Deep Learning-based Fault Diagnosis Methods Research Articles

Deep Forest-Based Fault Diagnosis Method for Chemical Process

With the rapid expanding of big data in all domains, data-driven and deep learning-based fault diagnosis methods in chemical industry have become a major research topic in recent years. In addition to a deep neural network, deep forest also provides a new idea for deep representation learning and overcomes the shortcomings of a deep neural network such as strong parameter dependence and large training cost. However, the ability of each base classifier is not taken into account in the standard cascade forest, which may lead to its indistinct discrimination. In this paper, a multigrained scanning-based weighted cascade forest (WCForest) is proposed and has been applied to fault diagnosis in chemical processes. In view of the high-dimensional nonlinear data in the process of chemical industry, WCForest first designs a set of relatively suitable windows for the multigrained scan strategy to learn its data representation. Next, considering the fitting quality of each forest classifier, a weighting strategy is proposed to calculate the weight of each forest in the cascade structure without additional calculation cost, so as to improve the overall performance of the model. In order to prove the effectiveness of WCForest, its application has been carried out in the benchmark Tennessee Eastman (TE) process. Experiments demonstrate that WCForest achieves better results than other related approaches across various evaluation metrics.

A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

In recent years, deep learning-based fault diagnosis methods have drawn lots of attention. However, for most cases, the success of machine learning-based models relies on the circumstance that training data and testing data are under the same working condition, which is too strict for real implementation cases. Combined with the features of robustness of deep convolutional neural network and vibration signal characteristics, information fusion technology is introduced in this study to enhance the feature representation capability as well as the transferability of diagnosis models. With the basis of multi-sensors and narrow-band decomposition techniques, a convolutional architecture named fusion unit is proposed to extract multi-scale features from different sensors. The proposed method is tested on two data sets and has achieved relatively higher generalization ability when compared with several existing works, which demonstrates the effectiveness of our proposed fusion unit for feature extraction on both source task and target task.

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating machines, balanced training data for different machine health conditions are assumed in most studies. However, the signals in machine faulty states are usually difficult and expensive to collect, resulting in imbalanced training dataset in most cases. That significantly deteriorates the effectiveness of the existing data-driven approaches. This paper proposes a deep learning-based fault diagnosis method to address the imbalanced data problem by explicitly creating additional training data. Generative adversarial networks are firstly used to learn the mapping between the distributions of noise and real machinery temporal vibration data, and additional realistic fake samples can be generated to balance and further expand the available dataset afterwards. Through experiments on two rotating machinery datasets, it is validated that the data-driven methods can significantly benefit from the data augmentation, and the proposed method offers a promising tool on fault diagnosis with imbalanced training data.

Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism

In the recent years, deep learning-based intelligent fault diagnosis methods of rolling bearings have been widely and successfully developed. However, the data-driven method generally remains a “black box” to researchers and there is a gap between the emerging neural network-based methods and the well-established traditional fault diagnosis knowledge. This paper proposes a novel deep learning-based fault diagnosis method for rolling element bearings. Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge. Experiments on a popular rolling bearing dataset intuitively show the effectiveness of the proposed method, which is able to provide reliable diagnosis even with very few training data. The experimental results suggest this research offers a promising tool for intelligent fault diagnosis and provides effort in understanding the underlying mechanism of deep neural network.

A negative correlation ensemble transfer learning method for fault diagnosis based on convolutional neural network.

With the development of the smart manufacturing, data-driven fault diagnosis has receiving more and more attentions from both academic and engineering fields. As one of the most important data-driven fault diagnosis method, deep learning (DL) has achieved remarkable applications. However, the DL based fault diagnosis methods still have the following two drawbacks: 1) One of the most major branch of deep learning is to construct the deeper structures, however the deep learning models in fault diagnosis is very shadow. 2) As stated by the no-free-lunch theorem, no single model can perform best on every dataset, and the individual deep learning model still suffers from the generalization ability. In this research, a new negative correlation ensemble transfer learning method (NCTE) is proposed. Firstly, the transfer learning based ResNet-50 is proposed to construct a deep learning structure that has 50 layers. Secondly, several fully-connected layers and softmax classifiers are trained cooperatively using negative correlation learning (NCL). Thirdly, the hyper-parameters of the proposed NCTE are determined by cross validation. The proposed NCTE is conducted on the KAT Bearing Dataset, and the prediction accuracy of NCTE is as high as 98.73%. This results show that NCTE has achieved a good results compared with other machine learning and deep learning method.

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery.

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

Deep‐learning based fault diagnosis using computer‐visualised power flow

Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep-learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer-visualised images. Using massive power-flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature-map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep-learning based method may diagnose system faults effectively.