Gearbox bearings are critical elements of wind power generation systems. Their stable operation supports the power generation, thus reducing the downtime and improving the economic efficiency of wind farms. With the wide availability of sensors, data-driven methods have started to be utilized instead of physical-based methods for condition monitoring of wind energy infrastructures. Deep learning provides significant advantages to achieving this end due to its ability to extract and select representative features without expert knowledge. The present study proposed an intelligent method based on one-dimensional convolutional neural networks (1D-CNN) to extract useful features from the vibration signals and classify different bearing faults. The performance of the proposed 1D-CNN model was evaluated employing the Case Western Reserve University dataset. As a result, the proposed method achieved an average prediction accuracy of 99.56%. The findings confirmed that the method has good stability and potentially be used to reduce operation and maintenance costs.
Today gears are one of the most crucial machine elements in the industry. They are used in every area of the industry. Due to the high performances of the gears, they are also used in aerospace and wind applications. In these areas due to the high torques, unstable conditions, high impact forces, etc. cracks can be seen on the gear surface. During the service life, these cracks can be propagated and gear damages can be seen due to the initial cracks. The aim of this study is to increase the fatigue crack propagation life of the spur gears by using asymmetric tooth profile. Nowadays asymmetric gears have a very important and huge usage area in the industry. In this study, the effects of drive side pressure angle on the fatigue crack propagation life are studied by using the finite element method. The initial starting points of the cracks are defined by static stress analysis. The starting angles of the cracks are defined constant at 45°. The crack propagation analyses are performed in ANSYS SMART Crack-Growth module by using Paris Law. Four different drive side pressure angles (20°-20°, 20°-25°, 20°-30° and 20°-35°) are investigated in this study. As a result of the study the fatigue crack propagation life of the gears is increased dramatically when the drive side pressure angle increase. This results show that the asymmetric tooth profile not only decrease the bending stress but also increase the fatigue crack propagation life strongly.
Gears are fundamental components used to transmit power and motion in modern industry. Their health condition monitoring is crucial to ensure reliable operations, prevent unscheduled shutdowns, and minimize human casualties. From this standpoint, the present study proposed a one-dimensional convolutional neural network (1-D CNN) model to diagnose tooth root cracks for standard and asymmetric involute spur gears. A 6-degrees-of-freedom dynamic model of a one-stage spur gear transmission was established to achieve this end and simulate vibration responses of healthy and cracked (25%–50%–75%–100%) standard (20°/20°) and asymmetric (20°/25° and 20°/30°) spur gear pairs. Three levels of signal-to-noise ratios were added to the vibration data to complicate the early fault diagnosis task. The primary consideration of the present study is to investigate the asymmetric gears’ dynamic characteristics and whether tooth asymmetry would yield an advantage in detecting tooth cracks easier to add to the improvements it affords in terms of impact resistance, bending strength, and fatigue life. The findings indicated that the developed 1-D CNN model’s classification accuracy could be improved by up to 12.8% by using an asymmetric (20°/30°) tooth profile instead of a standard (20°/20°) design.
rectangular loop causes resonance when perimeter is equal to one wavelength. Figure 15 shows that the rectangular loop occurs resonance at 7.9 GHz, and then the resonance can make broader upper frequency bandwidth. We also find that the RF is higher with the increasing of the magnitude "L" because the perimeter is shorter in the Figure 16. We make trade-off and take the advantage of shifting band to meet our specs.We show the radiation patterns and compare with simulations and measurements in xy and xz plane in Figure 17. The radiation pattern of peak gain is summarized in the Table 4. Some difference between simulation and measurement comes from the antenna implemented on a real printed dielectric substrate. Obviously, higher frequency band causes more difference. The radiation pattern is approaching to omnidirectional and it is very suitable for WLAN devices, for example notebooks, laptop computers, access points, etc. CONCLUSIONSWe successfully propose two methods, RSC and LRF, to design miniature PIFA and IFMA operating in WLAN bands. The main considerations in design process include broad bandwidth and easy implementation in a limited mechanism space. Both methods proposed exhibit different advantages for PIFA. The RSC shows precise impedance matching, but the bandwidth is worse. Its bandwidth is 220 MHz (9%) from 2.36 to 2.58 GHz, return loss is 40.8 dB at f r ϭ 2.47 GHz, and the gain is up to 3 dBi. The LRF shows broader bandwidth than RSC but little precise impedance matching results as Eq. (4) still works as an approximate equation. The Ϫ10 dB bandwidth of return loss is 304 MHz (12.4%) from 2.332 to 2.636 GHz, return loss is 23.39 dB at f r ϭ 2.47 GHz, and the gain is 2.5 dBi. The two methods are easy to implement, and we choose either one to design IFA depending on the requirements. Impedance matching and upper frequency bandwidth are obtained by controlling the length of SC. Although IFA presents a disadvantage for narrow bandwidth, we propose some ways to improve the problem. First, we change the type from IFA to PIFA by increasing the radiator and raise the height between ground and radiator, etc. Second, we use multiadjacent resonant frequencies to broaden bandwidth. We conclude that bandwidth is 210 MHz (8.5%) from 2.38 to 2.59 GHz and upper bandwidth is 2940 MHz (50.4%) from 4.36 to 7.3 GHz. For reaching miniature PIFA and IFMA, we bend the radiator. It is well known that the lower frequency is narrower because it is IFA without using adjacent RFs. However, the bandwidth is large enough for applying WLAN bands, and the peak gain is more or less 1 dBi at 2.4, 5.2, and 5.8 GHz. Higher frequency yields lower gain because the antenna is implemented in a real printed substrate with a loss tangent of 0.0025.
The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute towards the Prognostics and Health Management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.
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