Bearing datasets

The bearing datasets were measured during a joint experiment with The Leonardo Centre for Tribology at the University of Sheffield that is partici- pating in the Ricardo ‘Multilife’ wind energy bearing project. This project is aiming to increase service life of the ‘Multilife’ rolling element bearing by up to 500%. It is funded by the UK northern wind innovation programme (NWIP).

The ‘M ultiLif eT M_{’ mechanism is a concept developed by engineers at Ri-} cardo UK Ltd, and aims to increase service life of wind turbine planetary stage gearbox bearings by up to 500 percent. The plan of the acoustic emis- sion experiments is the following: the nominally stationary inner raceway is periodically rotated by a defined angular displacement so as to move ‘fresh’ inner raceway material into the loaded zone of the bearing, thus distributing damage around the entire raceway circumference.

The test rig used for this work houses N U 2244 type cylindrical roller bear- ings, typical of the size utilized in the planetary stage of 10 MW wind turbine gearboxes. The rig is designed to ensure a comparable mode of operation to reality; such that the bearings are rotated via their outer raceways, at a

rotational speed similar to that expected of the planetary stage bearings in service (100 RPM) and a one directional radial load is applied via a hy- draulic ram applying load through tension arms mounted to a non-rotating inner shaft. The bearing outer race is belt driven and the test bearing sits within a larger spherical roller bearing to permit this rotation.

The Leonardo Centre for Tribology at the University of Sheffield is partic- ipating in this project with the main research purpose, among others, to study and detect fatigue crack propagation in the inner race of the ‘Multil- ife’ rolling element bearing using acoustic emission and ultrasound measure- ments, mostly. For this reason, the test rig is currently located at one of the labs of the Department of Mechanical Engineering of the university. So participating in the experiments of the rig was of great interest.

The rig, that includes a stationary shaft, two pulley wheels and a hydraulic ram for the implementation of loads, a rig bearing and the test bearing is shown in Figure 4.4 (a). Figure 4.4 (b) shows the outer race and 16 the rollers of the rig. A schematic figure of the bearing is also given in Figure

4.5.

(a) (b)

Figure 4.4: (a) Ricardo ’Multilife’ rolling element bearing (b) and its rolling elements.

The experimental datasets used further on in this thesis were taken on two different dates: 26/11/2012 and 28/11/2012. They are acceleration measure- ments obtained from a PCB piezotronics triaxial accelerometer, with model number 356B21. The accelerometer was placed on the top of the bearing housing. Here only the x-axis datasets will be analysed. The sampling fre- quency was 10000 Hz. In addition, during the experiment, for both datasets, a radial load of 1000 kN was applied to the test bearing. On the first date the

Figure 4.5: Ricardo bearing schematic.

rig was run under healthy conditions (no damage was introduced). On the second date however (28/11/2012), damage was introduced to the rig using the electrical discharge machining method (EDM), otherwise known as wire erosion. A notch, 0.2 mm deep and almost 0.2 mm wide, was introduced at the inner race of the bearing at the point of maximum load (bottom-dead- centre), as depicted in Figure 4.6, and the rig was run up to failure (around 300000 rotations).

The speed of the shaft was around 110 RPM for the first dataset and 100 RPM for the second dataset. Because the measurements were captured dur- ing a fatigue experiment the rotational speeds used were low and not the same for each dataset. When the rotational speed is low the changes on one revolution of the shaft can be influential and create difficulties in damage de- tection. The main bearings of wind turbine drivetrains operate under quite low speeds that vary over time. This is basically one of the main reasons why there is often not great diagnostic information in the raw spectrum of these signals, since smearing effects in the spectra could mask the repetition frequencies related to damage. In this case, the rotational speeds during the experiment were slowly increasing during the experiment.

Generally, if the spectrum of the vibration of a healthy bearing contains any information at all, then it is information related to the shaft rotation

Figure 4.6: EDM notch introduced at the inner race.

speed and its harmonics, which is shown as Zone I in Figure 4.7. Any other frequencies might indicate noise, or frequencies related to other rotating machinery operating at the same time with the bearing examined. A rolling element bearing fault could appear at the inner, the outer race and/or on the rolling elements. During its early stages the damage on the surface is most of the time only local, e.g. pits or spalls. The vibration signal in this case includes repetitive impacts of the moving components on the defect. These impacts could create “repetition” frequencies that depend on whether the defect is on the inner or the outer race, or on the rolling element [101]. Apart from Zone I in this case, Zones II, III and/or IV might appear in the frequency spectrum of the vibration (Figure4.7). Most of the times, only the vibration spectra of bearings with early faults contain information of damage, since with time these faults can eventually be smoothed and not give as sharp impulses. So for early faults, the repetition impulses might create initially an increase of frequencies in the ultrasonic frequency range, Zone IV, and maybe excite the resonant frequencies of the bearing parts later on, Zone III, as well as the repetition frequencies of Zone II (BSF,BPFO, BPFI) [39]. It has been observed in previous studies though that, many times the vibration of a damaged bearing might not carry the desired information, and that in

Zone I Zone II Zone III Zone IV 1 × R P M 2 × R P M 3 × R P M B S F B P F O B P F I _{Bearing natural} resonances Bearing f ault f requencies

High f requencies (spike energy region)

M

ag

nitude

F requency

Figure 4.7: Frequency content of a signal of a damaged bearing.

this case envelope analysis might be of more use for damage detection. The test rig did not include other components, like gears, that could generate additional frequencies in the spectrum and generally it was observed that no significant amount of noise was in the vibration signals, so bandpass filtering in this particular case was not important. The acceleration diagrams for both datasets are given in Figure 4.8 and their frequency spectra are given in Figure 4.9.

The frequency spectrum of the first dataset is flat while the spectrum of the second dataset has more than 10 or so harmonics in the frequency bands of 6000 − 7000 Hz and 2000 − 4000 Hz as well as a peak at the frequency of 50 Hz and two harmonics at 100 and 150 Hz. The figures show normalised values of the frequency which were obtained by dividing the actual values with the sampling frequency. The harmonics of the higher frequency ranges are all separated by 50 Hz each. One could admit that this is a very apparent indication of damage to the rig even using this simple approach.

0 1000 2000 3000 4000 5000 −10 −5 0 5 10

Sample point number

Acceleration [g]

(a)

Sample point number

Acceleration [g]

(b)

Figure 4.8: Time history of the acceleration: (a) dataset 26/11/2012 and (b) dataset 28/11/2012.

0 0.2 0.4 0.6 0.8 1 0 0.005 0.01 0.015 0.02 0.025

Frequency [normalised]

|Y(f)|

(a)

Frequency [normalised]

|Y(f)|

(b)

Figure 4.9: Frequency spectrum of the acceleration: (a) dataset 26/11/2012 and (b) dataset 28/11/2012.