This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hier...This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.展开更多
>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in re...>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.展开更多
Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectr...Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.展开更多
文摘This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.
基金Project Supported by National Natural Science Foundation of China （ 50777069 ）.
文摘>Transformer faults are quite complicated phenomena and can occur due to a variety of reasons.There have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis applications.In order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this paper.The proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault diagnosis.Based on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert experience.The new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant information.The case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.
基金sponsored by the National Natural Science Foundation of China(No.51875105).
文摘Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods.To address this challenge,this paper proposes an effective local spectrum enhancement‐based diagnostic method that can identify weak fault frequencies in the original complicated raw signals.For this purpose,a traversal frequency band segmentation technique is first proposed for dividing the raw signal into a series of subfrequency bands.Then,the proposed synthetic quantitative index is constructed for selecting the most informative local frequency band(ILFB)containing fault features from the divided subfrequency bands.Furthermore,an improved grasshopper optimization algorithmbased stochastic resonance(SR)system is developed for enhancing weak fault features contained in the selected most ILFB with less computation cost.Finally,the enhanced weak fault frequencies are extracted from the output of the SR system using a common spectrum analysis.Two experiments on a laboratory planetary gearbox and an open bearing data set are used to verify the effectuality of the proposed method.The diagnostic results demonstrate that the proposed method can identify incipient faults of gears and bearings in an effective and accurate manner.Furthermore,the advantages of the proposed method are highlighted by comparison with other methods.
