Wind turbine condition monitoring and fault tolerant control

Transcription

1 1 Wind turbine condition monitoring and fault tolerant control PhD : Mahdi Ghane Main Supervisor: Prof. Torgeir Moan Prof. Zhen Gao Prof. Mogens Blanke (DTU)

2 2 Outline Introduction Objectives & Scope Maintenance Condition-based Maintenance & Fault Tolerant Control Failure mode, effects and criticality analysis (FMECA) Gearbox and Bearing Fault Detection and Identification Oil condition monitoring and wear debris analysis Gearbox and Bearing vibration analysis Selection of vibration tool analysis & measurments CUSUM GLRT FFT/ Envelope Analysis Derivation of the joint distribution of amplitude and exceedance duration

3 3 Introduction It is the second part of phase II of Statoil s MIT-NTNU cooperation for offshore wind turbines. NTNU Erin E. Backynski (2010- Michigan Ann A. ) Seongpil Cho (2014- Kalst Uni South Korea) Mahdi Ghane (2015- NTNU)

4 4 Objectives & Scope One of the main objective in any generating asset is to minimize the Levelized cost of electricity (LCOE) costs electrical generated energy

5 5 Expected Growth in Offshore Turbine Size Globally OM is 20-25% of the total floating wind turbine cost

6 6 Increasing reliability of wind turbine Experience from onshore wind farms suggests that unscheduled maintenance costs account for between 25% and 40% of total servicing spend. If the value of the revenue lost while the turbines are down was added, the total would be higher.

7 7 Maintenance Preventive maintenance (Periodic) Reactive maintenance (Run to failure) Predictive maintenance (Condition-Based) (Tchakoua et al., 2014)

8 8 Condition-based Maintenance & Fault Tolerant Control Step 1: Fault Detection [1 st paper] Fault Estimation [2 nd paper] Fault Isolation [3 rd paper] Step 2: Step3: Fault Prognosis Controller adaptation Prevent the reduction of system performance Reducing cost by postponing the equipment replacement until next maintenance schedule.

9 9 Failure mode, effects and criticality analysis (FMECA) It is not possible to consider the WT as a whole system for fault detection. Since we don t have access to real-time wind and waves real value.

10 10 Gearbox and Bearing Fault Detection and Estimation Using FDI techniques in rotating machinery for WTs need adaptation. Based on Residual generation, FDI methods classified into Model-Based Signal-Based Vibration analysis (70%) Oil and debris analysis (20%) NDT (Thermography, ultrasonic and radiographic) Bearing temperatures Blade defects detection Usually last stage of damage

11 11 Oil condition monitoring and wear debris analysis There are several parts which needs lubricant: gearbox, main shaft bearing, yaw bearing, pitch bearing, gearbox bearing The typical practice is to collect a periodic oil sample (generally every 6 months) from a gearbox and send it to a laboratory (offsite or onsite) for analysis. Oil CM relative humidity, oil quality (changes with the level of such contaminants as soot, oxidation products, glycol, and water) temperature. Wear debris analysis. ferrous and nonferrous particles

12 12 There are some new online/inline sensors to count the debris (ferrous or nonferrous) and measure the oil properties (viscosities, contamination, and relative moisture levels) Nowadays some new oil sensors are installed permanently for gearbox oil monitoring The usual trend is to use oil sensors with a vibration method since They are not fast enough for fault detection They are not capable to properly isolate the fault since several parts may have the same material property

13 13 Gearbox and Bearing vibration analysis Time domain method Peak, RMS, Crest Factor, Kurtosis, Skewness, Clearance Factor, Impulse Factor, Shape Factor Hypothesized testing Frequency domain method FT (FFT) PSD Empirical mode decomposition Envelope analysis (Hilbert transform) Shock pulse monitoring Order Analysis (Synchronous sampling) Cepstrum Time/ Frequency domain method STFT Wavelet

14 14 Why do bearings fail? Less than 10% of bearings reach their design lifetime! (SKF) Source of bearing failures Pay more attention to periodic lubrication Using auto refiling lubrication tubes

15 15 1 1

16 16 Selection of vibration tool analysis & measurements Very early stage of degradation Medium stage of degradation Degradation are developed

17 17 Fault detection and estimation of main shaft bearing CUSUM (1 st paper), GLRT (2 nd paper) Parameter Cut-in, rated and cut-out wind speed(m/s) Value Upwind/3 blades Hub height (m) 3, 11.4, 25 Rotor diameter (m) 87.6 Hub diameter (m) 126 Rotor mass (*1000 kg) 3 Nacelle mass (*1000kg) 110 Hub mass (*1000 kg) Wind turbine specification Radial Load Radial and Axial Load Fault: defected bearing The bearing damage is modelled by varying the bearing's deflection in the axial direction. For INP-B, the axial deflection is changed from 2.5e-3 to 2.5e-2 mm for each 1 kn applied force.

18 18 Cumulative SUM : μ ; ; 1 : μ ; ; 1 1 and No prior information about P(H0) and P(H1) Neyman Pearson theorem offer the optimal solution g(k) = max ; ln μ,, ; μ,, : μ ; ; km : μ ; ; k Mk g(k) = ln ; μ,, ; μ,,

19 19 Residual In order to make data (Relative axial acceleration of main shaft) robust to low frequency input disturbances such as wave and wind, axial acceleration was subtracted from nacelle acceleration. db/radian/sample PSD of main shaft axial acceleration Faulty case Fault free case db/radian/sample PSD of Error Residual Faulty case Fault free case radians/sample PSD of main shaft axial acceleration radians/sample PSD of Error Residual

20 20 Distribution fitting 6 5 Fauty data Fault free data 4 Pdf Data Histogram of faulty and fault free data f z i (( 1 ) / 2) z i ;,, 1/2 ( /2)( ) (location) (scale (shape parameter) parameter) Faulty case e Fault free case e t-distribution parameters for faulty and fault free cases

21 21 Probability of Detection and Failure in M step size k-n+1 j k k f z i ;,, g(k) = max ln max, i j k G k j k-n+1 j k f z i ; 0, 0, i j 0 M=300 x pdf1 data fit 2 pdf2 data fit 11 5 Density Data

22 22 Pdf1: Distribution: Lognormal ln x 2 1 f x, exp ; x 0. 2 x 2 2 ˆ ˆ Pdf2: Distribution: Weibull Probability pdf1 data fit 2 pdf2 data fit 11 b1 b bx x f x a, b exp aa a aˆ bˆ Data 6 x 10-3 Threshold Probability of Detection Probability of Failure

23 23 GLRT (Unknown change magnitude, 2 nd paper) Type of the distribution is assumed to be known but its parameters are unknown and must be estimated. ISO classifies four vibration zone boundaries, based on the operational class of machineries: Zone A: new machines Zone B: acceptable zone for long-term operation Zone C: unacceptable for long-term operation Zone D: can cause severe damage

24 24 Physical meaning of different fault cases using ISO Effect of main shaft bearing fault on gearbox components - PLC-B life

25 25 K-S goodness-of-fit test using bootstraping technique

26 26 Parameter estimation of t-distribution for unknown change MLE estimator k f z i ;,, g(k) = max ln max, max ln j i j k k Gj k 1 k-n+1 j k k-n+1 j k k-n+1 j k f z i ; 0, 0, i j 0 k ln 1 j0 1 ˆ 1& ˆ 1 k ln 1 j0 1 1 k j1 k k ln 1 j i j zi z 1 i 1 k k ln 1 j... k j1 ln i j z i x dx x d log x d x / dx digamma x

27 Moment estimator mean : E( l) Variance V l E l 2 2 : () (( )), 2 6 excess kurtosis :, ˆ ME 4 k 1 zi 4 ( ) 3 k j1 i j s k 1 2 ˆ 2 ˆ ME zi * k j1 i j ˆ Higher order statistical moments are highly sensitive, since they are based on the tail estimation. Therefore, we need large data size to get a robust estimate. By trial and error, samples (with fs=200hz, 60 sec ) are enough for moment estimator. However, for smaller sample size MLE estimator is far better than Moment estimator.

28 28 FC4 Decision function for M4 = (50 s) using mle estimators FC1 Decision function for M1 = (50 s) using moment estimators Test statistics g(k) for FC0, FC1 and FC4 for window size1 equal to 50 sec (10000 samples) Test statistics g(k) for FC0, FC1 and FC4 for window size2 equal to 250 sec (50000 samples)

29 29 Detector performance Estimated t-distribution parameters for FC0, FC1 and FC4

30 30 Frequency domain analysis FFT and Envelope analysis Welch method (non-parametric, direct method) is used to calculate Power Spectral Density for different measurement signals. 2fm1st-carrier fm2nd-planet fm2nd-carrier PSD for faults with different severity (Relative axial acceleration)

31 31 Fault in IMS-B Not possible to detect it using PSD or FFT Envelope analysis using Hilbert transfrom 1 xt ( ) H[x(t)] p.v. d t fm2nd-planet 3fm1st-carrier fm1st-sun

32 32 On the joint distribution of excursion duration and amplitude of a narrow-band Gaussian process Distribution of maximas (crest amplitude; p(ac)) The first milestone dates back to the middle of the last century with the pioneering works of Rice; the average crossing rate under favorable conditions (Rice formula) Distribution of wave period (p(t) or p(tc)) Wave period can be defined as the time between two successive local maxima or successive up-crossing of the mean level. Rice (1944) Longuet-Higgins ( )

33 33 Joint distribution of wave period and amplitude (p(t, Ac)) The joint distribution of wave periods and amplitudes is a more challenging problem in the most general case. However, for stochastic process with narrow-band spectrum some simplification can be made. Wooding (1955) for the first time derived the joint distribution of wave amplitude and period for a narrow-band spectrum using the density of the wave envelope and its time derivative. Cavanie et al. (1976) proposed a closed-form expression for the joint distribution assuming narrow-band Gaussian random process based on spectral moments up to 4th spectral moment (m0;m1;m2;m4). Longuet-Higgens (1963) ended up with a diagonal covariance matrix between slow varying envelope, highfrequency component and their time derivatives, and derived a simpler closed-form expression for the joint distribution of wave periods and amplitudes based on spectral moments (m0;m1;m2). The distribution of excursion duration ( p(th)) Time between a level-up-crossing followed by a level-down-crossing The density of excursion period was first derived by Rice (1945); it was, however, very complicated for non-zero levels and only can be estimated by means of demanding numerical integrations. By partitioning an n-dimensional normal space to subspaces, Wu-Zhou and Ming-shun (2001) proposed a semi-analytic method to reduce the computational efforts. Analytical expressions for mean value and variance of excursion duration can be found in Nigam (1963) and Mathiesen (1994), where Mathiesen used the mean value expression to validate the empirical parametric model. There are also some efforts mostly for asymptotic large level excursion based on some assumptions on autocorrelation function.

34 34 The distribution of exceedance (excursion) duration and amplitude ( p(ac, Th)) Closed-form expression for the joint distribution of exceedance duration and amplitude above a certain level (both for non-asymptotic and asymptotic levels)based on spectral moments (m0;m1;m2). Why is it important? Slamming loads, deck impact events, short-term probability distribution of impact duration, bottom-slam forces on deck Long-term application such as operation, installation and survival of oil and gas industry and offshore renewable energy. Detection delay Fading phenomenon in wireless communication

35 35 Assumptions A Gaussian random process The amplitude an is Rayleigh distributed. The phase is uniformly distributed over [0; 2). for a narrow-band process is densely distributed over 0,. Narrow-band assumption helps to break down into a carrier high frequency wave with the mean frequency, and a slowly varying envelope function. Mean frequency is defined as

36 36 Longuet-Higgins ( ) derived the closed-form joint distribution for a narrow-band Gaussian process which has a continuous first derivative: Where Spectral width parameter Period and Amplitude are normalized Normalization factor is defined as:

37 37 Derivation of the joint distribution of exceedance duration and amplitude Starting from Longuet-Higgins final joint distribution of amplitude and period Relation between exceedance duration and crest amplitude and period for a given level h

38 38 In order to obtain a simpler form:

39 39 Finally, the proposed joint distribution of amplitude and exceedance duration Where K(H, v) is a normalization factor to guarantee the unity of the integral:

40 40 Simulation of the joint distribution of amplitude and exceedance duration Contours of p(ac; TH) for v = 0:5 and for different normalized level H, where p takes the values [0.1, 0.3, 0.5, 0.7, 0.9, 0.99].

41 41 The marginal distribution of p(ac)

42 42 The marginal distribution of p(th) Using numerical integration, v = 0.5

43 43 Normalization Factor For a random process like and given a threshold H, expected value of the excursion period E[TH], as an alternative approach, can be presented as:

44 44 Comparison with ideal NB Gaussian process and real sea states

45 45

46 46 Comparison with real sea states

47 47

48 48 Kolmogorov-Smirnov Goodness of fit test One of the most valuable features of the K-S two-sided test statistic is that for significance level, its critical value may be used to form a confidence band for the true unknown distribution function,

49 49 K-S test statistics for Ideal narrow-band Gaussian process for different v and H values

50 50 K-S test statistics for different sea states and H values

51 51 Thank you for your attention!