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In recent years, research work on intelligent data-driven bearing fault diagnosis methods has received increasing attention. The detection of a fault, whether incipient or moderate, and the monitoring of its evolution are a major challenge in the field of fault diagnosis and are of great industrial interest. For an efficient identification of this type of fault, we propose in this paper a new method of bearing fault diagnosis ("novel BiLSTM" method). This new approach contributes to the improvement of fault diagnosis methods based on BiLSTM networks. The performance was tested under sixteen conditions and for different loads using the Case Western Reserve University (CWRU) bearing dataset under conditions higher than those proposed in the literature dealing with the same problem. The experimental results obtained show that the proposed method has excellent performance. Subsequently, the proposed method was experimentally compared with the CNN model. The results of this comparison showed that the model developed in this paper not only has a higher accuracy rate in the test set but also in the learning process. [ABSTRACT FROM AUTHOR]

As fault identification algorithms for rotating machinery based on deep learning are developing rapidly, convolutional neural networks (CNNs) have been attracting extensive attention due to their feature extraction capabilities. However, most of the current CNN–based fault identification models can only evaluate one aspect of the fault, and the recognition accuracy is low in the case of a complex fault. To achieve various and complex fault diagnoses, this paper proposes a multi-tasking atrous convolution neural network (MACNN). First, the network introduces sub-modules such as atrous convolutional layer, batch normalization processing, and PReLU activation function to improve the efficiency of down-sampling and better realize fault feature extraction. Secondly, a parallel multi-independent output layer and a special loss function corresponding to the multitasking structure are proposed, which make the network better in solving the problem of multi-dimensional fault assessment. Finally, based on the MACNN, experiments on rotating machinery fault diagnosis are carried out. In the experiment, three kinds of bearing fault positions and four kinds of depths are identified together, and the accuracy can reach more than 99%, which has obvious advantages over other neural network methods such as artificial neural network (ANN), traditional three-layer convolutional neural network (3L-CNN), and ACNN without multi-task learning. [ABSTRACT FROM AUTHOR]

Bearing fault is a factor that directly affects the reliability of the machine tools. Small sample bearing fault diagnosis plays an important role to improve the reliability of machine tools. However, the over-fitting and weak performance are common problems of small sample bearing fault diagnoses based on deep learning. This paper proposed a different method based on data enhancement and convolutional neural networks (CNN). The method firstly decomposes the vibration signals of the rolling bearing according to the optimal decomposition criterion of variational mode decomposition (VMD). Then, it selects the modes according to the fault frequency characteristics and filters the selected modes into multiple sub-band signals by band-pass filters. Moreover, it computes out the autocorrelation peak vector of the sub-band signals. Finally, the method uses the fault diagnosis network made from a 4-layer neural network, automatically extracts bearing fault features, and predicts the fault types of the testing signals. The experiment shows that the proposed method has a 99% accuracy rate in the rolling bearing fault data set XJTU-SY and requires fewer training samples than the latest methods of NKH-KELM and VMD-CNN. The proposed method has high accuracy under the small sample conditions, which makes it applicable in some practical CNC machine tools with difficulties obtaining bearing samples. [ABSTRACT FROM AUTHOR]

The successful diagnosis of the faulty signal in rolling element bearings relies on the accurate evaluation of the early fault present within the components of bearings. Because of system imperfection and interference between the data acquisition devices, the fault signal is heavily masked by noise. Hence, to extract the fault information, signal processing techniques are widely used in bearing diagnosis. Although numerous research have dedicated on finding representative features to indicate the damage of the bearing elements, the correlation between defect size and acquired vibration signal has not been properly established. In the recent few years, deep learning has been widely used in bearing diagnosis. In general, the unprocessed signal is directly input into the deep learning model and the neural network extracts useful features during the optimization process. Until now, the selection of features from signals is arbitrary which does not yield much insights into the diagnosis and prognosis. In addition, the features could contain noise information, which could possibly deteriorate diagnostic or prognostic results, while the effect of using preprocessed data has not been fully explored. In this paper, we present an innovative diagnosis model using the deep convolutional network with Bayesian optimization to diagnose the defect severity of bearings. The acquired signal is initially processed using the complementary ensemble empirical mode decomposition method to extract the frequency band containing the fault signature. An experimental based defect size estimation equation is implemented to calculate the defect size based on the signal and experimental setup. After the estimated defect size is obtained, the deep convolutional neural network is implemented to categorize the defect severity. The parameters of the network are optimized by the Bayesian algorithm. The proposed algorithm can be used for diagnosis of the health condition of various rotating machinery. [ABSTRACT FROM AUTHOR]

The development of an intelligent fault diagnosis system to identify automatically and accurately micro-faults affecting motors continues to be a challenge for industrial rotary machinery and needs to be addressed. In this paper, we put forward a novel approach based on ensemble empirical mode decomposition (EEMD) processing for incipient fault diagnosis of rotating machinery. Accurate selection and reconstruction processes are performed to reconstruct new vibration signals with less noise through the application of EEMD processing to original vibration signals. After the rebuilt of vibration signals, manually extracted features from the reconstructed vibration signals are fed then into a multi-class support vector machine and simultaneously to the mentioned technique, generated image representations of the same raw signals are taken afterward as an input to a deep convolutional neural network (CNN) for classification and fault diagnosis. The comparison between these developed methods demonstrates the effectiveness of the deep learning approach that identifies the differences between classes automatically and can successfully classify and locate the faulty bearing status with very high accuracy for the small size of training data. [ABSTRACT FROM AUTHOR]