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The development of modern intelligent manufacturing technology has become more and more widely used in robots, especially the use of 7-DOF robots in machine tool processing. In order to ensure the accuracy of the robot’s motion angle, the kinematics of the 7-DOF tandem robot is studied, and the forward kinematics and inverse kinematics are analysed. Firstly, the forward kinematics equation of the 7-DOF tandem robot is established by the spin theory method. Secondly, based on the forward kinematics equation, the geometric solution, the pose separation method and the shortest stroke are combined to solve the inverse solution of the robot. Finally, after simulation the kinematic angle function curve and the change value of the joint angle change curve are obtained. The result verifies the validity and correctness of the proposed method, and shows that the method has the characteristics of high precision and small calculation.

With the development of industries and advanced manufacturing technology, the performance of the machine tools plays an important role for the product quality. Nowadays five-axis linkage machining centers (5-axis MC) have been applied in the aerospace industry widely. During the thin-walled aircraft parts machining process of the 5-axis MC, the stochastic chatter phenomenon has important influence on the machining precision and quality of the parts. It has been the research hot point due to predict the 5-axis MC state hard with the complex operating process. “S” test work-piece(S test piece) is with the features of aerospace thin-walled parts. A State Prediction model for 5-axis MC based on the S surface finish is presented. Firstly, the movement principle of 5-axis MC during the S test piece processing is analyzed by using VERICUT Simulation tool; Then the information coupling method during geometry, operating process, monitoring and S test piece surface finish is built based on external export directions. By using the Geometry-Process-Monitor-Surface Quality information coupling method, the monitoring signal information data included vibration signals and acoustic emission signals are obtained while a S test piece finishing processing; the 5-axis MC state is predicted by using the ensemble empirical mode decomposition(EEMD) method with three indicators: fractal dimension (FD ),standard deviation (SD)and Power spectral entropy (PSE). By using a coordinate measuring machine (CMM) and a Scanning Electron Microscope to measure the machined S test piece, the actual geometry and surface finish of the S test piece can be obtained. Finally, the mapping relationship during the geometry-operating process-monitoring-surface finish of a S test piece are constructed. The prediction system is developed by using LABVIEW and MATLAB. This model is applied to the 5-axis MC and the experimental results show that this model will be the effective means for predicting the state of the five-axis machining center.

Wear is one important type of the common mechanical faults. The number and size information of wear metal particles in lubricating oil can reflect the wear fault state of mechanical equipment. Electromagnetic particle information detection is one important method of particle detection. It has the advantages of simple structure, fast response, small influence of external interference such as vibration and oil bubbles, and so on. In this paper, aiming at the difficulties of particle electromagnetic detection, the mathematical model of exciting magnetic field and induction electromotive force was analyzed. The structure of differential screw electromagnetic sensor was designed. The direct digital frequency synthesis method was used to generate the excitation signal. Based on STM32F103 and MK60DN512 DSP chips, the signal detection hardware system was designed. The information analysis software system was designed based on QT C++ language. The developed system can detect the minimum number of 180 mesh wear metal particles, and it can be the research condition for the analysis of mechanical equipment wear state, fault diagnosis and early fault warning.

Operation of the wind turbine is influenced by ever-changing wind speed and wind direction, and its transmission system running in a state of nonlinear and non-stationary operation. It is difficult and inaccurate to predict the amplitude of the vibration of the transmission system in real time. So the method, the trend prediction method of deterioration about equipment operation stability based on the degree of deterioration and quantification interval, is proposed in this paper. The vibration signal of the running state would be collected about the transmission system of the wind turbine; the concept, “the mean value of 1.5 dimensional spectral band energy”, is proposed. According to the concept of “the mean value of 1.5 dimension spectrum frequency band energy”, corresponding classification standard of vibration signal is established, and the degradation degree of running stability is quantified to obtain the quantification interval about the degree of deterioration. Then the state sequence of deterioration of the operation stability is established. The known observation sequence is predicted about tendency by using the improved superimposed Markov chain prediction method. In order to verify the effectiveness of the proposed method, measured vibration data experiment would be used to do verification test about the windfield wind turbine generator in working condition. The experimental results show that the development trend of the deterioration state could be got though the running stability deterioration trend prediction, which is consistent with the actual running state.

The prediction theories and approaches of stability deterioration trend were proposed through intensive studies against the operating stability and safety of complicated electromechanical systems in new energy equipment represented by conventional wind turbines, which provides the research foundation for improving the technical support level and scientific maintenance level of safety and stability of complicated electromechanical systems. In this research, the development rule of stability deterioration of electromechanical systems was revealed; the signal processing techniques for weak information of development trend of early failure were proposed; the feature extraction method for development trend of operating status of long-course load-variant electromechanical systems was proposed; and the prediction model and system for deterioration trend of operating stability of systems was constructed. The research achievement can be applied to the safety monitoring and fault warning of new energy equipment represented by wind turbines, which not only enhances the R&D level of related safety monitoring instruments, but also ensures safe and reliable operation of equipment and achieves scientific maintenance, improves the operation and management level of equipment, reduces the economic losses caused by failure, and promotes the utilization and development of green energy.

Concerning the problem of large rotating machinery like wind turbine which runs in low speed and non-stationary state, this research mainly focuses on separating fault trend feature from non-fault feature and the method of state deterioration evolution based on bi-spectrum. Firstly, the experimental signal such as low-speed startup vibration signal of rotor test rig in the normal state and a plurality of unbalanced state have been collected. Bi-spectrum method is applied to extract fault feature which submerged in complex background noise. On the basis of bi-spectrum analysis, the fault feature evolutionary matrix is defined to represent the state of equipment deterioration. The eigenvalues of fault feature evolutionary matrix are computed and fitted to a normal distribution curve, from which the mean value and variance are taken as fault feature parameters. Fault feature parameters are verified effectively by experiments. Finally, depending on fault feature parameters, graphical representation of state deterioration evolution is established. It is beneficial to provide guidelines for equipment deterioration trend. This method is applied to analyze the real vibration signal of wind turbine with the type of WD646/600 KW, and actual equipment condition verified the effectiveness of the proposed method.

Whether the large rotating electromechanical equipment running in safe and stable state has an important impact on the economy and society, so, this paper concerned on quantification method of fault deterioration based on 1.5-spectrum feature extraction method. Running stability deterioration feature extracting method based on higher-order cumulant diagonal slice has been discussed. Experiment data of varying degrees of deterioration under unbalance types of deterioration were collected from rotor test rig, and deterioration feature were extracted. The concept of frequency band mean energy based on 1.5-spectrum was proposed, and a quantification method based on this concept was proposed. 1.5 dimension frequency band mean energy was calculated to represent overall level of varying degrees of deterioration under unbalance types of deterioration, so, state deterioration sequence was obtained. The experiment shows that quantification method of fault deterioration is sensitive to the deterioration process of normal operation state changing to mild deterioration state; and it hastrend property to the overall deterioration process of normal operation state changing to severe deterioration state.

The photovoltaic power generation directly converts the solar energy into electric energy through photovoltaic cell, which is an important way to solve energy shortage and environmental pollution. In order to raise the reliability and reduce costs of grid-connected photovoltaic power station, we analyze types of common faults and causes, and specify numerous devices and disperse layout, design wireless monitoring diagnosis system to realize wireless monitor and control for grid-connected photovoltaic power station in order to ensure the safe operation.

In order to strengthen the security guarantee and improve the scientific management level of important equipments, the intelligent internet of things for equipment maintenance (IITEM) was presented in this paper. IITEM collects the static and dynamic information of electrical and mechanical equipments by all kinds of sensors, and standardizes various types of information, so as to suit the information transmission needs of the Internet of Things. According to the characteristics of mechanical and electrical equipment, IITEM makes intelligent processing of device information, including establishment of signal sample database and knowledge base, extraction of running status feature of complex equipment, state analysis, model building, fault diagnosis, fault prediction, etc. Centering on the functional objectives of IITEM, the demonstration project of IITEM was designed. It could realize online safety monitoring, fault prediction and comprehensive protection of key equipment groups, avoid economic losses and casualties, extend equipment maintenance cycle, and promote establishment of coding system, standards and norms related to the intelligent internet of things for equipment maintenance, which should be suitable for the Chinese Internet of Things.

Data process of large rotating machinery is in line with basic features of information fusion. A frame of fault diagnosis and prediction based on sensor information fusion is built. An improved extracting method of features is used to deal with the information fusion of single sensor, which raises the calculation efficiency and precision. The local fault prediction process is presented, and the fault deterioration trend is judged on the basis of dynamic weighted method. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

Data process of large rotating machinery is in line with basic features of information fusion. A framework of fault diagnosis and prediction based on sensor information fusion is built. An improved extracting method of features is used to deal with the information fusion of single sensor, which raises the calculation efficiency and precision. The local fault prediction process is presented, and the fault deterioration trend is judged on the basis of dynamic weighted method. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

In order to improve the development speed of measurement and control system for photovoltaic generation, a flexible integrated design platform and integrated design methods were presented. The platform integrated resources including software and hardware for designing the measurement and control system. Based on the platform, integrated methods were put forward and a new kind of measurement and control system for photovoltaic generation with sun tracking device was deigned. This control system combined time tracking method and photovoltaic tracking method and it integrated a fault knowledge base which was for system diagnosis. With the help of integrated platform, the cost and scale of the measurement and control system were reduced. The energy consumption of the power generation system was reduced and the reliability of the measurement and control system was improved.

Data process of large rotating machinery is in line with basic features of information fusion. The fault deterioration is extracted from the pattern spectrum as the fault feature, and its trend is predicted by the information fusion which is based on the dynamic weighted method for single-sensor and multi-sensors respectively. Actual example of Beijing Yanshan Petrochemical Co. shows the correction of conclusion.

Most parts of rotating machine rotate cyclically when the machine fails for some reason, and the vibration signal of rotating machine often has the cyclostation character. Aiming at this feature, Degree of Cyclostation(DCS) was presented to analyze the fault trend of rotating machine in this paper. DCS was constructed by the comparison between third-order non-stationary cyclic moments and stationary cyclic moments. It evaluated the cyclic frequency with the binary decision method and it could reflect the non-stationary character of vibration signal and suppress the noise and interference of the signal. In the experiments of analyzing the fault trend of rotating machine, DCS could get better trend result than the traditional vibration amplitude and vibration intensity.