Nader Sawalhi

Spectral kurtosis optimization for rolling element bearings

Spectral kurtosis (SK) represents a valuable tool for extracting transients buried in noise, which makes it very powerful for the diagnostics of rolling element bearings. However, SK requires the selection of a time-frequency frame for decomposition, so that the kurtosis of each frequency slot can be estimated over time. This paper proposes a technique to optimise SK for diagnostics of rolling element bearings. This technique involves two steps. First, the power spectral density of the signal is prewhitened using an autoregressive model. Second, the prewhitened signal (consisting of noise and transients) is decomposed using complex Morlet wavelets. The complex Morlet wavelet is used as a filter bank with constant proportional bandwidth (uniform resolution on a logarithmic frequency scale). Different banks are used to select the best filter for the envelope analysis - in terms of centre frequency and bandwidth - as the one that maximizes the SK.

Helicopter Gearbox Bearing Blind Fault Identification Using a Range of Analysis Techniques

Vibration acceleration signals were obtained from an overload test of a Bell 206 helicopter main rotor gearbox in order to complete a blind bearing fault analysis where no knowledge of the fault was made available prior to the analysis. A range of diagnostic techniques was applied. These included power spectral density comparisons, constant percentage bandwidth (CPB) spectrum analysis, SK analysis to determine the frequency bands with maximum impulsiveness and to filter the signal to maximise that impulsiveness, and envelope analysis to determine the fault frequencies. Order tracking was used to compensate for speed fluctuations, while linear prediction using autoregressive models (AR) was used to remove the regular gear meshing contribution in the signals. As a result of applying these techniques, a fault in one of the planetary bearings was identified. A match with the cage frequency and the inner race ball pass frequency indicated deterioration associated with these components. Roller fault frequencies were not directly detected, but the fact that roller faults give a modulation at cage frequency shows that their effect was still detected. SK gave a good measure of the severity of the fault when compared to the amount of metal wear debris in the oil. Details of the test, as well as application of a statistical fault detection technique can be found in a companion paper submitted by the Defence Science and Technology Organisation (DSTO) Australia.

Gearbox bearing fault simulation using a finite element model reduction technique

The dynamics of a mechanical system such as a gearbox assembly comprising shafts, gears and bearings can be simulated using Lumped Parameter Models (LPMs). Finite Element Method (FEM) reduction techniques based on the Craig-Bampton method of Component Mode Synthesis (CMS) are useful in creating more accurate dynamic models. These models, despite having more degrees-of-freedom for the individual components than the LPM, make very much larger FE models computationally tractable. In this paper both these approaches, namely LPM and reduced FEM, are compared to create a dynamic model of a gearbox. Earlier simulation models (both LPM and combined LPM and reduced FEM) are further improved to better match the geometry of the bearing faults used in the experimental measurements, and the experimental results from a gearbox test rig. The dynamic model is used to simulate the vibration signals in the presence of localised inner and outer race faults. The new results show better correspondence with the measured signals, in particular with respect to the detailed response to entry and exit from the fault, which can be used to determine fault size. The paper highlights the plausibility of fault simulation in Machine Condition Monitoring (MCM) where a large amount of data can be gathered without experiencing large numbers of actual failures or carrying out costly and time consuming experiments until failure with seeded faults. The simulation data can be used to train neural networks to automate the diagnostic and prognostic processes.

Signal Processing Tools for Tracking the Size of a Spall in a Rolling Element Bearing

There is considerable interest in diagnostics and prognostics of operating machines based on vibration analysis and signal processing, because the major economic benefit from condition-based monitoring comes from being able to predict with reasonable certainty the likely lead time before breakdown. In the case of rolling element bearings, a number of powerful techniques have been developed in recent years to separate the rather weak signals coming from faulty bearings from strong background vibrations, and to diagnose the type of fault. The MED (minimum entropy deconvolution) technique was initially applied to bearings to reduce the overlap of adjacent impulse responses in high speed bearings and thus allow their diagnosis by envelope analysis. It was then suspected that the technique also might have the potential to separate the impulses from entry into, and exit from an individual fault, and thus give information on the fault size. This paper gives the results of an initial study into the application of MED, and other techniques, to obtain the best measure of the length of a developing spall, to use in prognostic algorithms to estimate safe remaining life, based on current size and rate of evolution with time. It was found that the response to the entry and exit events was markedly different, so considerable pre-processing was required before the MED could be applied. The paper also discusses a number of methods to reduce noise and obtain an averaged estimate of the spall length.

Use of the cepstrum to remove selected discrete frequency components from a time signal

In machine diagnostics there are a number of tools for separating discrete frequency components from random and cyclostationary components. This is the basis of separating gear (deterministic) from bearing (second order cyclostationary) signals for example. Time synchronous averaging (TSA) requires a separate operation, including resampling, to be carried out for each periodic frequency, and the method cannot be used for discrete frequency sidebands, or partial bandwidth spectra. Self adaptive noise cancellation (SANC) and discrete/random separation (DRS) remove all discrete frequencies, whether harmonics or sidebands, and it is not possible to decide if some should be left. The method proposed here uses the cepstrum to localise discrete frequency components, which manifest themselves as harmonic or sideband families. Selected families can be removed in the cepstrum, leaving any it might be desirable to retain, and generating a notch filter that is flexible enough to allow for small speed fluctuations, or even narrow band noise peaks that sometimes result from slight random modulation of periodic signals. Normally, to edit the cepstrum and return to the time domain, it is necessary to use the complex cepstrum, but the latter requires the phase signal to be unwrapped. This is not possible for response signals containing discrete frequencies and noise, where the phase is not continuous. The procedure proposed here uses the real cepstrum to localise and edit the log amplitude of the original signal, removing the unwanted discrete frequency components, and then combines the edited amplitude with the original phase spectrum to return to the time domain. The paper shows how this technique can be used to remove discrete frequency components from signals measured on two machines with a faulty bearing, and then perform envelope analysis on the residual signal to diagnose the bearing fault. One is a gear test rig for which the discrete frequencies are harmonics of the shaft speed and gearmesh frequency, and the other a bladed disc test rig for which the discrete frequencies are harmonics of the shaft speed and bladepass frequency. Envelope analysis can be done on both full bandwidth and partial (zoom) bandwidth signals, the latter to save on computation, and restrict the amount of discrete frequency components to be removed, since the signal envelope is independent of frequency shifts.

Fault severity estimation in rotating mechanical systems using feature based fusion and self-organizing maps

The capability of Self-Organizing Maps (SOM) to visualize high-dimensional data is well known. The presented work concerns a SOM based diagnostic system architecture for the monitoring of fault evolution in bearings. Bearings form an essential part of rotating machinery and their failure is one of the most common causes of machine breakdowns. A SOM based approach has been used to map time series of feature data produced by acceleration sensors in order to capture the process dynamics. The fusion of specific features and the introduction of new features related to fault severity can enable the monitoring of fault evolution. The evolution of system states showing the bearing health trend has been shown to warn of impeding failure.

Vibration signal processing for spall size estimation in rolling element bearings using autoregressive inverse filtration combined with bearing signal synchronous averaging

The main challenging area in the health monitoring of rolling element bearings is the quantification of the spall size using vibration data analysis. This is very crucial for maintenance planning and management decisions. In this article, we present a signal processing scheme for estimating spall sizes in rolling element bearings using autoregressive inverse filtration combined with bearing signal synchronous averaging. The squared envelope of the synchronously averaged signal and its autocorrelation function are used to estimate the spall size. The focus of the preprocessing algorithm using autoregressive inverse filtration resides in enhancing the weak step response events originating from the entry of a rolling element into the spalled region and balancing these with prominent impulse responses which occur when a rolling element strikes the trailing edge. Preprocessing is attained through whitening the shaft order tracked (angular resampled) signal using an autoregressive model based on the shaft synchronously averaged part (autoregressive inverse filtration). Autoregressive inverse filtration is compared to autoregressive filtration based on the raw vibration signal. The selection of the autoregressive model order is realized using Akaike criterion. The efficacy of the two autoregressive filtration algorithms is established by comparing time-domain signals, bearing signal synchronous averages, and their squared envelopes and autocorrelation function. This is done on simulated signals with well-known characteristics and on two sizes of naturally originated and propagated inner race spalls from a high-speed test rig. The sizes of these faults were large in a sense that the rolling element did not bridge over the spall, and this required an adjustment to the size quantification equation to fit this case, which has not been presented before. The combination of autoregressive inverse filtration and the squared envelope of the bearing synchronous averaging gives a superior enhancement to the step response and balances it with the impulse response. This provides the best accuracy in estimating the size of the spall, and unlike other existing algorithms, there exist no need for further processing using wavelets for instance.

Gear Parameter Identification in Wind Turbines Using Diagnostic Analysis of Gearbox Vibration Signals

The correct diagnosis of faulty components in rotating machines requires a pre-knowledge of the characteristics of the system being monitored and the identification of the frequencies of interest. In gearboxes, the number of gear stages and the number of teeth for each gear are required to calculate the gear mesh frequencies and monitor these frequencies and their sidebands. It is not always possible to have this information available, especially in old equipment. In this chapter a fresh approach is presented to deduce such crucial information from the measured vibration signal. The approach focuses on fine tuning harmonic/sideband cursors to capture different gear mesh families. The approach is illustrated on a signal taken from a wind turbine gearbox, which poses the extra challenge of the variable speed within the measurement record. Results show the possibility of identifying the number of teeth for the first two stages with much more confidence than the planetary stage, where a trial and error approach was used to decide on the most likely combination for the ring, sun and planetary gears. This chapter sets a good practice example for understanding the system characteristics by detailed analysis of the vibration signal using finely tuned harmonic and sideband cursors.

Separation of Gear and Bearing Fault Signals from a Wind Turbine Transmission under Varying Speed and Load

Wind turbine transmissions are quite complex, with a multitude of gears and bearings. It is imperative that the different signals are separated from each other in order to locate the source of a problem. They differ from many other gearboxes by operating with a widely varying load over time periods corresponding to individual analysis records, and this gives particular problems with gear vibrations, where the effects of load have to be separated from those of condition. The most efficient turbines operate with widely varying speed as well, and this also has to be compensated for in the analysis procedures.

Computational Fluid Dynamic Analysis of a Vibrating Turbine Blade

This study presents the numerical fluid-structure interaction (FSI) modelling of a vibrating turbine blade using the commercial software ANSYS-12.1. The study has two major aims: (i) discussion of the current state of the art of modelling FSI in gas turbine engines and (ii) development of a “tuned” one-way FSI model of a vibrating turbine blade to investigate the correlation between the pressure at the turbine casing surface and the vibrating blade motion. Firstly, the feasibility of the complete FSI coupled two-way, three-dimensional modelling of a turbine blade undergoing vibration using current commercial software is discussed. Various modelling simplifications, which reduce the full coupling between the fluid and structural domains, are then presented. The one-way FSI model of the vibrating turbine blade is introduced, which has the computational efficiency of a moving boundary CFD model. This one-way FSI model includes the corrected motion of the vibrating turbine blade under given engine flow conditions. This one-way FSI model is used to interrogate the pressure around a vibrating gas turbine blade. The results obtained show that the pressure distribution at the casing surface does not differ significantly, in its general form, from the pressure at the vibrating rotor blade tip.