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Envelope analysis is a widely used method for bearing fault detection. To obtain high detection accuracy, it is critical to select an optimal narrowband for envelope demodulation. Fast Kurtogram is an effective method for optimal narrowband selection. However, fast Kurtogram is not sufficiently robust because it is very sensitive to random noise and large aperiodic impulses which normally exist in practical application. To achieve the purpose of denoising and frequency band optimization, this paper proposes a new fault detector based on modulation signal bispectrum analysis (MSB) for bearing fault detection. As MSB results highlight the modulation effects by suppressing stationary random noise and discrete aperiodic impulses, the detector developed using high magnitudes of MSB can provide optimal frequency bands for fault detection straightforward. Performance evaluation results using both simulated data and experimental data show that the proposed method produces more effective and robust detection results for different types of bearing faults, compared with optimal envelope analysis using fast Kurtogram.

This paper presents a novel method for diagnosing combination faults in planetary gearboxes. Vibration signals measured on the gearbox housing exhibit complicated characteristics because of multiple modulations of concurrent excitation sources, signal paths and noise. To separate these modulations accurately, a modulation signal bispectrum based sideband estimator (MSB-SE) developed recently is used to achieve a sparse representation for the complicated signal contents, which allows effective enhancement of various sidebands for accurate diagnostic information. Applying the proposed method to diagnose an industrial planetary gearbox which coexists both bearing faults and gear faults shows that the different severities of the faults can be separated reliably under different load conditions, confirming the superior performance of this MSB-SE based diagnosis scheme.

For accurate fault detection and diagnosis, this paper focuses on the study of bearing vibration responses under increasing radial clearances due to investable wear and different bearing grades. A nonlinear dynamic model incorporating with local defects and clearance increments is developed for a deep groove ball bearing. The model treats the inner race-shaft and outer race-housing as two lumped masses which are coupled by a nonlinear spring formalized by the Hertzian contact deformation between the balls and races. The solution of the nonlinear equation is obtained by a Runge-Kutta method in Matlab. The results show that the vibrations at fault characteristic frequencies exhibit significant changes with increasing clearances. However, an increased vibration is found for the outer race fault whereas a decreased vibration is found for inner race fault. Therefore, it is necessary to take into account these changes in determining the size of faults.

This paper proposes a scheme to improve the performance of applying envelope analysis in a wireless sensor network for bearing fault diagnosis. The fast kurtogram is realized on the host computer for determining an optimum band-pass filter for the envelope analysis that is implemented on the wireless sensor node to extract the low frequency fault information. Therefore, the vibration signal can be monitored over the bandwidth limited wireless sensor network with both intelligence and real-time performance. Test results have proved that the diagnostic information for different bearing faults can be successfully extracted using the optimum band-pass filter.

As a critical power transmission system, planetary gearbox is widely used in many industrial important machines such as wind turbines, aircraft turbine engines, helicopters. Early fault detection and diagnosis of the gearbox will help to prevent unexpected breakdowns of this important equip-ment. Misalignment is one of the major operating problems in the planetary gearbox which may be caused by inadequate system integration, variable operating conditions and differences of elastic deformations in the system. In this paper, the effect of varying degrees of installation misalignment of planetary gearbox are investigated based on vibration measurements using spectrum analysis and modulation signal bispectrum (MSB) analysis. It has shown that the misalignment can be diagnosed in the low frequency range in which the adverse effect due to co-occurrence of amplitude modula-tion and frequency modulation (AM-FM) effect is low compared with the components around meshing frequencies. Moreover, MSB produces a more accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for all operating conditions. In contrast, spectrum can produce correct results for some of the operating conditions. Keywords: Planetary gearbox, Condition Monitoring, Misalignment, Modulation signal bispectrum.

Centrifugal pumps are widely used in many important industries such as power generation plants, chemical processes and petroleum refiners. The condition monitoring of centrifugal pumps is highly regarded by many researchers and users to minimize unexpected break-downs. Impellers are the core parts of pumps but often appear early damages due to flow cav-itation and erosion. This paper investigates a new approach to monitoring the conditions of impellers using surface vibration with advanced signal analysis. As overall vibration respons-es contain high level of broadband noises due to cavities and turbulences, noise reduction is critical to develop reliable and effective features. However, considering the modulation effect between rotating shaft and blade passing components, a modulation signal bispectrum (MSB) method is employed to extract these deterministic characteristics of modulations, which is different from previous researches in that broadband random sources are often used. Experi-mental results show that the diagnostic features developed by MSB allow impellers with inlet vane damages and exit vane faults to be identified under different operating conditions.

Faults in rolling element bearing are among the main causes of breakdown in rotating machines. Vibration is an effective technique for machine condition monitoring. Vibration signals from a defective bearing with a localized fault contain a series of impulsive responses, which result from the impacts of the defective part(s) with other elements. Most researches carried out have focused on fault location identification. However, limited work has been reported for fault severity estimation, which is critical to make decision for maintenance actions. To improve current diagnostic capability, this paper pays more attention to bearing fault severity diagnosis. It models the vibration sources from bearing defects as an impact process with constant size but three different lengths corresponding to outer race fault, inner race fault and roller fault, respectively. Then an experimental study was followed to evaluate this model. Moreover, the conventional envelope analysis of the measured vibration signals from the tested faulty bearings is optimized by spectral kurtosis (SK) for automatic and reliable fault detection and fault category diagnosis. In the meantime, the diagnostic parameters for fault severity estimation: root mean squared (RMS) values and kurtosis amplitude are developed based on the model results and subsequently evaluated to be agreed vigorously with tested fault cases.

In this paper, motor current signals from electrical control systems, rather than installing additional measurement systems, are characterised for the fault diagnosis of centrifugal pumps. Modulation signal bispectrum (MSB) analysis is applied to reveal the weak nonlinear characteristics of current signals when the pump with different impeller faults operates under a wide range of flow conditions. Experimental results show that two static features including the amplitude at supply frequency and the frequency value of bar-passing frequency can be based on to diagnose impeller defects on exit vane tips and inlet vane tips. In addition, the dynamic parameter of sidebands at vane-passing frequency can also be a good indicator for differentiating between the faults.

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is\ud very important in the field of machinery health monitoring for safe and efficient operations. Currently,\ud envelope analysis is widely used to obtain the bearing defect harmonics from the envelope signal\ud spectrum for the detection and diagnosis and has shown good results in identifying incipient faults\ud occurring on the different parts of a bearing. However, a critical step in implementing envelope\ud analysis is to determine a frequency band that contains faulty bearing signal component with highest\ud signal to noise. Conventionally, the choice of the band is made by manual spectrum comparison via\ud identifying the resonance frequency where the largest change occurred. This paper presents a\ud spectral kurtosis (SK) based method to determine optimum envelope analysis parameters including\ud the filtering band and centre frequency through a short time Fourier transform (STFT). The results\ud show that the maximum amplitude of the kurtogram provides the optimal parameters for band pass\ud filter which allows both small outer race fault and large inner race fault to be determined from\ud optimised envelope spectrum.

Vibrations of a defective rolling bearing often\ud exhibit nonstationary and nonlinear characteristics which are\ud submerged in strong noise and interference components. Thus,\ud diagnostic feature extraction is always a challenge and has\ud aroused wide concerns for a long time. In this paper, the\ud multifractal detrended fluctuation analysis (MF-DFA) is\ud applied to uncover the multifractality buried in nonstationary\ud time series for exploring rolling bearing fault data.\ud Subsequently, a new approach for fault diagnosis is proposed\ud based on MF-DFA and Mahalanobis distance criterion. The\ud multifractality of bearing data is estimated with the\ud generalized Hurst exponent and the multifractal spectrum.\ud Five characteristic parameters which are sensitive to changes\ud of bearing fault conditions are extracted from the spectrum\ud for diagnosis of fault sizes. For benchmarking this new\ud method, the empirical mode decomposition (EMD) method is\ud also employed to analyze the same dataset. The results show\ud that MF-DFA outperforms EMD in revealing the nature of\ud rolling bearing fault data.

Although the wavelet analysis is a powerful tool and has been widely used for the vibration signal based gearbox fault diagnosis, there are some limitations that undermine its application. The results of the wavelet transform do not possess time invariant property, which may result in the loss of useful information and decrease the accuracy of fault diagnosis. Other limitations in wavelet transform are the selection of the suitable threshold and the wavelet function. A main challenge of wavelet analysis is the adaptability of the parameters of the mother wavelet to the time variance of the given signal. To overcome this deficiency, an adaptive Morlet wavelet transform method based on the information entropy optimization is proposed in this study. The proposed wavelet transform method is applied for analyzing the vibration signals to detect and diagnose the faults of a helical gearbox. A comparative study which used the kurtosis maximization to adapt the wavelet parameters are also carried out to evaluate the proposed method.

The rolling element bearing is a key part of machines. The accurate and timely diagnosis of its faults is critical for predictive maintenance. Most re-searches have focused on the fault location identification. To estimate the fault severity accurately, this paper focuses on the study of roller bearing vibration amplitude under increasing radial clearances due to inevitable wear using the modulation signal bispectrum (MSB). The experiment is carried out for bearings with two different clearances for the inner race fault and the outer race fault cases. The results show that the vibration amplitudes at fault characteristic frequencies exhibit significant changes with increasing clearances. However, the amplitudes of vibrations tend to increase with the severity of the outer race fault and decrease with the severity of the inner race fault. Therefore, it is necessary to take into account these effects in diagnosing the size of defect.

As key mechanical components, bearings and gearboxes are employed in most machines. To maintain efficient and safe operations in modern industries, their condition monitoring has received massive attention in recent years. This thesis focuses on the improvement of signal processing approaches to enhance the performance of vibration based monitoring techniques taking into account various data mechanisms and their associated periodic, impulsive, modulating, nonlinear coupling characteristics along with noise contamination. Through in-depth modelling, extensive simulations and experimental verifications upon different and combined faults that often occur in the bearings and gears of representative industrial gearbox systems, the thesis has made following main conclusions in acquiring accurate diagnostic information based on improved signal processing techniques:\ud \ud \ud 1) Among a wide range of advanced approaches investigated, such as adaptive line enhancer (ALE), wavelet transforms, time synchronous averaging (TSA), Kurtogram analysis, and bispectrum representations, the modulation signal bispectrum based sideband estimator (MSB-SE) is regarded as the most powerful tool to enhance the periodic fault signatures as it has the unique property of simultaneous demodulation and noise reduction along with ease of implementation.\ud \ud \ud 2) The proposed MSB-SE based robust detector can achieve optimal band selection and envelope spectrum analysis simultaneously and show more reliable results for bearing fault detection and diagnosis, compared with the popular Kurtogram analysis which highlights too much on localised impulses.\ud \ud \ud 3) The proposed residual sideband analysis yields accurate and consistent diagnostic results of planetary gearboxes across wide operating conditions. This is because that the residual sidebands are much less influenced by inherent gear errors and can be enhanced by MSB analysis.\ud \ud \ud 4) Combined faults in bearings and gears can be detected and separated by MSB analysis. To make the results more reliable, multiple slices of MSB-SE can be averaged to minimise redundant interferences and improve the diagnostic performance.