Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Effect of Alcohol Extract of Zataria multiflora (Boiss), Satureja bachtiarica (Bunge) and Zaravschanica membranacea (Boiss) on Immuno-Hematologic Factors in Rats

Purpose: To determine the effect alcohol extract of Zataria multiflora, Satureja bachtiarica and Zaravschanica membranacea on immunohematologic factors in Wistar rats.Methods: Wistar rats were randomly allocated to seven treatment groups which consisted of control group with water and feed only (1); 200 mg. kg-1 Z. membranacea (2); 400 mg. kg-1 Z. membranacea (3); 200 mg. kg-1 S. bachtiarica (4); 400 mg. kg-1 S. bachtiarica (5); 200 mg. kg-1 Z. multiflora (6) and 400 mg. kg-1 Z. multiflora (7). Erythrocyte counts (RBC), packed cell volumes (PCV), haemoglobin (Hb) concentration, mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), Translocation and Assembly Module (Tam) protein, IgM and albumin were measured after 29 days.Results: Z. membranacea at 200 mg kg-1 showed the highest level of Tam-protein content (p < 0.05). Z. multiflora boiss at 400 mg kg-1 produced higher levels of immunoglobulin M (IgM) compared to S. bachtiarica and Z. multiflora (p < 0.05). Both Z. membranacea and S. bachtiarica at 200 mg. kg-1 caused a significant increase in albumin levels in the rats (p < 0.05). Z. multiflora at 400 mg. kg-1 had the highest effect on white blood cells (WBC) while S. bachtiarica produced the highest effect on neutrophils (Nut) (p < 0.05). Z. membranacea and Z. multiflora at 200 mg. kg-1 showed significantly higher levels of monocytes (Mon) % (p < 0.05). Z. multiflora and S. bachtiarica at 400 mg. kg-1 showed a significant effect on phagocytosis % (p < 0.05) whilst S. bachtiarica at 400 mg.kg-1 had a significant effect on phagocytosis number (p < 0.05).Conclusion: The alcohol extracts of Z. multiflora Z. membranacea and S. bachtiarica extracts are capable of stimulating the immune defense mechanism without causing undesirable effects on hematological parameters.Keywords: Zataria multiflora, Satureja bachtiarica, Zaravschanica membranacea, Immunoglobulin, Serum albumen, Immunohematologic factor

An Integrated Feature-Based Failure Prognosis Method for Wind Turbine Bearings

In North America, many utility-scale turbines are approaching the half-way point of their anticipated initial lifespan. Accurate remaining useful life (RUL) estimation can provide wind farm owners insight into how to optimize the life and value of their farm assets. An improved understanding of the RUL of turbine components is particularly essential as many owners consider retiring, life-extending, or repowering their farms. In this article, an integrated prognosis method based on signal processing and an adaptive Bayesian algorithm is proposed to predict the RUL of various faulty bearings in wind turbines. The signal processing leverages feature extraction, feature selection, and signal denoising to detect the dynamics of various failures. Then, RUL of the faulty bearings is forecast via the adaptive Bayesian algorithm using the extracted features. Finally, a new fusion method based on an ordered weighted averaging (OWA) operator is applied to the RUL obtained from the features to improve accuracy. The efficacy of the method is evaluated using data from historical failures across three different Canadian wind farms. Experimental test results indicate that the OWA operator delivers a higher accuracy for RUL prediction compared to the other feature-based methods and Choquet integral fusion technique

A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

This paper introduces a new condition monitoring approach for extracting fault signatures in wind turbine blades by utilizing the data from a real-time Supervisory Control and Data Acquisition (SCADA) system. A hybrid fault detection system based on a combination of Generalized Regression Neural Network Ensemble for Single Imputation (GRNN-ESI) algorithm, Principal Component Analysis (PCA), and wavelet-based Probability Density Function (PDF) approach is proposed in this work. The proposed fault detection strategy accurately detects incipient blade failures and leads to improved maintenance cost and availability of the system. Experimental test results based on data from a wind farm in southwestern Ontario, Canada, illustrate the effectiveness and high accuracy of the proposed monitoring approach

Aggregate reliability analysis of wind turbine generators

In North America, many utility-scale turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non-parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records

Condition Monitoring and Failure prognostic of Wind Turbine Blades

Condition Monitoring (CM) has become an essential tool in complex engineering systems like wind turbines. They can prevent unexpected failures and contribute to a more reliable system. Information attained from monitoring can be employed for maintenance scheduling, hence, minimizing maintenance costs. Remaining Useful Life (RUL) is a critical aspect of CM. This paper introduces a new RUL prediction method for wind turbine blades using a novel fuzzy-based failure dynamic modeling via a Supervisory Control and Data Acquisition (SCADA) system. For this goal, a recursive Principal Component Analysis (PCA) is employed to compress the SCADA data and extract real-time Principal Components (PCs). Next, a wavelet-based Probability Density Function (PDF) is applied to obtain the probability of staying healthy from the extracted PCs. It is anticipated that blade degradation will lead to a subsequent decline in the PDF curve. A failure trajectory is then captured by transforming the PDF into the PC\u27s surface. Subsequently, the T-S fuzzy system is utilized to form the mathematical model of degradation from this failure trajectory. Next, a Bayesian algorithm is adaptively administered to predict the RUL. Experimental test results on Canadian wind farms explain a high performance of the proposed failure prognosis method in comparison with a Bayesian algorithm

Improved Remaining Useful Life Estimation of Wind Turbine Drivetrain Bearings under Varying Operating Conditions

The failure progression of wind turbine bearings comprises of multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on the failure dynamics severity is an essential task for bearing failure prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) and vibration signals to predict remaining useful life (RUL) for wind turbine bearings. The SCADA data are utilized to define the role of environmental conditions such as wind speed and ambient temperature in bearing failure dynamics. Afterward, for each environmental condition, failure dynamics are identified by the vibration signal. Finally, RUL of the faulty bearings is forecast via an adaptive Bayesian algorithm using the failure dynamics, conditional to the VOC. The efficacy of the method is validated using experimental data, and test results indicate a higher RUL accuracy compared to the Bayesian algorithm

Critical wind turbine components prognostics: a comprehensive review

As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.Natural Sciences and Engineering Research Council of Canada (NSERC) 860002 Ontario Centres of Excellence (OCE) Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1170044 Advanced Center for Electrical and Electronic Engineering (AC3E) under Basal Project FB000

New Condition-Based Monitoring and Fusion Approaches With a Bounded Uncertainty for Bearing Lifetime Prediction

Condition Monitoring (CM) is an essential element of securing reliable operating conditions of Wind Turbines (WT) in a wind farm. CM helps optimize maintenance by providing Remaining Useful Life (RUL) forecast. However, the expected RUL is not often reliable due to uncertainty associated with the prediction horizon. In this paper, we employ high-level fusion methods to expect the RUL of WT bearings. For this purpose, various features are extracted by vibration signals to capture deterioration paths. Then, a Bayesian algorithm is utilized to determine RUL for each selected feature. Eventually, high-level fusion schemes, including Hurwicz, Choquet integral, Ordered Weighted Averaging operator, are employed to integrate RUL numbers and lessen associated uncertainty in the prediction horizons. Besides, a pessimistic fusion strategy is driven to obtain a bounded uncertainty for the worst RUL prediction. The fusion methods are assessed by ten-year vibration signals of Canadian wind farms. Experimental results confirm accurate results with bounded uncertainty for high-level fusion approaches