The faults in switched reluctance motors(SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reason...The faults in switched reluctance motors(SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface(GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.展开更多
Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to compon...Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.展开更多
With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the ...With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation.Towards this,a number of scholars have pay attention to the fault diagnosis of WT gearbox.The efficiency of Machine Learning(ML)algorithms is highly correlated with signal type,data quality,and extracted features employed.The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms.More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms,while providing limited insights into the features of the acquired data.Therefore,it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms,and provide a detailed direction for future WT gearbox fault diagnosis technology research.In this paper,data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed.For the using of ML method in WT gearbox fault diagnosis,the data prepared for training is very important.The paper firstly reviewed the data analysing method which will support the ML method.The data analysing methods include data acquisition,data preprocessing and feature extraction method.Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis,as it serves as the essence of effective detection.This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis.Then typical ML method for WT gearbox fault diagnosis are carefully reviewed.Moreover,some prospects for future research directions are discussed in the end.展开更多
The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the av...The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data.However,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and laborious.As some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual machines.As a potential tool,transfer learning adapts a network trained in a source domain to its application in a target domain.Therefore,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes.In the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification.Various groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.展开更多
This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP...This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.展开更多
基金Project(No.KBü-BAP-C-11-D-003)supported by the Karabük University BAP Unit,Turkey
文摘The faults in switched reluctance motors(SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface(GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.
基金The Natural Sciences and Engineering Research Council of Canada(NSERC)the Department of National Defence(DND)under the Discovery Grant and DND Supplemental Programs。
文摘Ahealth monitoring scheme is developed in this work by using hybrid machine learning strategies to iden-tify the fault severity and assess the health status of the aircraft gas turbine engine that is subject to component degrada-tions that are caused by fouling and erosion.The proposed hybrid framework involves integrating both supervised recur-rent neural networks and unsupervised self-organizing maps methodologies,where the former is developed to extract ef-fective features that can be associated with the engine health condition and the latter is constructed for fault severity modeling and tracking of each considered degradation mode.Advantages of our proposed methodology are that it ac-complishes fault identification and health monitoring objectives by only discovering inherent health information that are available in the system I/O data at each operating point.The effectiveness of our approach is validated and justified with engine data under various degradation modes in compressors and turbines.
基金supported by the National Key R&D Program of China(Grant No.2020YFB1709800)Open Fund of the Intelligent Green Manufacturing Technology and Equipment Collaborative Innovation center of Shandong Province(IGSD-2020-017)+1 种基金ten thousand people plan project of Zhejiang ProvinceThe“Qizhen Program”of Zhejiang University.
文摘With the increasing of the installed capacity of wind power,the condition monitoring and maintains technique is becoming more important.Wind Turbines(WT)gearbox is one of the key wind power components as it plays the role of power transmission and speed regulation.Towards this,a number of scholars have pay attention to the fault diagnosis of WT gearbox.The efficiency of Machine Learning(ML)algorithms is highly correlated with signal type,data quality,and extracted features employed.The implementation of ML techniques has proven to be advantageous in simplifying the comprehension prerequisites for fault diagnosis technology concerning fault mechanisms.More and more current studies predominantly concentrate on the utilization and fine-tuning of ML algorithms,while providing limited insights into the features of the acquired data.Therefore,it is necessary to review the research in recent years from the perspective of the combination of feature extraction and ML algorithms,and provide a detailed direction for future WT gearbox fault diagnosis technology research.In this paper,data processing algorithms and typical fault diagnosis methods based on ML methods for WT gearbox are reviewed.For the using of ML method in WT gearbox fault diagnosis,the data prepared for training is very important.The paper firstly reviewed the data analysing method which will support the ML method.The data analysing methods include data acquisition,data preprocessing and feature extraction method.Feature extraction plays a pivotal role in the realm of gearbox fault diagnosis,as it serves as the essence of effective detection.This review will primarily focus on exploring methods that enable the utilization of efficient features in combination with ML techniques to achieve accurate gearbox fault diagnosis.Then typical ML method for WT gearbox fault diagnosis are carefully reviewed.Moreover,some prospects for future research directions are discussed in the end.
基金Natural Science Foundation of Shanghai (21ZR1425400)Shanghai Rising-Star Program (21QC1400200)+1 种基金National Natural Science Foundation of China (51977128)Shanghai Science and Technology Project (20142202600).
文摘The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data.However,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and laborious.As some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual machines.As a potential tool,transfer learning adapts a network trained in a source domain to its application in a target domain.Therefore,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes.In the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification.Various groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.
文摘This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.
