A suitable method for journal bearing wear measurement

Purpose – Ideally, there is no wear in hydrodynamic lubrication regime. A small amount of wear occurs during start and stop of the machines and the amount of wear is so small that it is difficult to measure with accuracy. Various wear measuring techniques have been used where out-of-roundness was found to be the most reliable method of measuring small wear quantities in journal bearings. This technique was further developed to achieve higher accuracy in measuring small wear quantities. The method proved to be reliable as well as inexpensive. The paper aims to discuss these issues. Design/methodology/approach – In an experimental study, the effect of antiwear additives was studied on journal bearings lubricated with oil containing solid contaminants. The test duration was too long and the wear quantities achieved were too small. To minimise the test duration, short tests of about 90 min duration were conducted and wear was measured recording changes in variety of parameters related to weight, geometry and wear debris. The out-of-roundness was found to be the most effective method. This method was further refined by enlarging the out-of-roundness traces on a photocopier. The method was proved to be reliable and inexpensive. Findings – Study revealed that the most commonly used wear measurement techniques such as weight loss, roughness changes and change in particle count were not adequate for measuring small wear quantities in journal bearings. Out-of-roundness method with some refinements was found to be one of the most reliable methods for measuring small wear quantities in journal bearings working in hydrodynamic lubrication regime. By enlarging the out-of-roundness traces and determining the worn area of the bearing cross-section, weight loss in bearings was calculated, which was repeatable and reliable. Research limitations/implications – This research is a basic in nature where a rudimentary solution has been developed for measuring small wear quantities in rotary devices such as journal bearings. The method requires enlarging traces on a photocopier and determining the shape of the worn area on an out-of-roundness trace on a transparency, which is a simple but a crude method. This may require an automated procedure to determine the weight loss from the out-of-roundness traces directly. This method can be very useful in reducing test duration and measuring wear quantities with higher precision in situations where wear quantities are very small. Practical implications – This research provides a reliable method of measuring wear of circular geometry. The Talyrond equipment used for measuring the change in out-of-roundness due to wear of bearings indicates that this equipment has high potential to be used as a wear measuring device also. Measurement of weight loss from the traces is an enhanced capability of this equipment and this research may lead to the development of a modified version of Talyrond type of equipment for wear measurements in circular machine components. Originality/value – Wear measurement in hydrodynamic bearings requires long duration tests to achieve adequate wear quantities. Out-of-roundness is one of the geometrical parameters that changes with progression of wear in a circular shape components. Thus, out-of-roundness is found to be an effective wear measuring parameter that relates to change in geometry. Method of increasing the sensitivity and enlargement of out-of-roundness traces is original work through which area of worn cross-section can be determined and weight loss can be derived for materials of known density with higher precision.

The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings

The coupling of kurtosis based-indexes and envelope analysis represents one of the most successful and widespread procedures for the diagnostics of incipient faults on rolling element bearings. Kurtosis-based indexes are often used to select the proper demodulation band for the application of envelope-based techniques. Kurtosis itself, in slightly different formulations, is applied for the prognostic and condition monitoring of rolling element bearings, as a standalone tool for a fast indication of the development of faults. This paper shows for the first time the strong analytical connection which holds for these two families of indexes. In particular, analytical identities are shown for the squared envelope spectrum (SES) and the kurtosis of the corresponding band-pass filtered analytic signal. In particular, it is demonstrated how the sum of the peaks in the SES corresponds to the raw 4th order moment. The analytical results show as well a link with an another signal processing technique: the cepstrum pre-whitening, recently used in bearing diagnostics. The analytical results are the basis for the discussion on an optimal indicator for the choice of the demodulation band, the ratio of cyclic content (RCC), which endows the kurtosis with selectivity in the cyclic frequency domain and whose performance is compared with more traditional kurtosis-based indicators such as the protrugram. A benchmark, performed on numerical simulations and experimental data coming from two different test-rigs, proves the superior effectiveness of such an indicator. Finally a short introduction to the potential offered by the newly proposed index in the field of prognostics is given in an additional experimental example. In particular the RCC is tested on experimental data collected on an endurance bearing test-rig, showing its ability to follow the development of the damage with a single numerical index.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.