Speed Rolling Element Bearings Research Articles

LOW SPEED ROLLING BEARING DIAGNOSTICS USING ACOUSTIC EMISSION AND HIGHER ORDER STATISTICS TECHNIQUE

Diagnostics in low speed rolling element bearings is difficult. Not only are normal frequency domain diagnostics methods not appropriate for this application, but the bearing response signals are usually immersed in background noise which make it difficult to detect these faults. Higher order statistics (HOS) techniques have been available for decades but have not been widely applied to machine condition monitoring with the exceptions of skewness and kurtosis. There is however reason to believe that these HOS techniques could play an important role in acoustic emission (AE) based condition monitoring of rolling element bearings at low speeds provided appropriate care is taken. To explore this hypothesis, AE signals at low bearing rotational speeds of 70, 80, 90 and 100 rpm respectively were used in this work for the monitoring of tapered roller bearings. In addition to the well-established statistical parameters such as mean, standard deviation, skewness and kurtosis, higher moments such as hyper flatness are considered in this study. A novel diagnostic method is proposed for fault extraction based on hyperflatness, combined with Kullback-Leibler divergence, and an indicator formula derived with the use of Lempel-Ziv Complexity is given. The Kullback-Leibler divergence is used together with the skewness and hyperflatness to obtain the Kullback-Leibler information Wave (KLW)with which the analysis is performed, and better results obtained as compared to conventional frequency domain analysis.

Estimation of Remaining Useful Life of Slow Speed Bearings Using Acoustic Emission Signals

Various direct and indirect sensing methods for machine condition monitoring have been reported in the literature. Among these methods, acoustic emission technique is one of the effective means of monitoring rolling element bearings during industrial processes. Today, many machines use computerized classification in a wide range of applications. Further, recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this paper concentrates on the estimation of the remaining useful life for bearings whilst in operation. To implement this, a linear regression classifier and multilayer artificial neural network model have been proposed to correlate the selected AE features with corresponding bearing wear throughout laboratory experiments. Results showed that the proposed models exhibit good prediction performance. This paper also presents the use of a new representative fault indicator, signal intensity estimator, employed for AE signals originating from natural degradation of slow speed rolling element bearings. It is concluded that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

Vibration signature analysis of a high speed rotor supported on ball bearings due to localized defects

This paper presents the vibration signature analysis of the high speed rolling element bearings due to localized defects with an un-cracked and a cracked rotor. In rotating machines, one of the main causes of breakdown is faults in ball bearings and crack in rotors. Thus, accurate prediction of severe vibration conditions and characteristic defect frequencies is crucial for the reliable operation. A test rig of a high speed rotor supported on healthy and defective ball bearings is used. The vibration response is obtained and analyzed for the un-cracked and cracked rotor with and without defects on bearing components. The defects such as spalls on the outer race, inner race and on the rolling elements are considered for the study. Combined parametric effects have been analyzed to predict the dynamic response of a rotor-bearing system using response surface method. It is also observed that the severe vibrations occur under bearings with spall on the inner race surface and on a ball under high speed rotor condition.

Fault Diagnosis of High Speed Rolling Element Bearings Due to Localized Defects Using Response Surface Method

In this paper, fault diagnosis of high speed rolling element bearings due to localized defects using response surface method has been done. The localized defects as spalls on outer race, on inner race, and on rolling elements are considered for this study. The mathematical formulation accounted for tangential motions of rolling elements and inner and outer races with the sources of nonlinearity such as Hertzian contact force and internal radial clearance. The nonlinear stiffness is obtained by the application of Hertzian elastic contact deformation theory. The mathematical formulation predicts discrete spectrum having peaks at the characteristic defect frequencies and their harmonics. Experimentation has also been performed to validate the results obtained from the mathematical model and it shows that the model can be successfully used to predict amplitude ratios among various spectral lines with localized surface defects. Combined parametric effects have been analyzed and their influence has been considered with design of experiments and surface response methodology is used to predict the dynamic response of a rotor bearing system.

Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versus vibration measurements

Bearing failure is often attributed to be one of the major causes of breakdown in industrial rotating machines that operate at high and low speeds. This paper presents results of a comparative experimental study on the application of the ultrasound technique for condition monitoring of low speed rolling element bearings and conventional vibration measurements with seeded faults on inner-race defects. The effectiveness of the ultrasound technique is demonstrated through signal processing techniques; use of statistical parameters derived from the time domain, and enveloped spectra in the frequency domain. The results reveal that the ultrasound technique is more effective in detecting low speed bearings failure than that of the vibration measurement.

The Impact of Oil and Sealing Air Flow, Chamber Pressure, Rotor Speed, and Axial Load on the Power Consumption in an Aeroengine Bearing Chamber

Trends in aircraft engines have dictated high speed rolling element bearings up to 3 million DN or more with the consequence of having high amounts of heat rejection in the bearing chambers and high oil scavenge temperatures. A parametric study on the bearing power consumption has been performed with a 124 mm pitch circle diameter (PCD) ball bearing in a bearing chamber that has been adapted from the RB199 turbofan engine DN∼2×106. The operating parameters such as oil flow, oil temperature, sealing air flow, bearing chamber pressure, and shaft speed have been varied in order to assess the impact on the power consumption. This work is the first part of a survey aiming to reduce power losses in bearing chambers. In the first part, the parameters affecting the power losses are identified and evaluated.

Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters

This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing transient vibration signals. An example is included that shows how the procedure was employed to analyse the transient vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration transient vibration signals are being generated.

Fault detection and diagnosis in low speed rolling element bearings using inductive inference classification

An effective procedure for the early detection and objective diagnosis of faults in rolling element bearings is described. The procedure involves the use of an inductive inference theory based classification program called ‘Snob’. The program objectively divides frequency spectra into classes representing different bearing conditions. The estimated description length of each spectrum, which is used for classification, can also be used to detect the early stages of bearing deterioration. The procedure was tested using parametric frequency spectra representing various low speed (60 rpm) rolling element bearing fault conditions. It is shown that the inductive inference theory based spectra classification procedure works well at objectively diagnosing faults in low speed rolling element bearings and allowing early detection of bearing deterioration.

Parametric spectral estimation to detect and diagnose faults in low speed rolling element bearings: Preliminary investigations

The monitoring of low speed bearings (⩽100 rpm) is fraught with difficulties, not the least of which is the impracticality of recording sufficiently long periods of data for appropriate data analysis to be performed. Based on current fast Fourier transform (FFT) techniques, it would be normal to analyse data for several minutes if low frequency bandwidths are being investigated. A parametric model based approach could reduce the data acquisition period to a fraction of the time taken by the FFT technique. This paper presents a brief review of the parametric spectral estimation method, as well as report on some preliminary work that compares the two methods when applied to low speed rolling element bearings.

Fault detection and diagnosis in low speed rolling element bearings Part II: The use of nearest neighbour classification

An effective procedure for automatic fault diagnosis in low speed (⩽100 RPM) rolling element bearings is described. The procedure involves the calculation of a statistical distance measure between vibration signals. The distance measure is then automatically used to distinguish between different fault conditions. A new trending index, based on the statistical distance measure, which may be used for fault detection, is also described.

Fault detection and diagnosis in low speed rolling element bearings Part I: The use of parametric spectra

An effective procedure for vibration condition monitoring of low speed (⩽100 RPM) rolling element bearings is described. The procedure incorporates fault detection using frequency domain trending indices with fault diagnosis using the frequency spectra. By using parametric models to generate frequency spectra, successful fault detection and diagnosis can be achieved from considerably shorter signal lengths than when using conventional procedures. The results presented here follow directly from earlier work done to compare parametric model based frequency spectra with FFT based frequency spectra, when being used to detect and diagnose faults in low speed rolling element bearings.