also small, incipient damages. Focusing on transient faults, one reliable processing to highlight their presence is the Envelope analysis of the vibration signal filtered in a band of interest. The challenge of selecting an appropriate band for the demodulation is an optimization problem requiring two ingredients: a utility function to evaluate the performance in a particular band, and a scheme to move within the search space of all the possible center frequencies and band sizes (the dyad {f, Δf}) toward the optimal. These problems were effectively tackled by the Kurtogram, a brute-force computation of the kurtosis of the envelope of the filtered signal (the utility function) of every possible {f, Δf} combination. The complete exploration of the whole plane (f, Δf) is a heavy task which compromises the computational efficiency of the algorithm so that the analysis on a discrete (f, Δf) paving was implemented (Fast Kurtogram). To overcome the lack of robustness to non-Gaussian noise, different utility functions were proposed. One is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the Autogram. To spread this improved algorithm in on-line industrial applications, a fast implementation of the Autogram is proposed in this pape

    Fault Detection in Rotating Machinery: Vibration analysis and numerical modeling

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    This thesis investigates vibration based machine condition monitoring and consists of two parts: bearing fault diagnosis and planetary gearbox modeling. In the first part, a new rolling element bearing diagnosis technique is introduced. Envelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding the suitable frequency band for demodulation has been a substantial challenge for a long time. Introduction of the Spectral Kurtosis (SK) and Kurtogram mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise these methods will fail. This major drawback may noticeably decrease their effectiveness and goal of this thesis is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of 2nd order cyclostationarity, especially in the presence of localized faults. A second-order cyclostationary signal is one whose autocovariance function is a periodic function of time: the proposed method, named Autogram by the authors, takes advantage of this property to enhance the conventional Kurtogram. The method computes the kurtosis of the unbiased autocorrelation (AC) of the squared envelope of the demodulated and undecimated signal, rather than the kurtosis of the filtered time signal. Moreover, to take advantage of unique features of the lower and upper portions of the AC, two modified forms of kurtosis are introduced and the resulting colormaps are called Upper and Lower Autogram. In addition, a new thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. Finally, the proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics. Moreover, a second novel method for diagnosis of rolling element bearings is developed. This approach is a generalized version of the cepstrum pre-whitening (CPW) which is a simple and effective technique for bearing diagnosis. The superior performance of the proposed method has been shown on two real case data. For the first case, the method successfully extracts bearing characteristic frequencies related to two defected bearings from the acquired signal. Moreover, the defect frequency was highlighted in case two, even in presence of strong electromagnetic interference (EMI). The second part presents a newly developed lumped parameter model (LPM) of a planetary gear. Planets bearings of planetary gear sets exhibit high rate of failure; detection of these faults which may result in catastrophic breakdowns have always been challenging. Another objective of this thesis is to investigate the planetary gears vibration properties in healthy and faulty conditions. To seek this goal a previously proposed lumped parameter model (LPM) of planetary gear trains is integrated with a more comprehensive bearing model. This modified LPM includes time varying gear mesh and bearing stiffness and also nonlinear bearing stiffness due to the assumption of Hertzian contact between the rollers/balls and races. The proposed model is completely general and accepts any inner/outer race bearing defect location and profile in addition to its original capacity of modelling cracks and spalls of gears; therefore, various combinations of gears and bearing defects are also applicable. The model is exploited to attain the dynamic response of the system in order to identify and analyze localized faults signatures for inner and outer races as well as rolling elements of planets bearings. Moreover, bearing defect frequencies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, frequency response of the system for different sizes of planets bearing faults are compared and statistical diagnostic algorithms are tested to investigate faults presence and growth
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