Diagnostic monitoring of drivetrain in a 5 MW spar-type floating wind turbine using Hilbert spectral analysis

The objective of this paper is to investigate the frequency-based fault detection of a 5MW spar-type floating wind turbine (WT) gearbox using measurements of the global responses. It is extremely costly to seed managed defects in a real WT gearbox to investigate different fault detection and condition monitoring approaches; using analytical tools, therefore, is one of the promising approaches in this regard. In this study, forces and moments on the main shaft are obtained from the global response analysis using an aero-hydro-servo-elastic code, SIMO-RIFLEX-AeroDyn. Then, they are utilized as inputs to a high-fidelity gearbox model developed using a multi-body simulation software (SIMPACK). The main shaft bearing is one of the critical components since it protects gearbox from axial and radial loads. Six different fault cases with different severity in this bearing are investigated using power spectral density (PSD) of relative axial acceleration of the bearing and nacelle. It is shown that in severe degradation of this bearing the first stage dynamic of the gearbox is dominant in the main shaft vibration signal. Inside the gearbox, the bearings on the high speed side are those often with high probability of failure, thus, one fault case in IMS-B bearing was also considered. Based on the earlier studies, the angular velocity error function is considered as residual for this fault. The Hilbert transform is used to determine the envelope of this residual. Information on the amplitude of this residual properly indicates damage in this bearing

Protonation State-Dependent Communication in Cytochrome c Oxidase

Proton transfer in cytochrome c oxidase from the cellular inside to the binuclear redox center (BNC) can occur through two distinct pathways, the D- and K-channels. For the protein to function as both a redox enzyme and a proton pump, proton transfer into the protein toward the BNC or toward a proton loading site (and ultimately through the membrane) must be highly regulated. The PR → F transition is the first step in a catalytic cycle that requires proton transfer from the bulk at the N-side to the BNC. Molecular dynamics simulations of the PR → F intermediate of this transition, with 16 different combinations of protonation states of key residues in the D- and K-channel, show the impact of the K-channel on the D-channel to be protonation-state dependent. Strength as well as means of communication, correlations in positions, or communication along the hydrogen-bonded network depends on the protonation state of the K-channel residue K362. The conformational and hydrogen-bond dynamics of the D-channel residue N139 is regulated by an interplay of protonation in the D-channel and K362. N139 thus assumes a gating function by which proton passage through the D-channel toward E286 is likely facilitated for states with protonated K362 and unprotonated E286. In contrast, proton passage through the D-channel is hindered by N139’s preference for a closed conformation in situations with protonated E286

Condition monitoring of spar-type floating wind turbine drivetrain using statistical fault diagnosis

Operation and maintenance costs are significant for large‐scale wind turbines and particularly so for offshore. A well‐organized operation and maintenance strategy is vital to ensure the reliability, availability, and cost‐effectiveness of a system. The ability to detect, isolate, estimate, and perform prognoses on component degradation could become essential to reduce unplanned maintenance and downtime. Failures in gearbox components are in focus since they account for a large share of wind turbine downtime. This study considers detection and estimation of wear in the downwind main‐shaft bearing of a 5‐MW spar‐type floating turbine. Using a high‐fidelity gearbox model, we show how the downwind main bearing and nacelle axial accelerations can be used to evaluate the condition of the bearing. The paper shows how relative acceleration can be evaluated using statistical change‐detection methods to perform a reliable estimation of wear of the bearing. It is shown in the paper that the amplitude distribution of the residual accelerations follows a t‐distribution and a change‐detection test is designed for the specific changes we observe when the main bearing becomes worn. The generalized likelihood ratio test is extended to fit the particular distribution encountered in this problem, and closed‐form expressions are derived for shape and scale parameter estimation, which are indicators for wear and extent of wear in the bearing. The results in this paper show how the proposed approach can detect and estimate wear in the bearing according to desired probabilities of detection and false alarm

On the Joint Distribution of Excursion Duration and Amplitude of a Narrow-Band Gaussian Process

The probability density of crest amplitude and duration that exceeds a given level is used in many theoretical and practical problems in engineering that are subjected to fluctuating loads such as wind and wave loads. The presently available joint distributions of amplitude and period are limited to excursion through a mean-level or to describe the asymptotic behavior of high level excursions. This paper extends the knowledge by presenting a theoretical derivation of probability of wave exceedance amplitude and duration for a stationary narrow-band Gaussian process. A density function is suggested that has the salient feature to depend only on the three lowest spectral moments m0, m1, and m2 and desired level of exceedance, H. It does not require any condition on the autocorrelation function. This paper shows how increase in H, increases the correlation between excursion periods and amplitude. This paper also shows that how the accuracy of the proposed joint distribution relates to spectral width parameter, ν, and that accuracy increases for higher levels of H, especially for a spectrum describing a physical phenomenon such as a sea state spectrum. It was demonstrated that the marginal distribution of amplitude is Rayleigh distributed, as expected, and that the marginal distribution of excursion duration works for asymptotic and non-asymptotic levels. Results demonstrate that the established distribution fits well with ideal narrow-band Gaussian processes as well as the sea states at three European sites —in the Atlantic Ocean and the North Sea. The suggested model is found to be a good replacement for the existing empirical distributions

Condition monitoring of spar‐type floating wind turbine drivetrain using statistical fault diagnosis

Operation and maintenance costs are significant for large‐scale wind turbines and particularly so for offshore. A well‐organized operation and maintenance strategy is vital to ensure the reliability, availability, and cost‐effectiveness of a system. The ability to detect, isolate, estimate, and perform prognoses on component degradation could become essential to reduce unplanned maintenance and downtime. Failures in gearbox components are in focus since they account for a large share of wind turbine downtime. This study considers detection and estimation of wear in the downwind main‐shaft bearing of a 5‐MW spar‐type floating turbine. Using a high‐fidelity gearbox model, we show how the downwind main bearing and nacelle axial accelerations can be used to evaluate the condition of the bearing. The paper shows how relative acceleration can be evaluated using statistical change‐detection methods to perform a reliable estimation of wear of the bearing. It is shown in the paper that the amplitude distribution of the residual accelerations follows a t‐distribution and a change‐detection test is designed for the specific changes we observe when the main bearing becomes worn. The generalized likelihood ratio test is extended to fit the particular distribution encountered in this problem, and closed‐form expressions are derived for shape and scale parameter estimation, which are indicators for wear and extent of wear in the bearing. The results in this paper show how the proposed approach can detect and estimate wear in the bearing according to desired probabilities of detection and false alarm

Statistical fault diagnosis of wind turbine drivetrain applied to a 5MW floating wind turbine

Deployment of larger scale wind turbine systems, particularly offshore wind turbine, requires more organized operation and maintenance strategies to make it as competitive as the classical electric power stations. It is important to ensure systems are safe, profitable and cost-effective. In this regards, the ability to detect, isolate, estimate faults play an important role. One of the critical wind turbine components is the gearbox. Failures in the gearbox are costly both due to the cost of the gearbox itself, but also due to high repair downtime. In order to detect faults as fast as possible to prevent them to develop into failure, statistical change detection is used. In this paper, Cumulative Sum method (CUSUM) is used to diagnose fault in downwind main bearing in a high fidelity gearbox model of a 5-MW spar-type wind turbine. Residuals are found to be non-Gaussian following a t-distribution with multivariable characteristic parameters. Results show CUSUM method could detect change and estimate change time very agile with desired false alarm and detection probabilitie