Fault Diagnosis for Bearing Based on 1DCNN and LSTM

Abstract

Condition monitoring and fault diagnosis of the bearing are essential for the smooth operation of rotating machinery. In this paper, an end-to-end intelligent fault diagnosis method for bearing combining one-dimensional convolutional neural network with long short-term memory network (1DCNN-LSTM) is proposed for the deficiencies of existing fault diagnosis methods. First, the proposed method takes one-dimensional fault data directly as input. Second, one-dimensional convolutional neural network (1DCNN) is used for self-adaptively extracting robust features from the original bearing signal, and more features are extracted while ensuring the validity and saliency of the extracted features by combining maximum pooling and average pooling layers to downsample features. Then, long short-term memory network (LSTM) is used to learn the temporal dependencies among features. At last, fault identification is achieved. 1DCNN-LSTM does not require any manual feature extraction, and the errors caused by reliance on expert experience and incomplete information in traditional feature extraction methods are avoided. The results show that the proposed classifier with good generalization performance not only diagnoses the category of fault quickly and accurately under different load conditions but also achieves an average fault identification accuracy of 99.95%. For its powerful learning abilities, this method can also be applied to the bearing fault diagnosis of rotating machinery in many fields.

Applications of convolutional neural networks in fault diagnosis of mechanical-electrical-hydraulic systems: a review

“…The Dropout layer was also added in the model to prevent overfitting and lessen the noise interference. In 2021, the Shandong University of Technology Sun et al [65] extracted the orientation features of bearings using 1DCNN with the combination of maximum pooling and average pooling, then they extracted the temporal information using the LSTM model. The application of the two types of pooling jointly ensured the effectiveness of the extracted features and improved the training speed.…”

Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review

“…For the shortcomings of existing fault detection methods, an end-to-end intelligent fault diagnosis procedure for the bearing is suggested in [103], which combines a long short-term memory network (LSTM) with a one-dimensional CNN. X. Chen et al [104] offered a neural network with automated feature learning that accepts raw vibration signals as inputs and employs two CNNs with varying kernel sizes to collect distinct frequency signal properties.…”

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

“…Shichao and Haibin proposed a bearing fault diagnosis model in which 1D-CNN with LSTM is implemented, which adaptively extracted potential features from the original vibration signal and ensured the validity of the features through merging of pooling layers of max and average values to down sample the features. Then, LSTM was employed to acquire the dependencies among features of timedomain signals to perform fault classification [17]. Zhe Yuan et al [18] presented a fault recognition approach for roller bearing using Multiscale CNN and Gated Recurrent Unit Network (GRUN) by providing multiple time scaled vibration data into the CNN to train the model and added the gated recurrent unit network to make the model predictive with an attention mechanism.…”