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This paper introduces the current situation of intelligent development of shearer at home and abroad. The shearer is currently developing towards intelligent robotics and is in the primary stage of intelligence. This paper elaborates on the development process and trends of intelligent shearer. There are six scientific research projects that have played important roles in the intelligent development of shearer, including the UK remotely operated longwall face (ROLF) program, mine operating system (MINOS), NASA long wall automation cooperation project, Australian Australian coal industry’s research program (ACARP), Australian commonwealth scientific and industrial research organization (CSIRO) project, and EU new mechanisation and automation of longwall and drivage equipment (NEMAEQ). The development trend of shearer is to use continuously developing monitoring, sensing, and remote control technologies to replace manual intervention and operation, continuously improve the production efficiency and reliability of working face equipment, and develop towards informationization, integration, unmanned, and intelligent direction. This paper introduces the key technologies of intelligent perception, intelligent control, and intelligent communication of shearer. It points out that intelligent cutting technology based on coal rock recognition, intelligent drum speed regulation technology, precise positioning technology of shearer, and planning and mining technology based on three-dimensional geological models are key technologies that urgently need to be broken through. The human-machine relationship that cannot be ignored in the intelligent development process of shearer is discussed from two aspects: dust reduction and noise reduction.

The health status of the planetary gears in the cutting section of shearer's rocker arm directly affects the cutting efficiency. The strong noise interference caused by multiple impacts during the cutting of coal and rock by the shearer, the complex gear structure, and the variable transmission path make it difficult to extract fault features. In order to solve the above problems, a fault diagnosis method for planetary gears in shearer based on spectral average denoising and correlation spectrum is proposed. Based on the distribution features of signal spectrum and the random features of noise, the spectrum average denoising method is adopted to suppress the interference of noise on the signal spectrum and obtain the signal denoising spectrum. The method constructs relevant spectra to establish the intrinsic relationship between few sample denoising spectra and multi sample denoising spectra, and reduce the demand for sample size for average spectrum denoising. The method uses a one-dimensional convolutional neural network (1D CNN) to establish an accurate mapping relationship between correlation spectra and fault categories, with correlation spectra as input and fault categories as output, to achieve planetary gear fault classification and recognition. The experimental verification of the fault diagnosis method for planetary gears in shearer based on spectral average denoising and correlation spectrum is carried out on the drivetrain diagnostics simulator transmission system fault diagnosis experimental platform. The results show that the method can enhance the key frequency that characterizes the fault features. The overall recognition rate for five types of health status signals of planetary gears, including normal, broken teeth, wear, missing teeth, and cracks, reaches 96%. Gear fault diagnosis can be effectively and accurately achieved when the signal-to-noise ratio is not less than 15 dB.

Energy harvesting is the process of converting external environmental energy into electrical energy. This technology has certain development potential in realizing self-powered low-power wireless sensors. At present, there are few researches on the adaptability of energy harvesting systems in terms of the application of energy harvesting technology in shears. Therefore, it is unable to achieve reliable applications. In order to solve the above problems, based on the characteristics of the working environment of shearer, this paper analyzes the feasibility of the weak environmental energy being harvested by shearer and the feasibility of converting the energy into electrical energy. It is pointed out that light energy, temperature difference heat energy and vibration energy are the three typical environmental energies that exist in shearer. Theses three energies have different adaptability due to different energy characteristics. Light energy has poor adaptability and is not suitable as an environmental energy source in energy harvesting technology. Temperature difference energy source is stable and the thermoelectric power generation device is easy to install. This energy has certain adaptability. However, the thermoelectric power generation device needs to be installed in the main heat producing part of shearer, and the installation location has certain restrictions. The total amount of vibration energy is large, and the piezoelectric power generation device has a simple structure. Vibration energy is less affected by the working environment factors and has a strong adaptability without installation location restrictions.

In view of the problems of lower stability and poor precision existed in present shearer height-adjustment system, which used electromagnetic switch valve to adjust height of drum, a model predictive control model of the shearer height-adjustment system was established through adding model predictive controller in conventional model of the shearer height-adjusting system and taking MG300/700-WD type AC traction shearer as the research object. The switching frequency of electromagnetic switch valve and working hours were reduced by adjusting sampling time and predictive time domain length of model predictive controller, the drum can be realize accurate and real time height adjustment according to the target height. Simulation results show that the shearer height-adjustment system based on model predictive control model can achieve stability in 6 s after given input compared with the conventional model, switch frequency of the electromagnetic switch valve is reduced, overshoot amount is decreased by 28.8%, stability and real-time performance of the system are improved.

In order to solve problems that conventional time-frequency domain analysis methods were sensitive to noise and had poor adaptability of energy change in coal-rock interface identification based on vibration signal analysis, a vibration signal identification method of coal-rock cutting of shearer based on cepstral distance was proposed. By analyzing cutting vibration signals of shearer acquired by vibration sensor under different load conditions, following conclusions are gotten. Compared with condition of cutting rock, the cepstral distance between vibration signal under condition of cutting coal and standard signal under no load condition is larger. The cepstral distance of the vibration signal under condition of rock cutting is obviously periodic, and the cycle is one rotation cycle of shearer drum, while the vibration signal under condition of cutting coal has no periodic feature. The industrial test result shows that the method has identification accuracy of 75% when hardness difference between coal and rock is more than 10 MPa.

For problems of single shearer control function existed in most terminal operation stations for shearer and single displaying form existed in a few productions, a new type of terminal operation station for shearer with true color graphic display function was designed, and design schemes of software and hardware of the terminal operation station were introduced. The terminal operation station has been applied in a shearer resulting in fast response, reliable operation, clear displaying and smooth picture switching as well as few energy consumption of a single terminal operation station of less than 2.5 W.

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings. [ABSTRACT FROM AUTHOR]

Abstract: Disocclusions and ghost contours are two typical artifacts in depth image-based rendering (DIBR). This paper explores a new view synthesis method to eliminate image artifacts. First, we combine depth discontinuities with virtual view location to divide the depth map into reliable regions and unreliable regions. Second, we use forward warping and inverse warping to deal with the two different regions respectively. Finally, a distance-depth-based inpainting algorithm is used to enhance the quality of the synthesized image. Both objective and subjective quality have been analyzed and the results were compared with the other three state-of-the-art view synthesis methods. It is indicated that the proposed view synthesis technique has notable advantages at artifacts elimination and PSNR improvement. [Copyright &y& Elsevier]

The circular disk is a basic mechanical element, which is used widely in industry such as in turbine machines, circular saws and computer memories. Considering the requirements for an actuator to control the transverse vibration of a circular disk, a non-contact electromagnetic actuator which can produce a large actuating force is used, and a complete control system which is composed of analog components is built. Good experimental results are obtained by using the control system to control the transverse vibration of a circular disk. [ABSTRACT FROM AUTHOR]

The carburizing and coking of ethylene cracking furnace tubes are the important factors that affect the energy efficiency of ethylene production. To realize the diagnosis and prediction of the different coking degrees of cracking furnace tubes, and then take corresponding treatment measures, is of great significance for improving ethylene production and prolonging the service life of the furnace tube. Therefore, a fusion diagnosis and prediction method based on artificial bee colony (ABC) and adaptive neural fuzzy inference system (ANFIS) is proposed, which also introduces a coking-time factor (CTF). The actual data verification shows that the method not only improves the training efficiency and diagnosis accuracy of the coking diagnosis and inference system of the cracking furnace tube, but also realizes the prediction of the development trend of the coking degree of the furnace tube. [ABSTRACT FROM AUTHOR]