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Blade tip timing (BTT) is a noncontact blade vibration measurement method for rotating blade condition monitoring. However, it is difficult to install a once-per-revolution (OPR) probe due to the internal environment limitation of an engine. To solve this problem, an OPR-free BTT method is proposed in this article. Compared with the existing OPR-free methods that are based on blade displacement, this method is based on blade spacing change (BSC). In addition, a probe layout selection method based on minimum redundancy linear array (MRLA) is introduced to reduce the redundancy of the acquired signal. Simulations are conducted to prove that the proposed probe layout selection method has a stronger signal extraction capability and better robustness. The experiment is conducted to show that BSC is an alternative way for rotating blade condition monitoring and the technical route of this article is feasible.

Blade health monitoring (BHM) is an important technology to ensure safe operation of turbine machinery. The blade tip timing (BTT) method is a non-contact approach of vibration monitoring that is widely used for aero-engines. A BTT signal can be characterized as nonuniform and sub-Nyquist sampled (i.e., under-sampled). For nonuniform sampling, the least squares (LS) fitting method has long been used to reconstruct the spectrum. However, the under-sampling of the BTT signal causes the spectrum obtained by LS fitting aliasing. The iterative reweighted LS periodogram (IRLSP) can eliminate the aliasing caused by sub-Nyquist and nonuniform sampling. This method weakens the frequency aliasing through the method of reweighted iteration, but the consequence of this operation is that the amount of calculation is greatly increased. Thus, we propose an adaptive iterative approach (AIA) based on the IRLSP that combines a priori information of the blade. The main contribution of this method is to improve the computational efficiency for processing the BTT signal and the effect of anti-aliasing. These two points mean that this method is expected to become a real-time blade vibration monitoring method. We combined a finite element model with modal testing to perform simulations and verify the improvement with AIA. A large number of experiments were also performed to verify the effectiveness of our approach for BHM.

Blades are weak and essential parts of an aeroengine. Monitoring the running state of blades is essential for ensuring the safety of aeroengine operation. Blade tip timing (BTT) is considered a promising technique for operational blade vibration monitoring for turbomachinery; however, it is difficult to analyze the BTT signal because of the inherent undersampled limitation. To overcome this limitation, the minimal sensor layout scheme is restricted to be “three.” Usually, more than five probes are required for good signal revolution. This requirement is an installation restriction that limits the application of BTT; however, for users, the conflict between probe layout optimization and real installation restriction is also a serious problem. Motivated by these issues, a single-probe BTT (SP-BTT) method is proposed; the SP-BTT method could extract the shift between natural frequencies, which could be used as a damage index for blade condition monitoring. To validate the effectiveness of the proposed method, numerical simulations and experimental tests are performed.

Blade tip timing (BTT) is an effective noncontact measurement technology for rotating blade health monitoring. However, due to the mismatching between the high-speed rotation and the limited amount of probes, the signal collected from the BTT system is severely undersampled, which induces the difficulty in feature extraction. Multiple signal classification (MUSIC) has the potential to overcome the undersampled problem once the probes are properly placed. Whereas, if traditional MUSIC is directly used in BTT, the accuracy of frequency identification cannot be high enough and the identified number of frequency components is also severely restrained. To address these two problems, an improved MUSIC is proposed as an alternative methodology to extract the blade vibration frequency for BTT. Based on the orthogonality of the signal subspace and the noise subspace from undersampled signal, the presented method can effectively identify the vibration frequency components from the undersampled signal of BTT.

Foreign object damage (FOD) is a frequent but dangerous failure of the compressor. Blade tip timing (BTT) is regarded as a promising on-line monitoring method for blades, but it is difficult to obtain the real FOD data in a laboratory environment. This paper proposes a virtual BTT measuremnt method for FOD. Firstly, we establish the dynamic response model of the multi-degree of freedom damped system, and use the modal superposition method to infer its response displacement under the impact of a foreign object. Secondly, we use the Johnson-Cook model to calculate the stress limit of the blade model. Thirdly, finite element analysis is employed to calculate the influence of different impact positions on the blade tip displacement and the blade failure. Finally, we use the Simulink module to simulate the blade vibration displacement signal, and the measurement process according to the principle of tip timing. By this means, the laboratory simulation of BTT measurement under FOD is realized.

Engine compressor blades play important roles in aeroengine operation. They are easily injured by foreign objects due to direct contact with the outside air. Blade tip timing (BTT) is a non-contacting vibration measurement method for rotating blade health monitoring. However, the under sampled problem is an inevitable obstacle in the feature extraction of BTT signal. An alternative feature extraction method called autocorrelation method for asynchronous vibration analysis is proposed in this paper to identify the vibration amplitude and frequency of the under sampled BTT signal simultaneously. Based on the assumption of single frequency vibration, the signals collected by three probes are used to form two equations, and the vibration frequency and amplitude are obtained by solving them. Additionally, the discussion on the probe layout required by the proposed method is conducted. The verification using simulation signal (the noised signal with one frequency component) and experimental signal (signal collected under constant rotating speed) shows that the amplitude and frequency can be accurately extracted by the proposed method.

The high intra class diversity of remote sensing image scene often leads to the problem of difficult classification of remote sensing image scenes. Therefore, this paper proposes the CA-EfficientNetV2 model, embedding the coordinate attention into the head of the EfficientNetV2 network to enhance the classification effect. The coordinate attention is used to generate the position relationship between image spaces and channels so as to learn features efficiently. We trained three improved models CA-EfficientNetV2-S, CA-EfficientNetV2-M and CA-EfficientNetV2-L on UC Merced remote sensing dataset respectively. The classification accuracy reached 99.55%, 97.49% and 97.09% respectively. Among them, CA-EfficientNetV2-S had the best effect, which was improved by 0.8% compared with the original network.

Compressor blades often operate in high temper-ature, high pressure, and heavy load environments. They are in direct contact with the outside air, which causes their life to be severely limited. Blade vibration is an important factor that reflecting the state of the blade. Therefore, effective blade vibration monitoring methods are needed. Blade tip timing (BTT) is a non-contact blade vibration measurement method that has the potential to achieve real-time condition monitoring. However, one of the biggest obstacles to real-time condition monitoring is the once per revolution (OPR) sensor in BTT. Due to the internal environment limitations of aero-engines, installing the OPR sensor in an actual engine is extremely expensive, and it is almost impossible to install the OPR sensor. To solve this problem, the relationship between the blade spacing change (BSC) and the blade vibration is analyzed and the measurement principle of BSC is presented in this study. Based on BSC, we proposed a novel BTT measurement method that does not need the OPR signal. The simulation and experiment are conducted to show the rationality of the proposed method.

The harsh working environment of the compressor hinders the condition monitoring of rotating blades. Blade faults are mostly induced by foreign object damage or high cycle failure due to initial defects. Detecting these failures before blades being broken will not only ensure engine safety but largely reduce repair costs. As a non-contact measurement technique, Blade Tip Timing(BTT) becomes a more popular method recently for health monitoring and fault diagnosis. By analyzing BTT data, many blade vibration parameters could be obtained. In this paper, we discuss how to extract first bend natural frequency, amplitude and static offset from BTT data. Then the change rule of these parameters around blade fault is illustrated. Afterward, a clustering method is employed to combine these parameters. In addition, a test with two faulty blades is introduced and used to verify the effectiveness of the proposed method.

Natural frequency is an essential parameter for rotating blade condition monitoring. Various previous simulations and experiments have shown that multiple signal classification (MUSIC) has the advantage of filtering out the synchronous frequency component to make the natural frequency more prominent in the frequency domain. However, the negative effect of this characteristic is the poor performance in the resonance area because of the overlap between the synchronous frequency component and the natural frequency. This effect results in the disconnection of the natural frequency line in the resonance area. Thus, morphological filtering and mean absolute error (MAE)-based curve fitting are applied to robustly extract and restore the natural frequency line. Based on this method, automatic tracking of the natural frequency in the time–frequency domain is proposed in this study. Additionally, the mathematical principle of the inherent characteristic of MUSIC to filter out synchronous frequency components is first mentioned and explored herein. Furthermore, simulations and experiments under variable rotating frequencies are conducted to show that the proposed method can track the natural frequency under variable operating conditions.

This paper, taking the administrative management of sea use licensing in China as an example, defines the stakeholders herein, including sea use applicant(s) and other stakeholders under management, as well as other administrative departments. It analyzes the interest appeal, social responsibility and obligations of different stakeholders. In combination with the current management system of administrative licensing of sea use, interest coordination approaches are investigated, including the interest coordination of those regulated, the opinions of management departments, and the intervention of government departments.

Non-Fourier heat behavior is an important issue for film material. The phenomenon is usually observed in some laser induced thermal responses. In this paper, the non-Fourier heat conduction problems with temperature and thermal flux relaxations are investigated based on the wavelet finite element method and solved by the central difference scheme for one- and two-dimensional media. The Cattaneo&ndash, Vernotte model and the Dual-Phase-Lagging model are used for finite element formulation, and a new wavelet finite element solving formulation is proposed to address the memory requirement problem. Compared with the current methodologies for the Cattaneo&ndash, Vernotte model and the Dual-Phase-Lagging model, the present model is a direct one which describe the thermal behavior by one equation about temperature. Compared with the wavelet method proposed by Xiang et al., the developed method can be used for arbitrary shapes. In order to address the efficient computation problems for the Dual-Phase-Lagging model, a novel iteration updating methodology is also proposed. The proposed iteration algorithms on time avoids the use the global stiffness matrix, which allows the efficient calculation for title issue. Numerical calculations have been conducted in the manner of comparisons with the classical finite element method and spectral finite element method. The comparisons from accuracy, efficiency, flexibility, and applicability validate the developed method to be an effective and alternative tool for material thermal analysis.

Blade tip timing (BTT) is one of the most important non-contact monitoring methods for blade vibration estimation. BTT predominantly consists of two steps: 1) acquiring the original pulse signal generated by the rotating blade through optical probes. 2) Obtaining the arrival time of the original pulses through a high-precision counter and then transforming it to deflection. Multiple noise is involved in BTT measurement, which is further complicated by the variable operating environment owing to the complexity and multiplicity of blade vibration and the transmission path. With the introduction of prior knowledge, sparse representation has proved a promising tool for the reconstruction of blade vibration features. However, the classical sparse representation model applied in BTT, is mostly formulated and conducted based on the simple assumption that the noise follows a Gaussian distribution. The assumption, too idealized for real practices, restricts the performance promotion of sparse representation in BTT. To address this problem and to represent the unknown noise, a robust sparse representation model based on a mixture of Gaussians (MoG) is proposed in this work. The solution algorithm of the proposed model is then derived from the perspective of the expectation maximization (EM) algorithm. To validate the effectiveness of the present method, the performance of the developed methodology is discussed in terms of different regular items.

Blade tip timing (BTT) is a noncontacting vibration measurement method for rotating blade condition monitoring. The BTT sampling frequency is proportional to the number of probes under uniform probe layout and constant rotating speed. However, the limitation of the probe installation space in aeroengines causes the lack of probes, which leads to the inherent undersampled feature of the BTT signal. Thus, frequency identification of nonuniform undersampled signal has become a popular field in BTT signal processing. Multiple signal classification (MUSIC) is an array signal processing method that has been applied to this field. Nevertheless, the frequency traversal in MUSIC is a time-consuming process that will affect the online monitoring performance. Its running time is highly related to the dimension of the noise subspace. The subspace dimension reduced MUSIC (SDR-MUSIC) is proposed in this article to reduce the running time of MUSIC by reducing the dimension of noise subspace. Simulations and experiments are conducted to testify that SDR-MUSIC can significantly reduce the running time of MUSIC while ensuring accuracy.

The harsh working environment of compressor threats the integrality of bladed disk. Crack is one of the most fatal faults of rotating blades. Detecting the crack of blades at early stage could ensure the safe operation of gas turbine and save economic costs. Blade tip timing (BTT) is a noncontact blade vibration measurement technique, which is widely used in recent years. In this article, a procedure of crack detection using BTT data is proposed. First, some typical parameters are taken into consideration, including resonance frequency, amplitude change of natural frequency, amplitude of blade tip, phase, and interblade spacing. It has been found that the frequency of the blade will decrease with the increment of crack length and reduction of crack height. In addition, the resonance frequency of the bladed disk will split when the blade cracks. The amplitude of different orders of resonance frequency varies with cracks. The amplitude of the first order changes very slightly, whereas the second and third orders are relatively obvious. The blade tip vibration amplitude and phase change with cracks without obvious regularity. The interblade distance will change due to the appearance of cracks. According to the above rules, the frequency and the interblade distance are adopted as crack diagnosis indicators. First, we use the Lomb–Scargle periodogram to quickly determine whether the crack appears and then use the blade frequency and interblade distance to locate the number of the blade having crack. In the experimental test, BTT data measured on a titanium bladed disk is used to verify the effectiveness of our method.

Increasing demand for practical applications is forcing more in-depth research into optical vortices (OVs): from the generation and measurement to the shaping and multi-singularity manipulation of OVs. Herein, we propose a new type of phase structure called the high-order cross-phase (HOCP), which can be employed to modulate OVs to implement polygonal shaping and multi-singularity manipulation simultaneously at far-field. Theoretically, we investigate the propagation characteristics of OVs modulated by a HOCP. In experiments, we achieve the polygonal shaping and multi-singularity manipulation of OVs via HOCPs. On this basis, we discuss the relationship between shapes and the orders of HOCPs, where we find that the order of a HOCP is exactly equal to the number of sides of polygonal OVs. This work provides a novel method to achieve polygonal shaping and multi-singularity manipulation simultaneously, which facilitate applications in optical micro-manipulation and optical communication.