Nejra Beganovic

Application of diagnosis and prognosis to wind turbine system based on fatigue load

Fatigue damage in wind turbine structures is mainly induced by fluctuating loads strongly affecting the structural response of the system. The examination of fatigue damage, classification of the system state, prediction of remaining lifetime as well as the extension of maintenance interval become a challenge in structural health monitoring of wind turbine systems mainly due to offshore application. This contribution focuses on the structural load analysis in terms of maintenance intervals as well as service lifetime extensions. To postpone the point in time at which the system becomes nonfunctional, the structural load has to be mitigated while the energy production is retained as close as possible to the desired value. As fatigue damage is strongly influenced by the inflow parameters, a suitable control strategy is adopted to reduce the bending moments in the blades as one typical example. The contribution discusses the case when the damage accumulation is suddenly increased due to an unexpected situation (for instance high crack propagation rate) targeting to show that even if it happens, it is possible to retain the planned service lifetime through an suitably adopted control strategy. Influencing factors on the fatigue damage progression are pointed out. Flap-wise, edge-wise blade bending moments, fore-aft and side-to-side tower bending moments time series data simulated using FAST model developed by NREL are used for these purposes. Furthermore, the system state determination based on accumulated fatigue damage is done using the diagnosis-based data-filtering algorithm, and is represented in form of traffic-light-like coding. Here each color describes a specific system state.

Investigation of damage detectability in composites using frequency-based classification of acoustic emission measurements

Advances in composite technology led to the substitution of conventional, metallic construction material by composites. However, the more widespread application of composites is currently restricted by complex fracture mechanisms, which are not well understood. One approach to overcome this challenge is structural health monitoring systems which provide a lot of information on the current system state as well as state of health in real time. In this context, reliability assessment of structural health monitoring systems is currently an open issue. The reliability of conventional non-destructive testing systems is evaluated, measured, and partly standardized using widely accepted methods such as the probability of detection rate. Frequently, the a90|95 value, which is determined from the probability of detection curves, is used as a performance measure indicating the minimum damage size that is detected with a probability of 90% and 95% confidence. In contrast to non-destructive testing, structural health monitoring involves additional data analysis steps, that is, statistical pattern recognition, where the classification results are also subject to uncertainty. Because similar methods are not available, the reliability of structural health monitoring systems is usually not quantified. To investigate the influences on the classification performance, experiments were conducted. In particular, the effect of variable loading conditions and the evolution of damage over time are considered. To this end, acoustic emission measurements were performed, while the specimens of the composite material were subjected to different cyclic loading patterns. Here, acoustic emission refers to elastic stress waves in the ultrasound regime, which emerge from the structure on damage initiation and propagation. Furthermore, a frequency-based damage classification scheme for acoustic emission measurements is proposed. Time–frequency domain features are extracted from the measurement signals using short-time Fourier transform. Classification is performed using support vector machine. Both choices serve as typical examples to discuss the effects which apply equally to other approaches. Experimental results presented in this article regarding fault diagnosis and discrimination of delamination, matrix crack, debonding, and fiber breakage in carbon-fiber-reinforced polymer material show that good performance applying support vector machine could be achieved using 10-fold cross validation. However, during model deployment, strong dependency of the classification reliability on loading conditions can be clearly stated, which could not be seen from the previous evaluation. Concluding from these results, it can be stated that the application of classifier-based structural health monitoring is more complex than generally assumed. The relations between the classification approaches, testing conditions, measurement devices, and filters have to be discussed with respect to the ability to provide reliable statements about the actual damage state.

Wear process lifetime prediction based on parametric model applied to experimental data

Lifetime prediction of a technical system plays a significant role also with respect to the avoidance of breakdowns. The first part of this contribution is a brief review of lifetime models followed by an introduction of a new parametric lifetime model. Experimental data for the lifetime model training and evaluation are taken from a tribological system describing a wear process. The main focus of this contribution is the development of a prognosis approach for the end-of-lifetime estimation using proposed lifetime model. The impact of the number of datasets used for model training on prediction accuracy is discussed. Two cases are studied considering different number of datasets used for lifetime model training. Additionally, prediction accuracy with the approach to the end-of-lifetime (higher number of available measurements) are discussed.

Wear Aging and Related Impact on System Reliability

Deterioration processes occurring within two moving surfaces of tribological system manifested through the loss of material or any other material change are defined as wear. In this contribution, the analysis of wear is indispensable in terms of wear rate decrease as the point in time at which the system becomes nonfunctional. It is assumed that this moment in time can be postponed by ensuring lower wear rate. The technical system concerned in this contribution as example is a wind turbine system (WTS). The analysis of wear mechanisms herein is carried out taking into consideration critical components of WTS. Concerning this the most attention is attracted to thermal, mechanical, and chemical aspects of wear processes occurring in critical components and contributing to the loss of functionality due to related aging. The failures appearing within wind turbine systems as well as corresponding decrease in WTS reliability are intensively inferred in recent years. A number of reliability databases have ensued targeting to obtain detailed knowledge of failure causes and failure modes with special emphasis to the correlation of failure modes to applied loads. Using benchmark data stored in reliability databases, the identification of critical components concerning the failure occurrence frequency, the number of system downtimes, mean time between failures, as well as related additional reliability parameters is facilitated. In this contribution, the focus is given on wear process of rotor blades and bearings. The wear modes briefly introduced herein are associated with aforementioned components. The idea lies behind gaining the knowledge about actual system’s state is the modeling of deterioration processes. Finally, the options for adapted operating conditions targeting to achieve decreased deterioration rate are pointed out. doi: 10.12783/SHM2015/353

Li-O Battery Aging Process: A Comprehensive Review With Respect to the Integration of Aging Into System’s Powermanagement

Intensive development of hybrid electric vehicles in recent years is conditioned by ecological requirements reflecting in the reduction of greenhouse gas emission and by the limitation of the use of fossil fuel. Lithium-Ion Batteries (LIBs) become unavoidable component serving as an energy storage element in transportation industry as well as in solar and wind energy systems. The problems related to battery state monitoring in hybrid electric vehicles refer to the estimation of immeasurable degradation parameters. Concerning existing literature, the measurements indirectly correlated to the deterioration process are used for the calculation of degradation parameters. Special attention is given to LIB parameters related to battery aging such as capacity fade and internal resistance increase. Existing approaches to these parameters estimation/calculation concern no direct measurement of degradation parameters. Indirect relations can be established assuming suitable assumptions. The contribution addresses the main issues related to LIB parameters monitoring as well as to the adopted control strategy providing high energy efficiency while maintaining as less as possible rate of component degradation.Copyright