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Multiscale-based entropy methods have proven to be a promising tool for extracting fault information due to their high feature extraction ability and easy application. Despite multiscale analysis showing great potential in extracting fault characteristics, it has some drawbacks, such as cutting the data length and neglecting high-frequency information. This paper proposes a bi-filter multiscale diversity entropy (BMDE) to filter comprehensive fault information and address the data length problem. First, the low-frequency information is filtered out by moving average in a multi-low procedure and the high-frequency information is filtered out by an adjacent subtraction in a multi-high procedure. Second, a modified coarse-grained process is introduced to overcome the issue of data length. The validity of the BMDE method is evaluated using both simulation signals and experimental measurements. Results demonstrate that the proposed method offers optimal feature extraction capability with the highest diagnostic accuracy compared with four other traditional entropy-based diagnosis methods.

Compared with the deepening development of time-space coding metasurfaces in the electromagnetic and acoustic field, the research on time-space coding metasurface in the field of elastic vibration is still in the infancy. In order to enrich the designed schemes of spatiotemporal coding for elastic vibration, a time-space coding metasurface based on the random modulation strategy of wavefront phase is proposed in this paper. Compared with traditional homogeneous materials with highly correlated vibration transmission, the proposed coding metasurfaces with homogeneous materials realizes extremely low correlated vibration transmission in different vibration directions, which provides a physical realization for the measurement matrix in compression sensing. Furthermore, combined with compressed sensing technology, the potential application value of the proposed time-space coding metasurfaces in single-sensor vibration location and impact identification is verified through simulation.

Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.

To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.

Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest accuracy.

According to the performance degradation law of aeroengine in operation, we proposed a method of aeroengine performance degradation evaluation based on the fusion decision of Hierarchical Bayes method (HB) and deep neural network (DNN). Hierarchical Bayes method was used to build the aeroengine reliability model. Then Based on the aeroengine operation monitoring parameters and reliability model, we used deep learning method to extract the performance degradation law and to evaluate aeroengine performance level, realized the fusion decision of engine performance degradation assessment. 9467 monitoring samples, contaminated with sensor noise, collected from 50 aeroengines was used to evaluate the quality of the network, and the average accuracy is 92.01%. The results showed that this method showed good robustness and reduced the risk of the evaluation results error caused by noise.

With the development of power market construction, in order to ensure the effective competition of the power market and the stable operation of the system, under the competitive electricity market mode, it has become an urgent problem to improve the power sales market supervision system. Firstly, the characteristics of the electricity sales market under the competitive electricity market mode is analyzed. Secondly, problems in the surveillance of the electricity sales market are pointed out. Finally, the countermeasures and suggestions for the surveillance of the electricity sales market under the competitive electricity market mode are put forward.

The temperature rise caused by three-phase short current at stator side of doubly fed induction generator (DFIG) may damage DFIG and its adjacent electrical equipment. By using the step-by-step integration, the thermal effect may be quantified, but the contribution of periodic component and aperiodic component of thermal effect cannot be distinguished, and the amount of calculation is large. In this paper, the steady-state component of rotor flux is considered to reduce the error of analytical expression of short current. The components of short current are decomposed and the square of the short current is integrated. The calculation method of equivalent duration of aperiodic component of thermal effect is proposed, and the effect of equivalent duration on different crowbar resistance and stator voltage drop ratio is quantified. The simulation results are provided to validate the accuracy of the proposed models.

With the continuous development of the energy Internet, small and micro market entities are given the right to choose independently. Multiple new entities such as distributed energy, virtual power plants, electric vehicles, interactive energy use enter the power market and participate in power trading, creating an open and shared power market environment. At the same time, with the deepening of China’s power system reform, the focus of market construction will shift from wholesale side to retail side. Therefore, it is urgent to study the business model of power retail market, so as to take necessary preventive measures and avoidance measures as early as possible to avoid and reduce the pressure and risk faced by grid companies.

In this paper, we mainly study the application of a multi-objective optimization algorithm based on the black hole algorithm in motor optimization. Using the combination of global search and local search, the continuous search area is better. The random search method is used in the global search, and a momentum gradient method is used in the local search, which makes the search results have faster convergence rates and easier convergence to the global optimal. A new file management strategy is used to make the optimization results more global and have better generalization ability. in practical application, the design of motor is often affected by manufacturing error, so random noise is added to the selection of optimization results, which can be more in line with the practical application. Finally, the actual motor model and complex function are used to test the performance of the optimization algorithm. Finally, the actual motor model and complex function are used to verify the performance of the optimization algorithm.

A mitochondrion has a pH gradient between the two sides of its inner membrane in order to produce adenosine triphosphate (ATP). Because ATP depletion causes numerous diseases, the measurement of the pH value around the mitochondrion is expected to clarify the mechanism of these diseases. In this study, a dual-wavelength pH-sensitive dye was excited by two-photon absorption initiated using a femtosecond pulse laser. In addition, fluorescence from the dye was directly collected from the fluorescent point using the collection-mode probe of a scanning near-field optical microscope. By this proposed method, a pH calibration curve was obtained from the fluorescent intensity ratio of the dye solution, and temporal pH variations with 0.1 s time resolution following the addition of acid were observed. Moreover, mitochondrial activity on the basis of the pH changes was successfully observed in three different mitochondrial densities.