Detection, Prognosis and Decision Support Tool for Offshore Wind Turbine Structures

Vásquez, Sandra; Verhelst, Joachim; Brijder, Robert; Ompusunggu, Agusmian Partogi

doi:10.3390/wind2040039

Cited by 2 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state-parameter distribution is simulated to predict the machine's EoL and RUL. Assuming the noise is both normally distributed, the Kalman filter (KF) is an efficient method to estimate the state of a system given a sequence measurement and times, a mathematical model describing system dynamic, and a model that corresponds to measurement value [35,36]. The KF is optimal for handling the linear transformation of both systems dynamic and measurement models.…”

Section: Prognostics Algorithmmentioning

confidence: 99%

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

2023

Self Cite

View full text Add to dashboard Cite

An aircraft engine is expected to have a high-reliability system as a safety-critical asset. A scheduled maintenance strategy based on statistical calculation has been employed as the current practice to achieve the reliability requirement. Any improvement to this maintenance interval is made after significant reliability issues arise (such as flight delays and high component removals). Several publications and research studies have been conducted related to this issue, one of them involves performing simulations and providing aircraft operation datasets. The recently published NASA CMAPPS datasets have been utilised in this paper since they simulate flight data recording from various measurements. A prognostics model can be developed by analysing these datasets and predicting the engine’s reliability before failure. However, the state-of-the-art prognostics techniques published in the literature using these NASA CMAPPS datasets are mainly purely data-driven. These techniques mainly deal with a “black box” process which does not include uncertainty quantification (UQ). These two factors are barriers to prognostics applications, particularly in the aviation industry. To tackle these issues, this paper aims at developing explainable and transparent algorithms and a software tool to compute the engine health, estimate engine end of life (EoL), and eventually predict its remaining useful life (RUL). The proposed algorithms use hybrid metrics for feature selection, employ logistic regression for health index estimation, and unscented Kalman filter (UKF) to update the prognostics model for predicting the RUL in a recursive fashion. Among the available datasets, dataset 02 is chosen because it has been widely used and is an ideal candidate for result comparison and dataset 03 is employed as a new state-of-the-art. As a result, the proposed algorithms yield 34.5–55.6% better performance in terms of the root mean squared error (RMSE) compared with the previous work. More importantly, the proposed method is transparent and it quantifies the uncertainty during the prediction process.

show abstract

Section: Prognostics Algorithmmentioning

confidence: 99%

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Corrosion prognosis is an essential component within a decision support pipeline where an optimal decommissioning time can be computed depending on the cost of operations and maintenance, energy production, risk aversion, etc. [13].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 3. Output of the corrosion prognosis algorithm based on the power-law corrosion model on (simulated) measurement data, along with the ground truth for reference, see also[13].…”

mentioning

confidence: 99%

Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures

Brijder

Helsen

Ompusunggu

2023

Wind

View full text Add to dashboard Cite

Since manual inspections of offshore wind turbines are costly, there is a need for remote monitoring of their health condition, including health prognostics. In this paper, we focus on corrosion detection and corrosion prognosis since corrosion is a major failure mode of offshore wind turbine structures. In particular, we propose an algorithm for corrosion detection and three algorithms for corrosion prognosis by using Bayesian filtering approaches, and quantitatively compare their accuracy against synthetic datasets having characteristics typical for wall thickness measurements using ultrasound sensors. We found that a corrosion prognosis algorithm based on the Pourbaix corrosion model using unscented Kalman filtering outperforms the algorithms based on a linear corrosion model and the bimodal corrosion model introduced by Melchers.

show abstract

Detection, Prognosis and Decision Support Tool for Offshore Wind Turbine Structures

Cited by 2 publications

References 26 publications

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

Explainable Data-Driven Method Combined with Bayesian Filtering for Remaining Useful Lifetime Prediction of Aircraft Engines Using NASA CMAPSS Datasets

Switching Kalman Filtering-Based Corrosion Detection and Prognostics for Offshore Wind-Turbine Structures

Contact Info

Product

Resources

About