Xinhua Yang

A novel collaborative optimization algorithm in solving complex optimization problems

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment

Display Omitted An improved adaptive PSO based on Alpha-stable distribution and dynamic fractional calculus is studied.A new multi-objective optimization model of gate assignment problem is proposed.The actual data are used to demonstrate the effectiveness of the proposed method. Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

Study on EAI Based on Web Services and SOA

With the advent of Web services and SOA (service-oriented architecture), it seems to be feasible to realize EAI (enterprise application integration) and automatic inter-enterprise interactions. This paper combines SOA and Web service technology which simplify the application integration into the development and using of services, solve the connectivity of the isomerous platform, security, and the loose coupling between systems, as well as the refactoring and optimization of the processes. It integrates the isomerous enterprise systems, applications, and business processes and composing application environment of the data sources as a whole system. In addition, the technique standards, such as SOAP, WSDL, BPEL, WDDI, etc., are studied. The various key components of SOA are integrated and implemented. By studying the service-oriented software analyzing and development characteristics, an EAI system with great interoperability, reusability, flexibility is to be realized.

Research on vibration suppression method of alternating current motor based on fractional order control strategy

At present, the changing structure, material and increasing device are used to suppress the vibration of motor in general. These methods increase system complexity in the different degree. So a novel vibration suppression method based on fractional order Proportional-Integral-Derivative (PID) controller is proposed in this article. First, the digital realization process of fractional order PID controller is illustrated in detail. Then the integer order PID controller and fractional order PID controller are, respectively, used to adjust the input current of inverter to control the 1.5 kW alternating current motor. The vibration frequency spectrums and stator current frequency spectrums in low-frequency and carrier frequency band are, respectively, studied by using the comparison and analysis methods. At the same time, the vibration frequency spectrum and stator current frequency spectrum of 15 kW alternating current motor are compared and analyzed. And the frequency spectrums near the rotating frequency of stator current of 1.5 kW and 15 kW alternating current motors are amplified to deeply analyze spectrum characteristics. The experimental results show that the fractional order PID controller has the characteristics of multi-point control by comparing with the integer order PID controller. It changes the frequency components of stator current, and then the electromagnetic torque is more stable. So, the fractional order PID controller can better suppress the vibration of alternating current motor. The proposed method can provide a new idea for vibration suppression of rotating machinery.

A novel fault analysis and diagnosis method based on combining computational intelligence methods

In order to improve the correctness and efficiency of fault diagnosis, a novel hybrid intelligence method based on integrating rough set, genetic algorithms, and radial basic function neural network (RGRN) was proposed for motor fault diagnosis in the complicated CNC system in this article. In the proposed RGRN method, combination and condition supplement algorithm was used to deal with the incomplete fault data and the original data were discretized using genetic algorithms to construct a decision table. Rough set theory as a new mathematical tool was used to eliminate the redundant and irrelevant attributes in order to obtain the minimum rule set for reducing the number of input nodes of the radial basic function neural network. Genetic algorithms were directly used to optimize the structure and weights of radial basic function neural network to establish an optimized radial basic function neural network (GRN) model; then, the minimum rule set was inputted into the GRN model in order to obtain the optimized RGRN model. Finally, the completed fault symptom information was inputted into the RGRN model to obtain the fault diagnosis results. The robustness of the RGRN method was tested. Simulating experiments on motor fault diagnosis in the complicated CNC system show the RGRN method not only improves the global optimization performance and quickens the convergence speed, but also obtains the robust solution with a better quality.

A Fault Feature Extraction Method for Motor Bearing and Transmission Analysis

Roller bearings are the most widely used and easily damaged mechanical parts in rotating machinery. Their running state directly affects rotating machinery performance. Empirical mode decomposition (EMD) easily occurs illusive component and mode mixing problem. From the view of feature extraction, a new feature extraction method based on integrating ensemble empirical mode decomposition (EEMD), the correlation coefficient method, and Hilbert transform is proposed to extract fault features and identify fault states for motor bearings in this paper. In the proposed feature extraction method, the EEMD is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with different frequency components. Then the correlation coefficient method is used to select the IMF components with the largest correlation coefficient, which are carried out with the Hilbert transform. The obtained corresponding envelope spectra are analyzed to extract the fault feature frequency and identify the fault state by comparing with the theoretical value. Finally, the fault signal transmission performance of vibration signals of the bearing inner ring and outer ring at the drive end and fan end are deeply studied. The experimental results show that the proposed feature extraction method can effectively eliminate the influence of the mode mixing and extract the fault feature frequency, and the energy of the vibration signal in the bearing outer ring at the fan end is lost during the transmission of the vibration signal. It is an effective method to extract the fault feature of the bearing from the noise with interference.

Modularized fault diagnosis model of induction motor based on radial basis function neural network

A modular fault diagnosis model of induction motor is proposed based on radial basis function neural network. The modular structure makes model configuration flexible, training time short and model convergence easier. The training algorithm of the fault diagnosis model is given. Through an example, factors of the affecting fault diagnosis model classification are analyzed, methods of feature extraction and feature enhance are discussed, and the algorithm of feature enhance is presented. Lastly, the construction, training and verification of the motor fault diagnosis model consisting of two sub-models are introduced. Research results show that because of adopting the modular model and using a sub-model to recognize a fault state, model training becomes easier. It is more important that the fault recognition ability and application flexibility of the fault diagnosis model proposed are improved obviously.

Research on a new information fusion method based on SVM and SCQPSO and its application

The optimal parameters of the support vector machine (SVM) are very important for accuracy modeling and generalization performance. The quantum particle swarm optimization (QPSO) algorithm takes on the characteristics of the rapid global optimization, scale chaos method provides the characteristics of the fast convergence and the SVM has the characteristics of the nonlinear fitting. These advantages of the scale chaos method and the QPSO algorithm are used to propose a scale chaos QPSO (SCQPSO) algorithm. Then the SCQPSO algorithm is used to optimize the parameters of the SVM model. A new information fusion method based the SCQPSO algorithm and the SVM model (SCQPSO-SVM) is proposed in this paper. The SCQPSO-SVM algorithm uses the global optimization ability of the SCQPSO algorithm to comprehensively optimize the penalty coefficient, kernel parameter and hybrid weight of the SVM model. The goal is to improve the solved speed and solution accuracy of the SVM model. The SCQPSO-SVM algorithm is applied in the testing function and the rotor fault diagnosis of traction motor. The experimental results show that the SCQPSO algorithm can search for the good optimization results and the SCQPSO-SVM algorithm can reduce the error rate of the fusion recognition. So the SCQPSO-SVM algorithm takes on better generalization performance and prediction accuracy in the real application.At the request of the Editor, the Publisher and the corresponding author, the following article Research on a new information fusion method based on SVM and SCQPSO and its application by Wu Deng, Xinhua Yang, Huimin Zhao, Li Zou, Zhengguang Li and Wen Li Journal of Vibration and Control first published on March 19, 2014, DOI: 10.1177/1077546314521447 has been retracted. SAGE and the journal Editor became aware of author misconduct concerning fabricated identities as well as the impersonation of legitimate individuals to manipulate the peer review process. SAGE and the journal Editor immediately launched an investigation and have decided to retract the article for reasons of author misconduct. SAGE regrets that the corresponding author compromised the academic record by perverting the peer review process and apologises to readers.

Flexible Application System Integration Based on Agent-Enabled SOA in the Industrial Field

With the development of Chinese industry, it is more and more important that information infrastructure as the mean to bring together different software applications is the key technology to enable cooperation and information and knowledge exchange in an open industrial environment. In order to resolve the deficiency of traditional enterprise application integrity, this article describes the business process using agent-enabled SOA in the flexible industrial application integration.And agent-enabled SOA plays an important role for service integration. A flexible application integration framework based on combining Web services and intelligent agent technology is to be presented.This framework shows unification of agent and Web service models. Finally the paper describes the flexible application system integration architecture and the approach shows the great interoperability, reusability, agility by an industrial application from the auto drilling.