Key Engineering Materials Vols. 569-570

Abstract: This paper presents a new approach for bearing defect diagnosis in induction motor by taking advantage of three-phase stator current analysis based on Concordia transform. The current signature caused by bearing defect is firstly analyzed using an analytic model. Concordia transform is performed to extract the instantaneous frequency based on phase demodulation. The bearing defect feature is then identified via spectrum analysis of the variation of current instantaneous frequency. Both simulation and experimental studies are performed to demonstrate the effectiveness of proposed method in identifying bearing defects. The method is inherently low cost, non-invasive, and computational efficient, making it a good candidate for various applications.

Abstract: Tooth breakage is a typical failure form of wind-turbine planetary gear transmission system, it is important to study the influence of tooth breakage on vibration characteristics of planetary gear transmission system. In this paper, considering the tooth breakage defect, a lumped parameter vibration model of a planetary gear system with time-periodic mesh stiffness is established. Effects of the length and width of tooth breakage on meshing stiffness and dynamic response are discussed in detail. The relation between characteristic frequency of the tooth breakage fault and rotating speeds is pointed out. Several statistical indicators are utilized to show the influence of two parameters (length of planet tooth breakage and input speed) on the dynamic response of the system. Experiments are carried out to verify the simulation results. These results would be useful for fault diagnosis of wind turbine transmission system at different operation conditions.

Abstract: Spalling of the races or rolling elements is one of the most common faults in rolling element bearings. Exact estimation of the spall size is helpful to the life prediction for rolling element bearings. In this paper, the dual-impulsive phenomenon in the response of a spalled rolling element bearing is investigated experimentally, where the acoustic emission signals are utilized. A new method is proposed to estimate the spall size by extracting the envelope of harmonics of the ball passing frequency on the outer race from the squared envelope spectrum. Compared with the cepstrum analysis, the proposed procedure shows more powerful anti-noise ability in the fault size evaluation.

Abstract: Wind is a competitive, clean, and fast-growing renewable energy industry. However, in order for wind to compete with fossil fuel-based energies, it is necessary to achieve lower cost of energy. One way is to reduce operations and maintenance costs by integrating structural health monitoring (SHM) systems with wind turbines. It has been found that the fiber-reinforced polymer (FRP) composite-based wind blades are susceptible to damage (e.g., cracks, debonding, and impact). Damage is typically invisible to the naked eye and can propagate rapidly to cause sudden failure. This work presents a new SHM approach using embedded thin film sensors for detecting damage in FRP-based wind blades. While previous studies have shown that carbon nanotube-based thin films can be incorporated with FRPs for sensing, this study further investigates their electromechanical properties. First, a unique spray fabrication approach was employed so that films can be assembled on a low cost basis and can be deposited onto any substrate or structure. Second, the electrical properties of films subjected to post-fabrication thermal annealing were compared. Finally, freestanding films were prepared and subjected to uniaxial tensile cyclic loading while their resistivity was measured simultaneously. The results showed that these films were piezoresistive.

Abstract: Operational Modal Analysis (OMA), also known as output-only modal analysis, allows identifying modal parameters only by using the response measurements of the structures in operational conditions when the input forces cannot be measured. These information can then be used to improve numerical models in order to monitor the operating and structural conditions of the system. This is a critical aspect both for condition monitoring and maintenance of large wind turbines, particularly in the off-shore sector where operation and maintenance represent a high percentage of total costs. Although OMA is widely applied, the wind turbine case still remains an open issue. Numerical aeroelastic models could be used, once they have been validated, to introduce virtual damages to the structures in order to analyze the generated data. Results from such models can then be used as baseline to monitor the operating and structural condition of the machine.

Abstract: As wind turbines increase in magnitude without a proportional increase in stiffness, the risk of dynamic instability is believed to increase. Wind turbines are time dependent systems due to the coupling between degrees of freedom defined in the fixed and moving frames of reference, which may trigger off internal resonances. Further, the rotational speed of the rotor is not constant due to the stochastic turbulence, which may also influence the stability. In this paper, a robust measure of the dynamic stability of wind turbines is suggested, which takes the collective blade pitch control and non-linear aero-elasticity into consideration. The stability of the wind turbine is determined by the maximum Lyapunov exponent of the system, which is operated directly on the non-linear state vector differential equations. Numerical examples show that this approach is robust for stability identification of the wind turbine system.

Abstract: A stochastic time domain fatigue analysis is pursued on steel tripod and jacket support structures for offshore wind turbines, following the basic concepts of recently validated simplified linear analyses. A set of environmental lumped load cases with corresponding probabilities of occurrence is considered. Results are compared in terms of a lifetime damage equivalent load.

Abstract: The paper summarises some advanced damage detection approaches used for Structural Health Monitoring (SHM) and Condition Monitoring (CM) of wind turbine systems. In the signal processing part, recent time-frequency analysis methods will be presented and examples of their application on condition monitoring of gearboxes will be given. In the pattern recognition part, examples of damage detection in blades will be used to introduce different algorithms for novelty detection.

Abstract: This paper presents a method to detect and identify damage in a laboratory offshore wind turbine support structure. The structure consists of three different parts: the jacket, the tower and the nacelle. The jacket is a lattice structure joined with several bolts. The tower consists of three different sections joined by bolts. The nacelle is composed of a single piece. The different parts are also joined with bolts. The damage in the structure is simulated by loosening some of the bolts in the jacket. Two damage detection algorithms, namely AutoRegressive methods and NullSpace methods, have been tested in a primitive variation of this structure without the jacket, obtaining good results. In this paper we present the application of the last damage detection method to the new structure with the jacket and an extension to identification of the damage.