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The evaluation of the wear state of the hob during gear machining can effectively optimize the tool change strategy, which is of great significance to the improvement of machining efficiency. Aiming at the wet machining conditions for large gears, a method of hob wear monitoring called 'Drift Prediction of Gamma Distribution Parameter (DP-GDP)' is proposed based on time domain impulses statistical distribution. Firstly, a vibration signal model of the hob is established based on the combined action of periodic cutting force excitation and random impact excitation. The vibration parameters such as damping and stiffness are discussed. Then, the Poisson process mechanism of random collision between hob and material particles during hob cutting is taken into consideration. The relationship between hob wear stage and occurrence of impulses excited by material particles collision is modeled under over-damping condition. The gamma distribution mechanism of the time required for impulses generation is elucidated. Furthermore, a wear feature extraction method based on the statistical parameters of random impulses is proposed. The method focuses on single impulse identification and description of impulses occurrence probability distribution in time domain signal. Based on this, a prediction curve that reflects the wear state can be got for hob life degeneration assessment. Finally, a full-life test of hobs during the hob cutting process in the industrial field is completed. The data of hob whole life in different wear states are acquired. The simulation and experimental results show that the proposed method can effectively predict the whole life wear process of the hob. The extracted features can accurately map the hob wear state. Compared with traditional methods such as RMS and wavelet, the proposed method has better validity and accuracy under specific processing conditions. [ABSTRACT FROM AUTHOR]

Compressed Air Energy Storage (CAES) is considered as one of the key solutions to handle intermittent and random wind power. However, limited energy conversion efficiency and high capital cost of energy storage have restricted significantly the integration of wind power with CAES. In this study, a grid-connected power optimization strategy based on piecewise averaging of real-time wind power and electricity price data is developed to ensure continuous and stable power outputs to the grid using modified profit-maximizing algorithm. Thermodynamic analysis on the performance of low-temperature adiabatic CAES, energy conversion, and economic evaluation were carried out for a hybrid wind/low-temperature adiabatic CAES system (wind/LA-CAES) with pressure vessels. The proposed optimization strategy reduced the required capacity of CAES and the levelized cost of electricity (LCOE) significantly with greater utilization of wind power and operation profitability. The findings presented in this study is of significant reference value to future development of large-scale wind power integrated with CAES. [ABSTRACT FROM AUTHOR]

This paper proposes a novel and powerful pattern recognition method named symmetrical singular value decomposition representation (SSVDR) and presents its application to face recognition. The SSVDR method is based on singular value decomposition (SVD) and symmetry prior. In this method, the given image is firstly decomposed into a composition of a set of base images by the singular value decomposition technique. Then, the first few base images (which can be proved to be the low-frequency asymmetrical base images) are turned into symmetrical base images according to facial symmetry. Finally, a new representation of the original image is reestablished for the final recognition. For evaluating the performance of the SSVDR method, some experiments are conducted in two famous face databases: extended Yale B and CMU-PIE database. The experiment results show the proposed SSVDR method can reestablish a new homogeneous representation of the original image and has an encouraging performance on face recognition compared with the current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A method for constructing grayscale images of vibration signals has been proposed. • An unconventional convolutional kernel design method has been proposed. • The validity of the fault diagnosis method in this paper were verified. Rolling bearings are widely used in various industries, including rail transit, aerospace, and wind power generation, playing a critical role. However, bearing failures can lead to serious consequences, impacting equipment operation and even causing safety accidents. Hence, the diagnosis of bearing faults is of utmost importance. However, the variable speed conditions experienced during bearing operation pose significant challenges to fault diagnosis. To overcome the limitations of traditional methods in diagnosing bearing faults under variable speed conditions, this paper proposes a fault diagnosis method based on the gray-level co-occurrence matrix (GLCM) and Dual Channel Convolutional Neural Network (DCCNN). The method introduces a two-dimensional grayscale matrix construction (2D-GMC) technique to extract grayscale texture features for fault diagnosis. Additionally, an unconventional kernel design method, based on grayscale image contrast, is proposed to reduce the complexity associated with traditional square kernels. A new DCCNN architecture is developed accordingly. Furthermore, the transfer learning concept is utilized to train the proposed DCCNN model using fault signals at specific rotational speeds. The method intercepts the variable speed into multi-speed short-time series, then constructs gray image under different speed to realize the rapid fault diagnosis of bearings under variable speed conditions. [ABSTRACT FROM AUTHOR]

The integrated electricity and heating system (IEHS) can improve energy efficiency and promote renewable energy consumption. When quality-quantity regulation (QQR) is adopted, namely adjusting hydraulic and thermal conditions of heating system, the increase in operation flexibility is more remarkable. However, present IEHS dispatch models considering QQR overlook hydraulic characteristics of valves and variable frequency pumps resulting in inexecutable dispatch results easily and hindering IEHS's flexibility potential. Furthermore, loop networks and the access of multiple heat sources in heating systems may lead to flow reversal, but current research adopts a fixed flow direction which suppresses the potential of flow reversal to improve flexibility. In this paper, we propose an IEHS dispatch model considering QQR to promote renewable energy consumption, which considers hydraulic characteristics of valves and variable frequency pumps, and flow reversal. Specifically, we introduce two binary flow direction labels for every branch in heating system model and then the IEHS dispatch model which can deal with flow reversal is established. A sequential solution process combining linear and nonlinear optimization is formulated to overcome the non-convex feature of IEHS dispatch model. Specifically, piecewise linearization and piecewise McCormick relaxation are combined to handle complex nonlinear terms in the dispatch model. Therefore, a mixed integer linear programming model is obtained and solved, of which results are used as initial values for nonlinear optimization. Results in the case study show that operation cost is decreased by 0.97 % and renewable power consumption rate is increased from 83.31 % to 84.42 % after considering valve adjustment. Operation cost is further decreased by 6.06 % and renewable power consumption rate is increased to 94.04 % after considering flow reversal. • IEHS dispatch model with quality-quantity regulation of heating systems is proposed. • Operation characteristics of pumps and valves in heating system is considered. • Heating system model with flow reversal is constructed. • A sequential method combining linear and nonlinear optimization is formulated. • Impact of valve adjustment and flow reversal on IEHS dispatch is investigated. [ABSTRACT FROM AUTHOR]