Fault Detection in Rotating Machinery: Vibration analysis and numerical modeling

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

Using Unbiased Autocorrelation to Enhance Kurtogram and Envelope Analysis Results for Rolling Element Bearing Diagnostics

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 spectral kurtosis (SK) mostly solved this problem but in situations where signal to noise ratio is very low or in presence of non-Gaussian noise this method will fail. This major drawback may noticeably decrease the effectiveness of the SK and goal of this paper is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of 2nd order cyclostationarity, especially in the presence of localised 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 spectral kurtosis. First, a maximal overlap discrete wavelet packet transform (MODWPT) is adopted to split a signal in different frequency bands and central frequencies. Second, unbiased autocorrelation of the squared envelope is calculated to reduce the level of uncorrelated random noise. Third, kurtosis of the autocorrelation is computed and a two dimensional colormap, named Autogram, is presented in order to locate the optimal frequency band for demodulation. The purpose is to increase the detection and characterization of transients in the temporal signal, which contains the bearing defect frequencies as well as appropriate frequency at which the fault impulses are modulated. Finally, the Fourier transform is used to obtain a frequency domain representation of the envelope signal so to identify the defect frequencies of the bearing. The proposed method has been tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics. The results are very positive, and bearing characteristic frequencies from signals masked by Gaussian and non-Gaussian background noise can be extracted

Fast Computation of the Autogram for the Detection of Transient Faults

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

The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis

Envelope analysis is one of the most advantageous methods for rolling element bearing diagnostics but finding a 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 paper is to overcome this problem. Vibration signals from rolling element bearings exhibit high levels of second-order cyclostationarity, especially in the presence of localized faults. The autocovariance function of a 2nd order cyclostationary signal is periodic and the proposed method, named Autogram, 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 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 thresholding method is also proposed to enhance the quality of the frequency spectrum analysis. A new indicator, Combined Squared Envelope Spectrum, is employed to consider all the frequency bands with valuable diagnostic information and to improve the fault detectability of the Autogram. The proposed method is tested on experimental data and compared with literature results so to assess its performances in rolling element bearing diagnostics

Planetary gearbox with localised bearings and gears faults: simulation and time/frequency analysis

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. The objective of this paper 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 frequen- cies of inner/outer race and ball/roller and also their sidebands are discussed thoroughly. Finally, fre- quency 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

The spectral amplitude modulation: A nonlinear filtering process for diagnosis of rolling element bearings

International audienc

Innovative Conveyor Belt Monitoring via Current Signals

This paper proposes, investigates, and validates, by comprehensive experiments, new online automatic diagnostic technology for belt conveyor systems based on motor current signature analysis (MCSA). Motor current signature analysis (MCSA) is a method employed for detecting faults in electric motors by analyzing the current waveforms generated during motor operation. The technology capitalizes on the fact that motor defects, such as mechanical misalignment, bearing damage, and rotor bar defects, cause variations in a motor’s current waveforms, which can be discerned and analyzed using advanced signal processing techniques. MCSA is a non-invasive and cost-effective technique that can detect motor faults in real-time without requiring expensive equipment or disassembly of the motor. In this study, the researchers tested the proposed diagnostic technology, which relies on a power feature. The power feature is calculated as the integrated power within a specific frequency range, centered around the fundamental harmonic of the supply frequency. The purpose of the study is to evaluate for the first time the effectiveness of the proposed diagnostic technology for the diagnosis of a tracking of a belt conveyor. The proposed technology’s effectiveness is assessed using current signals that are obtained for two different scenarios: the normal belt tracking, and a belt mis-tracking under two different loads of a belt conveyor system. The study’s findings indicate that the proposed technology has a high level of diagnostic effectiveness when used for belt mis-tracking. Therefore, it is feasible to recommend this technology for diagnosing tracking issues in belt conveyors

Effect of Fasciola hepatica on acetic acid induced ulcerative colitis in rat model

Background & Objective: Ulcerative colitis is one of the common causes of gastrointestinal tract diseases in developing countries. This disease causes disability, especially in young adults, and imposes many social and economic burdens on the community. The aim of this study was to investigate the effects Effect of Fasciola hepatica extract on acetic acid induced ulcerative colitis in rat model. Materials & Methods: Thirty adult male Wistar rats were divided into 3 equal groups. Colitis was induced by administration of 4% acetic acid. Control group just was administrated phosphate-bufferedsaline (S.C.) and the other groups were received Prednisolone (0.5 mg/kg) and Fasciola hepatica extract (100 mg/kg) for 10 consecutive days.Since then, the animals were euthanized and intestinal tissue and blood samples were taken. Macroscopic and microscopic features, inflammatory mediators as well as cytokines, TNF-α and IL-6 were assessed. Results: The amount of the myeloperoxidase production was the same between the two treatment groups. As compared to the control, both of the treatment groups produced lower amount of TNF-α and IL-6, however this reduction was more pronounced in Prednisolone treated animals than those of extract treated. There were no significant differences between Prednisolone andFasciola extract regarding gross damages and histopathological features. Conclusion: since preparation of Fasciola extract is easy and simple, it may be used as ameliorative agent for ulcerative colitis