A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size

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Abstract

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. The application of acoustic emission (AE) for bearing diagnosis is gaining ground as a complementary diagnostic tool, however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. Furthermore, the extent of bearing damage has eluded the diagnostician. The experimental investigation reported in this paper was centred on the application of the AE technique for identifying the presence and size of a defect on a radially loaded bearing. An experimental test rig was designed such that defects of varying sizes could be seeded onto the outer race of a test bearing. Comparisons between AE and vibration analysis over a range of speed and load conditions are presented. In addition, the primary source of AE activity from seeded defects is investigated. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis. Furthermore, the AE technique also provided an indication of the defect size, allowing the user to monitor the rate of degradation on the bearing; unachievable with vibration analysis.

Introduction

Acoustic emissions (AEs) are defined as transient elastic waves generated from a rapid release of strain energy caused by a deformation or damage within or on the surface of a material [1]. In this particular investigation, AEs are defined as the transient elastic waves generated by the interaction of two surfaces in relative motion. The interaction of surface asperities and impingement of the bearing rollers over the seeded defect on the outer race will generate AEs. Due to the high-frequency content of the AE signatures typical mechanical noise (less than 20 kHz) is eliminated.

Section snippets

Bearing defect diagnosis and AEs

There have been numerous investigations reported on applying AE to bearing defect diagnosis. Roger [2] utilised the AE technique for monitoring slow rotating anti-friction slew bearings on cranes employed for gas production. In addition, successful applications of AE to bearing diagnosis for extremely slow rotational speeds have been reported [3], [4]. Yoshioka and Fujiwara [5], [6] have shown that selected AE parameters identified bearing defects before they appeared in the vibration

Experimental test rig and test bearing

The bearing test rig employed for this study had an operational speed range of 10–4000 rpm with a maximum load capability of 16 kN via a hydraulic ram. The test bearing employed was a Cooper split-type roller bearing (01B40MEX). The split-type bearing was selected as it allowed defects to be seeded onto the races, furthermore, assembly and disassembly of the bearing was accomplished with minimum disruption to the test sequence, see Fig. 1. Characteristics of the test bearing (Split Cooper, type

Data-acquisition system and signal processing

The transducers employed for vibration and AE data acquisition were placed directly on the housing of the bearing, see Fig. 1. A piezoelectric-type AE sensor (Physical Acoustic Corporation type WD) with an operating frequency range of 100–1000 kHz was employed whilst a resonant-type accelerometer, with a flat frequency response between 10 and 8000 Hz (Model 236 Isobase accelerometer, ‘Endevco Dynamic Instrument Division’) was used for vibration measurement. Pre-amplification of the AE signal was

Experimental procedure

Two test programmes were undertaken.

  • An investigation to ascertain the primary source of AE activity from seeded defects on bearings was undertaken, in addition to determining the relationship between defect size, AE and vibration activity. In an attempt to identify the primary source of AE activity, a surface topography (Form Talysurf 120L; stylus used had a 2 μm radius diamond tip) of the various defects was taken. Furthermore, two types of defect conditions were simulated; firstly, a seeded

Test programme 1: AE source identification and defects of varying severities

Five test conditions of varying severities were simulated on the outer race of the test bearing which was positioned in the load zone; top-dead-centre for this particular test-rig configuration, see Table 1. In addition, the nomenclature used to label all test conditions is detailed in Table 1. The test conditions were:

  • Baseline or defect-free condition in which the bearing was operated with no defect on the outer-race. Fig. 3 shows a visual condition of the race and a surface roughness map

Test programme 2: defects of varying size

For this particular test programme, two experiments (E1 and E2) were carried out to authenticate observations relating AE to varying defect sizes. Each experiment included seven defects of different lengths and widths, see Table 2. A sample defect is shown in Fig. 8. In Experiment 1 (E1), a point defect (D1) was increased in length in three steps (D2–D4) and then increased in width in three more steps (D5–D7). However, in Experiment 2 (E2), a point defect was increased in width and then in

Analysis procedure

If the defects simulated were to produce AE transients, as each rolling element passed the defect, it was envisaged that the AE bursts would be detected at a rate equivalent to the outer race defect frequency (‘4.1X’). In addition, it was also anticipated that the defect frequency would be observed in the vibration frequency range.

For test programme 1 (defects of different severities) AE in time domain and vibrations in frequency domain were analysed. Furthermore, the AE and vibration r.m.s.

Observations of AE time waveform

As already stated the outer race defect frequency was calculated for the various test speeds; 41, 69, 137 and 205 Hz at 600, 1000, 2000 and 3000 rpm, respectively. Typical AE and vibration time waveforms for two different conditions are displayed in Fig. 9, Fig. 10. It was noted that for all defects conditions, other than the smooth defect, AE burst activity was noted at the outer race defect frequency. Observations of AE time waveforms from noise and smooth defect conditions showed random AE

Data analysis: test programme 2

The analysis of this test programme was centred on AE time-domain observations.

Discussion

The source of AE for seeded defects is attributed to material protrusions above the surface roughness of the outer race. This was established as the smooth defect could not be distinguished from the no-defect condition. However, for all other defects where the material protruded above the surface roughness, AE transients associated with the defect frequency were observed. As the defect size increased, AE r.m.s. maximum amplitude and kurtosis values increased, however, observations of

Conclusion

It has been shown that the fundamental source of AE in seeded defect tests was due to material protrusions above the mean surface roughness. Also, AE r.m.s. maximum amplitude and kurtosis have all been shown to be more sensitive to the onset and growth of defects than vibration measurements. A relationship between the AE burst duration and the defect length has been presented.

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