Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Signal Processing Diagnostic Tool for Rolling Element Bearings Using EMD and MED

The signal processing techniques developed for the diagnostics of mechanical components operating in stationary conditions are often not applicable or are affected by a loss of effectiveness when applied to signals measured in transient conditions. In this chapter, an original signal processing tool is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition, Minimum Entropy Deconvolution and the analytical approach of the Hilbert transform. The tool has been developed to detect localized faults on bearings of traction systems of high speed trains and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on envelope analysis or spectral kurtosis, which represent until now the landmark for bearings diagnostics.

Diagnostic of Rolling Element Bearings with Envelope Analysis in Non-Stationary Conditions

In the field of rolling element bearing diagnostics, envelope analysis has gained in the last years a leading role among the different digital signal processing techniques. The original constraint of constant operating speed has been relaxed thanks to the combination of this technique with the computed order tracking, able to resample signals at constant angular increments. In this way, the field of application of this technique has been extended to cases in which small speed fluctuations occur, maintaining high effectiveness and efficiency. In order to make this algorithm suitable to all industrial applications, the constraint on speed has to be removed completely. In fact, in many applications, the coincidence of high bearing loads, and therefore high diagnostic capability, with acceleration-deceleration phases represents a further incentive in this direction. This chapter presents a procedure for the application of envelope analysis to speed transients. The effect of load variation on the proposed technique will be also qualitatively addressed.

Benefits and risks of large hydrostatic recesses in hydroelectric turbine thrust bearings

Operators of hydroelectric power stations sometimes call upon engineers to modify existing hydroelectric turbines, usually several decades old, for improved maintainability and reliability. One common modification is the hybridisation of plain thrust pads to allow hydrostatic operation to reduce the risk of bearing wipe at low speed (virtually all new installations benefit from this feature). A modification such as this is not a difficult undertaking; however, there are numerous factors that need to be considered in order to maximize bearing performance. One factor that stands out above the others is whether the thrust bearing should be designed to lift the turbine immediately from the standing condition, which presents an interesting challenge: the recess has to have a sufficiently large area in order for the supply pressure to be able to overcome the dead weight of the turbine. If the combination of groove area and pressure is insufficient, then lifting is neither immediate nor guaranteed. This need not be a significant problem, as the bearings have exhibited adequate performance even in the absence of a hydrostatic lubricant supply. A case study is presented whereby relatively large hydrostatic recesses are added to the pads of thrust bearing. It is demonstrated with the aid of simple numerical modelling that the impact of the recess relative to the original pad is small under normal operating conditions. Most surprising, however, is that significant reductions in average oil film temperature and power dissipation are predicted.

Endurance test-rig for diagnostics and prognostics of rolling element bearings

Detection of faults in roller element bearing is a topic widely discussed in the scientific field. Bearings diagnostics is usually performed by analyzing experimental signals, almost always vibration signals, measured during operation. A number of signal processing techniques have been proposed and applied to measured vibrations. The diagnostic effectiveness of the techniques depends on their capacities and on the environmental conditions (i.e. environmental noise). The current trend, especially from an industrial point of view, is to couple the prognostics to the diagnostics. The realization of a prognostic procedure require the definition of parameters able to describe the bearing condition during its operation. Monitoring the values of these parameters during time allows to define their trends depending on the progress of the wear. In this way, a relation between the variation of the selected parameters and the wear progress, useful for diagnostics and prognostics of bearings in real industrial applications, can be established. In this paper, a laboratory test-rig designed to perform endurance tests on roller element bearing is presented. Since the test-rig has operated for a short time, only some preliminary available results are discussed.

Tuning second order cyclostationarity parameters for the diagnostics of rolling element bearings

Failures on rolling element bearings usually originate from cracks that are detectable even in their early stage of propogation by properly analyzing vibration signals measured in the proximity of the bearing. Due to micro-slipping in the roller-races contact, damage-induced vibration signals belong to the family of quasi-periodic signals with a strong second order cyclostationary component. Cyclic coherence and its integrated form are widely considered as the most suitable tools for bearing fault diagnostics and their theoretical bases have been already consolidated. This paper presents how to correctly set the parameters of the cyclostationary analysis tool to be implemented in an automatable algorithm. In the first part of the paper some general guidelines are provided for the specific application. These considerations are further verified, applying cyclostationary tools to data collected in an experimental campaign on a specific test-rig.

Diagnostics of rolling element bearings for the traction system of high speed trains : experimental evidences

Monitoring of the integrity of rolling element bearings in the traction system of high speed trains is a fundamental operation in order to avoid catastrophic failures and to implement effective condition-based maintenance strategies. Diagnostics of rolling element bearings is usually based on vibration signal analysis by means of suitable signal processing techniques. The experimental validation of such techniques has been traditionally performed by means of laboratory tests on artificially damaged bearings, while their actual effectiveness in industrial applications, particularly in the field of rail transport, remains scarcely investigated. This paper will address the diagnostics of bearings taken from the service after a long term operation on a high speed train. These worn bearings have been installed on a test-rig, consisting of a complete full-scale traction system of a high speed train, able to reproduce the effects of wheel-track interaction and bogie-wheelset dynamics. The results of the experimental campaign show that suitable signal processing techniques are able to diagnose bearing failures even in this harsh and noisy application. Moreover, the most suitable location of the sensors on the traction system is also proposed.

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.

Fatigue crack propagation in rocker and roller–rocker bearings of railway steel bridges

In this work, a method is proposed for rolling contact fatigue crack propagation analysis using contact and fracture theories in conjunction with fatigue laws. The proposed method is used in the fatigue analysis of rocker and roller–rocker bearings of a railway open web girder bridge which is instrumented with strain gages. Using a contact algorithm based on the minimum energy principle for bodies in rolling contact with dry friction, the normal and tangential pressure distribution are computed. It is seen that the most critical location of a crack in bearings is at a point very close to the contact region, as expected.

Fatigue crack propagation in rocker and roller-rocker bearings of railway steel bridges

In this work, a method is proposed for rolling contact fatigue crack propagation analysis using contact and fracture theories in conjunction with fatigue laws. The proposed method is used in the fatigue analysis of rocker and roller-rocker bearings of a railway open web girder bridge which is instrumented with strain gages. Using a contact algorithm based on the minimum energy principle for bodies in rolling contact with dry friction, the normal and tangential pressure distribution are computed. It is seen that the most critical location of a crack in bearings is at a point very close to the contact region, as expected. (C) 2010 Elsevier Ltd. All rights reserved.

A study of chemically prepared small metal clusters in the nonmetallic regime

Small gold clusters [mean diameter (d)[less, similar] 1.4 nm], unlike larger clusters, show a higher Au(4f) binding energy relative to the bulk value and the presence of a conductance gap in tunnelling measurements, just as the molecular cluster compound, Au55(PPh3)12Cl6; small platinum clusters show similar nonmetallic features.

Study of corrugations in motor bearings

Corrugation formation is investigated in bearing components in squirrelcage induction motors. The study, conducted on site, measured shaft voltage and analysed motor bearing vibrations from 48 motors on nine sites. The on-site frequency data was compared with the measured natural frequency of the motors. Detailed profilometric, optical and SEM studies were carried out on the surface of failed bearings to aid discussion on the formation of corrugations in bearings used in squirrelcage induction motors.