The Experts below are selected from a list of 16326 Experts worldwide ranked by ideXlab platform
Amin Almasi - One of the best experts on this subject based on the ideXlab platform.
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Condition monitoring for Rotating Machinery
Chemical Engineering, 2012Co-Authors: Amin AlmasiAbstract:The article discusses the implementation of advanced condition monitoring of Rotating Machinery in chemical process industries (CPI) facilities. It states that advanced condition monitoring is being utilized to improve the operation and maintenance of workhorse components. It says that the CPI facilities implement condition monitoring and predictive-maintenance programs to assure that critical Rotating machines can be operated efficiently without an unscheduled shutdown.
Lin Wang - One of the best experts on this subject based on the ideXlab platform.
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Development of Vibration Monitoring Device for Rotating Machinery Based on $\mu$ C/OS-III
2019 16th International Multi-Conference on Systems Signals & Devices (SSD), 2019Co-Authors: Lin Wang, Bin Liu, Sidan DaiAbstract:The rotation speed and vibration amplitude of the Rotating Machinery during high-speed operation are two parameters that must be paid attention to, which directly reflects the operating state of the Rotating Machinery. According to the operating characteristics of Rotating Machinery, the development of Rotating Machinery vibration monitoring device based on embedded operating system is carried out. A single DSP is used as the core processor, and design the amplifier and AD acquisition circuit for full-scale range. Then transplant the μ C/OS-III embedded operating system on the DSP, and design subsection measurements and anti spike signal interference algorithm for rotation speed measurement. Realize the digitization of the speed sensor signal by AD conversion, and then perform FFT processing to obtain the vibration amplitude of the Rotating Machinery. The multi-task operation method is used to measure the two parameters (speed and vibration amplitude) of the Rotating Machinery to ensure the real-time performance of the parameter detection. Tested and verified, the device can measure the speed and vibration amplitude of Rotating Machinery, and the overall function, parameter measurement accuracy and real-time performance meet the requirements.
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SSD - Development of Vibration Monitoring Device for Rotating Machinery Based on $\mu$ C/OS-III
2019 16th International Multi-Conference on Systems Signals & Devices (SSD), 2019Co-Authors: Changsong Ma, Lin WangAbstract:The rotation speed and vibration amplitude of the Rotating Machinery during high-speed operation are two parameters that must be paid attention to, which directly reflects the operating state of the Rotating Machinery. According to the operating characteristics of Rotating Machinery, the development of Rotating Machinery vibration monitoring device based on embedded operating system is carried out. A single DSP is used as the core processor, and design the amplifier and AD acquisition circuit for full-scale range. Then transplant the $\mu\ C/OS-III$ embedded operating system on the DSP, and design subsection measurements and anti spike signal interference algorithm for rotation speed measurement. Realize the digitization of the speed sensor signal by AD conversion, and then perform FFT processing to obtain the vibration amplitude of the Rotating Machinery. The multi-task operation method is used to measure the two parameters (speed and vibration amplitude) of the Rotating Machinery to ensure the real-time performance of the parameter detection. Tested and verified, the device can measure the speed and vibration amplitude of Rotating Machinery, and the overall function, parameter measurement accuracy and real-time performance meet the requirements.
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Research on Control Method of Temperature Distribution of Rotor Sidewall of Rotating Machinery
2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Sys, 2018Co-Authors: Lin Wang, Fan LiAbstract:By investigating the existing temperature control technology and analyzing the technical characteristics of temperature controlling of the rotor sidewall of Rotating Machinery, this paper establishes a single-channel temperature closed-loop control model. Furthermore, a multi-channel temperature control structure is designed and related control strategies are formulated. In order to carry out the verification experiment, this paper builds a multi-channel temperature control experimental platform for the rotor sidewall of Rotating Machinery. The platform adopts the "LabVIEW + PID regulator" architecture to achieve active control of temperature distribution of the rotor sidewall. The experiment proves that the architecture of the experimental platform is indeed feasible, the temperature control precision is high, and it is safe and reliable. Therefore, the method for controlling the temperature distribution of the rotor sidewall of Rotating Machinery proposed in this paper is also indeed feasible. This work lays an important foundation for studying the relationship between the temperature distribution of the rotor sidewall of Rotating Machinery and its performance.
Qinghua Zhang - One of the best experts on this subject based on the ideXlab platform.
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ICIA - Fault diagnosis research of Rotating Machinery based on Dendritic Cell Algorithm
2015 IEEE International Conference on Information and Automation, 2015Co-Authors: Qinghua Zhang, Jianbin Xiong, Qinxue LiAbstract:Rotating Machinery play an important role in modern industry. Ensuring security and reliability of manufacture equipments has been receiving more and more recognition. This paper proposes an innovative techniques for Rotating Machinery fault diagnosis. The fault diagnosis system is comprised of clearly defined separate submodels including antigen submodel, memory submodel, DC submodel, analyse submodel and diagnositic submodel etc. Based on the system model, a novel Rotating Machinery fault diagnosis scheme based on Dendritic Cell Algorithm (DCA) and dimensionless parameter is proposed in this paper. To demonstrate our method, we apply our method to the real test bed of concurrent fault diagnosis for Rotating Machinery. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for Rotating Machinery and has high potential applications in real project.
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Fault diagnosis for Rotating Machinery based on artificial immune algorithm and evidence theory
The 27th Chinese Control and Decision Conference (2015 CCDC), 2015Co-Authors: Guoxi Sun, Qinghua Zhang, Qin Hu, Aisong Qin, Longqiu ShaoAbstract:Along with the continuous development of science and technology, the structures of Rotating Machinery become to be larger scale and more complicated, which results in higher probability of concurrent fault under actual working conditions. In order to achieve concurrent fault diagnosis for Rotating Machinery, an integrated method using artificial immune algorithm and evidence theory is proposed in this research work. The self-nonself recognition mechanism of artificial immune system for data analysis and processing has been derived from the negative selection algorithm. Five kinds of dimensionless immune detectors are generated based on negative selection algorithm, then the local diagnosis result of dimensionless immune detector is gotten. Combining with evidence theory fusion rules, the final diagnosis can be obtained. Experimental result demonstrates that the method can realize effectively concurrent fault diagnosis for Rotating Machinery.
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Fault diagnosis research of Rotating Machinery based on Dendritic Cell Algorithm
2015 IEEE International Conference on Information and Automation, 2015Co-Authors: Qinghua Zhang, Jianbin Xiong, Qinxue LiAbstract:Rotating Machinery play an important role in modern industry. Ensuring security and reliability of manufacture equipments has been receiving more and more recognition. This paper proposes an innovative techniques for Rotating Machinery fault diagnosis. The fault diagnosis system is comprised of clearly defined separate submodels including antigen submodel, memory submodel, DC submodel, analyse submodel and diagnositic submodel etc. Based on the system model, a novel Rotating Machinery fault diagnosis scheme based on Dendritic Cell Algorithm (DCA) and dimensionless parameter is proposed in this paper. To demonstrate our method, we apply our method to the real test bed of concurrent fault diagnosis for Rotating Machinery. Experimental result demonstrates that the method can realize effectively real-time fault diagnose for Rotating Machinery and has high potential applications in real project.
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concurrent fault diagnosis for Rotating Machinery based on vibration sensors
International Journal of Distributed Sensor Networks, 2013Co-Authors: Qinghua Zhang, Qin Hu, Xiaosheng SiAbstract:Rotating Machinery is widely used in modern industry. It is one of the most critical components in a variety of Machinery and equipment. Along with the continuous development of science and technology, the structures of Rotating Machinery become of larger scale, of higher speed, and more complicated, which results in higher probability of concurrent failure in practice. It is important to enable reliable, safe, and efficient operation of large-scale and critical Rotating Machinery, which requires us to achieve accurate diagnosis of concurrent fault, for example, rolling bearing diagnosis, gearbox diagnosis, and compressor diagnosis. In this paper, to achieve concurrent fault diagnosis for Rotating Machinery, which cannot be accurately diagnosed by existing methods, we develop an integrated method using artificial immune algorithm and evidential theory.
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Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method
IEEE Transactions on Control Systems Technology, 1Co-Authors: Jingfei Zhang, Qinghua Zhang, Xiao He, Donghua ZhouAbstract:Rotating Machinery plays an important role in large-scale equipment. The fault diagnosis of Rotating Machinery is of great significance and can increase industrial safety. Up until now, most existing fault diagnosis techniques have been proposed under the condition that only a single fault will occur at the same time. However, in industrial applications, compound faults are more common to take place due to the tight coupling of different components. To diagnosis compound faults accurately is of great significance to the safe operation of industrial equipment. A fused imbalance learning method is proposed in this article exploiting the nonlinear-mapping ability of neural networks. The dimensionless parameterization combined with time-frequency transformation method is utilized to extract data features and construct different evidence sources. Basic probability assignment with nested structure is generated from a novel weighted extreme learning machine based on sensitivity analysis. Evidence combination is implemented to obtain a final inference about the compound-fault class. Experiments are conducted on a large Rotating Machinery fault diagnosis experimental platform. Both single faults and compound faults in bearings and wheel gears of the large Rotating Machinery are considered. Experimental results illustrate the effectiveness of the proposed method.
Chang Tao Mo - One of the best experts on this subject based on the ideXlab platform.
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a method for intelligent fault diagnosis of Rotating Machinery
Digital Signal Processing, 2004Co-Authors: Chang Zheng Chen, Chang Tao MoAbstract:Abstract This paper presents an intelligent methodology for diagnosing incipient faults in Rotating Machinery. In this fault diagnosis system, wavelet transform techniques are used in combination with a function approximation model to extract fault features. Wavelet neural networks are also constructed. The main contributions of this paper are as follows: First, a wavelet theory based on a nonlinear adaptive algorithm is developed for an excitation function approximation of neural networks. Preprocessing of a single fault signal is required to perform diagnosis using an intelligent system. Second, a neural network classifier for identifying the faults is developed. The system is scalable to different Rotating Machinery and has been successfully demonstrated with a turbine generator unit.
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A method for intelligent fault diagnosis of Rotating Machinery
Digital Signal Processing: A Review Journal, 2004Co-Authors: Chang Zheng Chen, Chang Tao MoAbstract:This paper presents an intelligent methodology for diagnosing incipient faults in Rotating Machinery. In this fault diagnosis system, wavelet transform techniques are used in combination with a function approximation model to extract fault features. Wavelet neural networks are also constructed. The main contributions of this paper are as follows: First, a wavelet theory based on a nonlinear adaptive algorithm is developed for an excitation function approximation of neural networks. Preprocessing of a single fault signal is required to perform diagnosis using an intelligent system. Second, a neural network classifier for identifying the faults is developed. The system is scalable to different Rotating Machinery and has been successfully demonstrated with a turbine generator unit. ?? 2004 Elsevier Inc. All rights reserved.
Qu Liangsheng - One of the best experts on this subject based on the ideXlab platform.
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Rub failure signature analysis for large Rotating Machinery
Mechanical Systems and Signal Processing, 1990Co-Authors: He Zhengjia, Sheng Yudi, Qu LiangshengAbstract:In many cases rub failure in large Rotating Machinery may produce noise in certain bandwidths known as coloured noise. It is difficult to diagnose rub failure between Rotating and stationary parts using only ordinary FFT spectra or autoregressive spectra. In this paper, a new technique combining principal components analysis and autoregressive spectra (PCAT) is introduced. This technique can reasonably estimate the spectra and determine the chief characteristic parameter of the coloured noise. A successful example of rub failure analysis given in this paper shows that PCAT is an efficient method for use with large Rotating Machinery. © 1990.
