本文選題：緩變故障 + 早期診斷��； 參考：《山東大學(xué)學(xué)報(bào)(工學(xué)版)》2017年05期

【摘要】：為了克服傳統(tǒng)的早期微小故障診斷方法不能區(qū)分多個(gè)不同時(shí)刻發(fā)生故障的不足,提出一種將深度學(xué)習(xí)和PCA相結(jié)合的方法實(shí)現(xiàn)微小緩變故障早期診斷及壽命預(yù)測。對采集的數(shù)據(jù)進(jìn)行深度學(xué)習(xí)實(shí)現(xiàn)逐層特征抽取,學(xué)習(xí)早期微小故障特征,建立微小緩變故障早期診斷模型,結(jié)合PCA方法將深度學(xué)習(xí)所抽取的高維故障特征向量集成為一個(gè)故障特征變量,根據(jù)歷史故障數(shù)據(jù)特征變量演化規(guī)律定義數(shù)據(jù)驅(qū)動的故障演變標(biāo)尺,并通過指數(shù)型非線性擬合方法建立壽命預(yù)測模型。選取TE平臺數(shù)據(jù)進(jìn)行算法有效性檢驗(yàn),并與其他算法對比,從而驗(yàn)證了所提出算法的有效性。
[Abstract]:In order to overcome the shortcoming that the traditional early micro fault diagnosis method can not distinguish the fault at many different times, a method combining depth learning and PCA is proposed to realize the early diagnosis and life prediction of small and slow variable faults. The data are deeply studied to extract features from each layer, to learn the features of small faults in the early stage, and to establish a model for early diagnosis of small and slowly changing faults. Combined with PCA method, the high dimensional fault feature vector extracted by depth learning is integrated into a fault feature variable, and a data-driven fault evolution scale is defined according to the evolution rule of historical fault data feature variables. The life prediction model is established by exponential nonlinear fitting method. The data of te platform are selected to verify the validity of the algorithm, and compared with other algorithms, the validity of the proposed algorithm is verified.
【作者單位】： 河南大學(xué)計(jì)算機(jī)與信息工程學(xué)院;杭州電子科技大學(xué)自動化學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(U1604158) 河南省教育廳科學(xué)技術(shù)研究重點(diǎn)資助項(xiàng)目(16A413002)
【分類號】：TP18;TP277

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