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首頁> 美國衛(wèi)生研究院文獻>Sensors (Basel Switzerland) >A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
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A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

機譯：基于改進的CNN-SVM和多通道數(shù)據(jù)融合的旋轉(zhuǎn)機械智能故障診斷深度學習新方法

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Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.
機譯：基于深度學習的智能故障診斷方法成為故障診斷領(lǐng)域的研究熱點。自動且準確地識別旋轉(zhuǎn)機械的初期微故障，特別是對于故障方向和嚴重程度而言，仍然是智能故障診斷領(lǐng)域的主要挑戰(zhàn)。傳統(tǒng)的故障診斷方法依賴于具有先驗知識的工程師的手動特征提取。為了有效地識別旋轉(zhuǎn)機械中的早期故障，本文提出了一種新的方法，即改進的卷積神經(jīng)網(wǎng)絡(luò)-支持向量機（CNN-SVM）方法。該方法通過引入全局平均池技術(shù)和SVM改進了傳統(tǒng)的卷積神經(jīng)網(wǎng)絡(luò)（CNN）模型結(jié)構(gòu)。首先，將來自多個傳感器的時空多通道原始數(shù)據(jù)直接輸入到改進的CNN-Softmax模型中，以訓練CNN模型。其次，改進的CNN用于從原始故障數(shù)據(jù)中提取代表性特征。最后，將提取的稀疏代表特征向量輸入SVM以進行故障分類。該方法適用于滾動軸承的多通道振動信號監(jiān)測數(shù)據(jù)診斷。結(jié)果證實，該方法比其他現(xiàn)有的智能診斷方法（包括SVM，K近鄰，反向傳播神經(jīng)網(wǎng)絡(luò)，深度BP神經(jīng)網(wǎng)絡(luò)和傳統(tǒng)的CNN）更有效。

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