The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.展开更多
The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is often premised on big data and class-balance.However,due to the limitation of working environment,operating conditions and equi...The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is often premised on big data and class-balance.However,due to the limitation of working environment,operating conditions and equipment status,the fault data collected by mechanical equipment are often small and imbalanced with normal samples.Therefore,in order to solve the abovementioned dilemma faced by the fault diagnosis of practical mechanical equipment,an auxiliary generative mutual adversarial network(AGMAN)is proposed.Firstly,the generator combined with the auto-encoder(AE)constructs the decoder reconstruction feature loss to assist it to complete the accurate mapping between noise distribution and real data distribution,generate highquality fake samples,supplement the imbalanced dataset to improve the accuracy of small sample class-imbalanced fault diagnosis.Secondly,the discriminator introduces a structure with unshared dual discriminators.Realize the mutual adversarial between the dual discriminator by setting the scoring criteria that the dual discriminator are completely opposite to the real and fake samples,thus improving the quality and diversity of generated samples to avoid mode collapse.Finally,the auxiliary generator and the dual discriminator are updated alternately.The auxiliary generator can generate fake samples that deceive both discriminators at the same time.Meanwhile,the dual discriminator cannot give correct scores to the real and fake samples according to their respective scoring criteria,so as to achieve Nash equilibrium.Using three different test-bed datasets for verification,the experimental results show that the proposed method can explicitly generate highquality fake samples,which greatly improves the accuracy of class-unbalanced fault diagnosis under small sample,especially when it is extremely imbalanced,after using this method to supplement fake samples,the fault diagnosis accuracy of DCNN and SAE are relatively big improvements.So,the proposed method provides an effective solution for small sample class-unbalanced fault diagnosis.展开更多
Significant progress has been made in machine learning with large amounts of clean labels and static data.However,in many real-world applications,the data often changes with time and it is difficult to obtain massive ...Significant progress has been made in machine learning with large amounts of clean labels and static data.However,in many real-world applications,the data often changes with time and it is difficult to obtain massive clean annotations,that is,noisy labels and time series are faced simultaneously.For example,in product-buyer evaluation,each sample records the daily time behavior of users,but the long transaction period brings difficulties to analysis,and salespeople often erroneously annotate the user’s purchase behavior.Such a novel setting,to our best knowledge,has not been thoroughly studied yet,and there is still a lack of effective machine learning methods.In this paper,we present a systematic approach RTS both theoretically and empirically,consisting of two components,Noise-Tolerant Time Series Representation and Purified Oversampling Learning.Specifically,we propose reducing label noise’s destructive impact to obtain robust feature representations and potential clean samples.Then,a novel learning method based on the purified data and time series oversampling is adopted to train an unbiased model.Theoretical analysis proves that our proposal can improve the quality of the noisy data set.Empirical experiments on diverse tasks,such as the house-buyer evaluation task from real-world applications and various benchmark tasks,clearly demonstrate that our new algorithm robustly outperforms many competitive methods.展开更多
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
基金co-supported by the Special Project of the National Key Research and Development Program of China (No. 2020YFB1709801)the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. KYCX21_0230)+1 种基金the National Natural Science Foundation of China (No. 51975276)the National Science and Technology Major Project (No. 2017-Ⅳ-0008-0045).
文摘The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is often premised on big data and class-balance.However,due to the limitation of working environment,operating conditions and equipment status,the fault data collected by mechanical equipment are often small and imbalanced with normal samples.Therefore,in order to solve the abovementioned dilemma faced by the fault diagnosis of practical mechanical equipment,an auxiliary generative mutual adversarial network(AGMAN)is proposed.Firstly,the generator combined with the auto-encoder(AE)constructs the decoder reconstruction feature loss to assist it to complete the accurate mapping between noise distribution and real data distribution,generate highquality fake samples,supplement the imbalanced dataset to improve the accuracy of small sample class-imbalanced fault diagnosis.Secondly,the discriminator introduces a structure with unshared dual discriminators.Realize the mutual adversarial between the dual discriminator by setting the scoring criteria that the dual discriminator are completely opposite to the real and fake samples,thus improving the quality and diversity of generated samples to avoid mode collapse.Finally,the auxiliary generator and the dual discriminator are updated alternately.The auxiliary generator can generate fake samples that deceive both discriminators at the same time.Meanwhile,the dual discriminator cannot give correct scores to the real and fake samples according to their respective scoring criteria,so as to achieve Nash equilibrium.Using three different test-bed datasets for verification,the experimental results show that the proposed method can explicitly generate highquality fake samples,which greatly improves the accuracy of class-unbalanced fault diagnosis under small sample,especially when it is extremely imbalanced,after using this method to supplement fake samples,the fault diagnosis accuracy of DCNN and SAE are relatively big improvements.So,the proposed method provides an effective solution for small sample class-unbalanced fault diagnosis.
基金the National Key R&D Program of China(2022YFC3340901)the National Natural Science Foundation of China(Grant No.62176118)。
文摘Significant progress has been made in machine learning with large amounts of clean labels and static data.However,in many real-world applications,the data often changes with time and it is difficult to obtain massive clean annotations,that is,noisy labels and time series are faced simultaneously.For example,in product-buyer evaluation,each sample records the daily time behavior of users,but the long transaction period brings difficulties to analysis,and salespeople often erroneously annotate the user’s purchase behavior.Such a novel setting,to our best knowledge,has not been thoroughly studied yet,and there is still a lack of effective machine learning methods.In this paper,we present a systematic approach RTS both theoretically and empirically,consisting of two components,Noise-Tolerant Time Series Representation and Purified Oversampling Learning.Specifically,we propose reducing label noise’s destructive impact to obtain robust feature representations and potential clean samples.Then,a novel learning method based on the purified data and time series oversampling is adopted to train an unbiased model.Theoretical analysis proves that our proposal can improve the quality of the noisy data set.Empirical experiments on diverse tasks,such as the house-buyer evaluation task from real-world applications and various benchmark tasks,clearly demonstrate that our new algorithm robustly outperforms many competitive methods.
