Rotor Sideband Power Factor (SBPF rotor ) Algorithm

Similarly to the previous case, based on the Durham WTCMTR experimental

results shown in Section 5.4.2, the 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 detection sensitivity to generator

rotor electrical asymmetry, %𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟}, has been calculated as

%𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟} =(𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟)𝑓− (𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟)ℎ

(𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟})_ℎ × 100 (5.12)

where (𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟})_ℎ and (𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟})_𝑓 are the healthy and faulty 𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟}

values, respectively, under similar operating conditions. The results are shown in Figure 5.18.

Figure 5.18: 𝑆𝐵𝑃𝐹_{𝑟𝑜𝑡𝑜𝑟} detection sensitivity to 21% and 43% rotor asymmetry

conditions for the experimentally investigated WTCMTR power loads.

As for balanced rotor the 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 magnitude does not vary

significantly with the load, Figure 5.12, %𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 varies almost

0 1000 2000 3000 4000 5000 6000 0 10 20 30 40 50 60 70 80 90 100 %SB PF rot or [%] Power [%] 21% Rotor Asymmetry 43% Rotor Asymmetry

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exponentially with the load for both rotor asymmetry seeded-fault datasets.

The sensitivity analysis shows that 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 proves successful in the

detection of both low and high stages of rotor fault level, with average detection sensitivities of 743% and 1897%, respectively. Under similar load conditions, the sensitivity function increases with the fault severity showing

the 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 ability to clear discriminate the fault levels and to provide an

early fault detection.

5.7.3.

Harmonic Power Factor (HPF) Algorithm

Based on the experimental results from the Manchester test rig presented in Section 5.6, the 𝐻𝑃𝐹 detection sensitivity to generator bearing outer race fault, %𝐻𝑃𝐹, has been calculated as

%𝐻𝑃𝐹 =𝐻𝑃𝐹𝑓− 𝐻𝑃𝐹ℎ

𝐻𝑃𝐹_ℎ × 100 (5.13)

where 𝐻𝑃𝐹ℎ and 𝐻𝑃𝐹𝑓 are the healthy and faulty 𝐻𝑃𝐹 values, respectively,

under similar steady-state operating speeds conditions. Table 5.1 and Table 5.2 summarise the %𝐻𝑃𝐹 values for the generator bearing fault conditions investigated in this research and for the data collected by the vertical and the horizontal accelerometers, respectively. As expected, the average 𝐻𝑃𝐹 detection sensitivity, %𝐻𝑃𝐹, increases with the fault level. The vertical accelerometer data provides higher detection sensitivity than the horizontal accelerometer for the lowest bearing fault severity investigated, i.e. 3 mm hole. Comparable %𝐻𝑃𝐹 magnitudes are obtained for the intermediate fault condition from both accelerometers, while the horizontal accelerometer data exhibits the highest %𝐻𝑃𝐹 magnitude for the last stage of bearing fault investigated. In both cases, the results show the 𝐻𝑃𝐹 algorithm ability to clearly discriminate the fault severity regardless of the generator speed.

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The successful application of the 𝐻𝑃𝐹 algorithm to vibration signals from the two independent accelerometers strengthens the confidence in its fault detection and diagnosis capability, eventually reducing false alarms.

5.8. Summary

This chapter presents the experimental work conducted on two WT small- scale test rigs. The test rigs have been operated at constant and wind-like variable speed conditions and a number of seeded-faults have been applied and detected.

Seeded-fault tests have been performed by the Author on the Durham WTCMTR to investigate gearbox HS pinion tooth damage, through vibration signature analysis, and generator rotor electrical asymmetry, through

sensitivity for the vertical accelerometer dataset. Generator Speed

(rev/min)

Bearing Fault Severity

3 mm hole 6 mm hole 12 mm hole

1530 197% 4675% 18735%

1560 177% 2708% 12953%

1590 217% 1448% 15855%

Average %𝐻𝑃𝐹 197% 2944% 15848%

Table 5.2: Generator drive-end side bearing damage average 𝐻𝑃𝐹 detection sensitivity for the horizontal accelerometer dataset.

Generator Speed (rev/min)

Bearing Fault Severity

3 mm hole 6 mm hole 12 mm hole

1530 86% 4050% 48456%

1560 63% 1872% 27188%

1590 54% 2810% 35284%

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electrical and vibration signature analysis. Further experimental data, relative to the investigation of rotor electrical asymmetry and generator bearing faults, through vibration signature analysis, has been provided to the Author for analysis and processing from the University of Manchester.

In each case the relevant fault frequencies of interest, introduced throughout Chapter 3, have been investigated by using conventional FFT spectra provided by commercial WT CMSs. Identifiable fault frequencies have been extracted from the rig signatures and used to reflect the health of the component. Three novel algorithms have been then designed to track the observed fault component total power for automatic WT damage detection

and diagnosis: the 𝑆𝐵𝑃𝐹𝑔𝑒𝑎𝑟, the 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 and the 𝐻𝑃𝐹 algorithms. The main

aim of these algorithms is to automatically analyse and interpret the large volumes of CM data usually produced by a CMS in a WF, significantly reducing the large degree of manual analysis currently required.

For gearbox HS pinion tooth damage, the proposed 𝑆𝐵𝑃𝐹_{𝑔𝑒𝑎𝑟} algorithm

has proved to be a reliable gear health indicator, successfully allowing the assessment of the fault severity by tracking the progressive tooth damage introduced on the WTCMTR during variable speed and load conditions. The proposed algorithm has successfully detected both early and final stages of tooth damage, showing a higher effectiveness at the percentage loads above 20% and average detection sensitivity of 100% and 320%, respectively.

For generator rotor asymmetry detection through electrical signature analysis, the experimental results have demonstrated the benefits of tracking

the 𝑆𝐵𝑃𝐹𝑟𝑜𝑡𝑜𝑟 values as the speeds and loads vary. The algorithm has proved

capable of enabling clear fault detection, for both early and advanced stages of rotor fault, with average detection sensitivity of 743% and 1897%, respectively. In the case of the Durham WTCMTR, the analysis of generator vibration signature was shown to enable rotor asymmetry detection via the

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observed changes in magnitude of the |2𝑠|𝑓𝑠 upper SB of the supply frequency

second harmonic.

Finally, the 𝐻𝑃𝐹 algorithm has proved to be a reliable indicator of the outer race bearing faults investigated during the seeded-fault tests. The proposed algorithm worked successfully achieving clear fault detection, from early to more severe levels of bearing fault, under the steady-state conditions investigated during the tests, with large average detection sensitivities. The work presented in this Thesis clearly demonstrates the potential of developing WT real-time tracking applications based on the 𝐻𝑃𝐹 algorithm.

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SBPFgear Case Study: Validation against

NREL 750kW Gearbox

6.1. Introduction

Based on the experimental evidence from the Durham WTCMTR, a novel

fault detection algorithm, the Gear Sideband Power Factor, 𝑆𝐵𝑃𝐹𝑔𝑒𝑎𝑟,

specifically developed to aid in the detection of WT gear tooth damage has been presented in Section 5.4.1. By tracking the overall power of the FFT

spectra associated with the 2𝑥𝑓𝑚𝑒𝑠ℎ,𝐻𝑆 SB frequency window, 𝑆𝐵𝑃𝐹𝑔𝑒𝑎𝑟 proved

effective in detecting the presence of the gear damage introduced into the 30 kW WTCMTR gearbox, that is, damage location, and in identifying the precise damaged gear, that is, damage diagnosis. In the healthy gear mesh, SBs had small amplitude compared with the centre mesh frequency or were

missing resulting in low 𝑆𝐵𝑃𝐹_{𝑔𝑒𝑎𝑟} values. As damage developed on the pinion

tooth passing through the gear mesh, the SBs increased in amplitude, as well

as in number, as did 𝑆𝐵𝑃𝐹_{𝑔𝑒𝑎𝑟}. The proposed 𝑆𝐵𝑃𝐹_{𝑔𝑒𝑎𝑟} method successfully

allowed the assessment of the gear fault severity on the small-scale test rig by tracking the progressive tooth damage, from the early stages of development, during the variable wind-like speed and load conditions.

In this chapter, to validate the performance and the reliability of the

proposed 𝑆𝐵𝑃𝐹𝑔𝑒𝑎𝑟 technique on a full-size gearbox, the algorithm has been

tested on data from the National Renewable Energy Laboratory’s (NREL) wind turbine Gearbox Condition Monitoring Round project (Sheng, 2012). These vibration signals were collected from a real WT gearbox that had sustained gear damage during its field test.

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