Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are...Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification model.Aiming to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns.First,an unsupervised clustering technique is used to construct an initial identification model.Second,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response.Third,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification model.The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications.As an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes.Therefore,this method can be flexibly and efficiently used for performance pattern identification of the CESs.Moreover,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.展开更多
Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and ide...Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities.However,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of products.Herein,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information.In this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA information.Subsequently,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution.Finally,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method.An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.展开更多
In order to recognize the different operating conditions of a distributed and complex electromechanical system in the process industry,this work proposed a novel method of condition recognition by combining complex ne...In order to recognize the different operating conditions of a distributed and complex electromechanical system in the process industry,this work proposed a novel method of condition recognition by combining complex network theory with phase space reconstruction.First,a condition-space with complete information was reconstructed based on phase space reconstruction,and each condition in the space was transformed into a node of a complex network.Second,the limited penetrable visibility graph method was applied to establish an undirected and un-weighted complex network for the reconstructed condition-space.Finally,the statistical properties of this network were calculated to recognize the different operating conditions.A case study of a real chemical plant was conducted to illustrate the analysis and application processes of the proposed method.The results showed that the method could effectively recognize the different conditions of electromechanical systems.A complex electromechanical system can be studied from the systematic and cyber perspectives,and the relationship between the network structure property and the system condition can also be analyzed by utilizing the proposed method.展开更多
In this paper,the results of experimental study of dispersion characteristics of complex electromechanical parameters of ferroelectrically“hard”porous piezoceramics based on PZT composition were presented.Experiment...In this paper,the results of experimental study of dispersion characteristics of complex electromechanical parameters of ferroelectrically“hard”porous piezoceramics based on PZT composition were presented.Experimental samples of porous piezoceramics were fabricated using a modified method of burning-out a pore former.The complex constants of porous piezoceramics with relative porosity 16%and their frequency dependences were measured using the piezoelectric resonance analysis method.As a result of experimental studies,regions of elastic,piezoelectric and electromechanical dispersion,characterized by anomalies in the frequency dependences of the imaginary and real parts of the complex constants of porous piezoelectric ceramics were found.It was revealed also that the microstructural features of porous piezoceramics determine the character of frequency dependences of complex electromechanical parameters of porous piezoelectric ceramics.In conclu-sion,the microstructural and physical mechanisms of electromechanical losses and dispersion in porous piezoceramics were discussed.展开更多
Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between moni...Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.展开更多
基金supported in part by the National Key R&D Program of China(Grant No.2017YFF0210500)in part by China Postdoctoral Science Foundation(Grant No.2017M620446)
文摘Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification model.Aiming to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns.First,an unsupervised clustering technique is used to construct an initial identification model.Second,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response.Third,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification model.The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications.As an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes.Therefore,this method can be flexibly and efficiently used for performance pattern identification of the CESs.Moreover,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.
基金Supported by National Nat ural Science Foundation of China(Grant Nos.51675227,51975249)Jilin Province Science and Technology Development Funds(Grant Nos.20180201007GX,20190302017GX)+2 种基金Technology Development and Research of Jilin Province(Grant No.2019C037-01)Changchun Science and Technology Planning Project(Grant No.19SS011)National Science and technology Major Project(Grant No.2014ZX04015031).
文摘Heavy-duty machine tools are composed of many subsystems with different functions,and their reliability is governed by the reliabilities of these subsystems.It is important to rank the weaknesses of subsystems and identify the weakest subsystem to optimize products and improve their reliabilities.However,traditional ranking methods based on failure mode effect and critical analysis(FMECA)does not consider the complex maintenance of products.Herein,a weakness ranking method for the subsystems of heavy-duty machine tools is proposed based on generalized FMECA information.In this method,eight reliability indexes,including maintainability and maintenance cost,are considered in the generalized FMECA information.Subsequently,the cognition best worst method is used to calculate the weight of each screened index,and the weaknesses of the subsystems are ranked using a technique for order preference by similarity to an ideal solution.Finally,based on the failure data collected from certain domestic heavy-duty horizontal lathes,the weakness ranking result of the subsystems is obtained to verify the effectiveness of the proposed method.An improved weakness ranking method that can comprehensively analyze and identify weak subsystems is proposed herein for designing and improving the reliability of complex electromechanical products.
基金supported by the National Natural Science Foundation of China (Grant by No. 51175402)
文摘In order to recognize the different operating conditions of a distributed and complex electromechanical system in the process industry,this work proposed a novel method of condition recognition by combining complex network theory with phase space reconstruction.First,a condition-space with complete information was reconstructed based on phase space reconstruction,and each condition in the space was transformed into a node of a complex network.Second,the limited penetrable visibility graph method was applied to establish an undirected and un-weighted complex network for the reconstructed condition-space.Finally,the statistical properties of this network were calculated to recognize the different operating conditions.A case study of a real chemical plant was conducted to illustrate the analysis and application processes of the proposed method.The results showed that the method could effectively recognize the different conditions of electromechanical systems.A complex electromechanical system can be studied from the systematic and cyber perspectives,and the relationship between the network structure property and the system condition can also be analyzed by utilizing the proposed method.
基金The study was financially supported by the Ministry of Science and Higher Education of the Russian Federation[State task in the field of scientific activity,Scientific Project No.0852-2020-0032(BAS0110/20-3-08IF)].
文摘In this paper,the results of experimental study of dispersion characteristics of complex electromechanical parameters of ferroelectrically“hard”porous piezoceramics based on PZT composition were presented.Experimental samples of porous piezoceramics were fabricated using a modified method of burning-out a pore former.The complex constants of porous piezoceramics with relative porosity 16%and their frequency dependences were measured using the piezoelectric resonance analysis method.As a result of experimental studies,regions of elastic,piezoelectric and electromechanical dispersion,characterized by anomalies in the frequency dependences of the imaginary and real parts of the complex constants of porous piezoelectric ceramics were found.It was revealed also that the microstructural features of porous piezoceramics determine the character of frequency dependences of complex electromechanical parameters of porous piezoelectric ceramics.In conclu-sion,the microstructural and physical mechanisms of electromechanical losses and dispersion in porous piezoceramics were discussed.
基金supported by the National Natural Science Foundation of China(Grant No.51375375)
文摘Abnormal conditions are hazardous in complex process systems, and the aim of condition recognition is to detect abnormal conditions and thus avoid severe accidents. The relationship of linkage fluctuation between monitoring variables can characterize the operation state of the system. In this study,we present a straightforward and fast computational method, the multivariable linkage coarse graining(MLCG) algorithm, which converts the linkage fluctuation relationship of multivariate time series into a directed and weighted complex network. The directed and weighted complex network thus constructed inherits several properties of the series in its structure. Thereby, periodic series convert into regular networks, and random series convert into random networks. Moreover, chaotic time series convert into scale-free networks. It demonstrates that the MLCG algorithm permits us to distinguish, identify, and describe in detail various time series. Finally, we apply the MLCG algorithm to practical observations series, the monitoring time series from a compressor unit, and identify its dynamic characteristics. Empirical results demonstrate that the MLCG algorithm is suitable for analyzing the multivariable linkage fluctuation relationship in complex electromechanical system. This method can be used to detect specific or abnormal operation condition, which is relevant to condition identification and information quality control of complex electromechanical system in the process industry.
