Query: Double-level adversarial domain adaptation network for intelligent fault diagnosis

title: Double level adversarial domain adaptation network for intelligent fault diagnosis
abstract: Deep neural networks have been widely studied in the field of mechanical fault diagnosis with the rapidity of intelligent manufacturing and industrial big data, however, attractive performance gains usually come from a premise that source training data and target test data have the same distribution. Unfortunately, this assumption is generally untenable in practice due to changeable working conditions and complex industrial environment. To address this issue, a double level adversarial domain adaptation network (DL ADAN) is presented for cross domain fault diagnosis, which is able to bridge the divergences between the source and target domains. Specifically, the proposed diagnostic framework is composed of a feature extractor based on deep convolutional network, a domain discriminator and two label classifiers, which conducts two minimax adversarial games. In the first adversarial stream, the feature extractor and domain discriminator game with each other to achieve domain level alignment from a global perspective. On the other line, the extractor and two classifiers are against each other to conduct class level alignment, in which Wasserstein discrepancy is used to detect outlier target samples. As a result, the extractor can learn transferable discriminative features for accurate fault diagnosis. Extensive diagnostic experiments are constructed for performance analysis and several state of the art diagnostic methods are selected for comparative study. The comprehensive results demonstrate the effectiveness and superiority of the proposed method.

title: Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis
abstract: The data distribution shift is inevitable in practical fault diagnosis due to internal and outside changes of equipment. These obstacles will lead to performance degrade or even failure of diagnostic models. In light of these problems, a novel unsupervised intelligent diagnostic framework named Adversarial Adaptation network based on Classifier Discrepancy (AACD) is introduced for mechanical fault diagnosis where the training and test domains have different data distribution. Specifically, AACD mainly contains two parts: one is the shared feature generator built by the 1 D convolutional neural network and the other is double task specific classifiers. They play an adversarial training game to learn class separable and domain invariant features for fault diagnosis. The proposed AACD is evaluated by 15 transfer diagnosis tasks constructed on the planetary gearbox test bed and the rolling bearing test bed. Moreover, six popular algorithms are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach.

title: Deep multi scale adversarial network with attention: A novel domain adaptation method for intelligent fault diagnosis
abstract: Data driven based intelligent fault diagnosis methods, as a promising approach, have been widely employed in the health management and maintenance decision of rotating machinery. However, the domain shift phenomenon caused by internal and external interference inevitably exists in practical application scenarios, which significantly deteriorates the performances of the intelligent diagnosis model. And the preparation of label information in real complex scenes is usually time consuming and expensive. To overcome these challenges, a novel unsupervised domain adaptation framework named deep multi scale adversarial network with attention (MSANA) is introduced for machinery fault diagnosis. It is established based on two main components, one is the shared feature generator, which is constructed by two novel multi scale modules with attention mechanism, and the other part is a fault pattern recognition module composed of two differentiated discriminators. While the multi scale module is used to obtain rich features through different internal perceptual scales, the attention mechanism determines the weights of different scales, which promotes the dynamic adjustment performance and adaptive ability of the model. Then, decision boundary assisted adversarial learning strategy is employed to eliminate domain distribution differences and obtain domain invariant features. A total of ten rolling bearing based transfer scenarios and six gearbox based transfer scenarios are adopted to evaluate the transferability of the proposed MSANA model, and the cross domain transfer results show that it has superior transferability and stability.

title: Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation
abstract: With the rapid development of fault diagnosis methods based on deep learning, many studies have investigated the transfer of intelligent fault diagnosis methods to learn the domain invariant features of machines under different conditions. Previous researches focused on learning domain invariant features through domain adaptation. However, the domain alignment methods cannot remove the domain shift, the target samples may be incorrectly classified by the decision boundary learned from the source domain and eventually cause the domains to be aligned in the wrong direction. To cope with this problem, we propose a deep adversarial domain adaptation network (DADAN) to transfer fault diagnosis knowledge. DADAN uses domain adversarial training based on the Wasserstein distance to learn domain invariant features from the raw signal. In addition, the network is combined with a supervised instance based method to learn the discriminative features with better intraclass cohesion and interclass separability, which can benefit the domain alignment. A data set of bearing data including three speed conditions and a data set of hard disk data acquired from accelerated degradation test and real case conditions were used to evaluate the performance of the proposed DADAN.

title: Residual joint adaptation adversarial network for intelligent transfer fault diagnosis
abstract: Although deep networks based diagnostic methods have been increasingly studied and acquired certain achievements in recent years, most of them suppose that the training and test data share similar probability distribution. The data distribution discrepancy is common and inevitable in practical industry due to the change of working conditions, equipment wear and environment interferences, which will lead to significant performance degradation of models. To address this problem, an unsupervised transfer learning framework named Residual Joint Adaptation Adversarial Network (RJAAN) is proposed in this paper for more effective intelligent fault diagnosis. In this framework, one dimensional residual network is designed to directly process raw mechanical signal for adaptive feature learning, in which the joint maximum mean discrepancy (JMMD) and adversarial adaptation discriminator are introduced to simultaneously reduce the shifts of joint distribution and marginal distribution across different domains. Consequently, the proposed method can learn category discriminative and domain invariant features information for cross domain fault diagnosis. Eighteen transfer fault diagnosis tasks based on two experimental platforms, i.e. the planetary gearbox and the rolling bearing, are conducted to evaluate the effectiveness of the proposed method. Meanwhile, five popular methods are selected for comprehensive analysis and comparison. The results show that the robustness and superiority of the proposed approach under various diagnostic tasks.

title: Intelligent Fault Diagnosis for Bearing Dataset Using Adversarial Transfer Learning based on Stacked Auto Encoder
abstract: Intelligent fault diagnosis of bearing dataset based on transfer learning has made more attention because transfer learning has powerful transferability. However, in previous studies, a major assumption accepted by default is that the source and target domain have the same number of fault categories. Unfortunately, this assumption is invalid in real industrial work conditions because the target domain samples are unlabeled, which leads to the number of fault categories are unknown. In this case, the existing transfer learning methods fail to achieve well transfer results. Therefore, inspired by the idea of open set domain adaptation, an adversarial transfer learning based on stacked auto encoder method is proposed to address new fault emerging problem for the target domain. In the proposed method, a stacked auto encoder (SAE) network is adopted to effectively extract transferable features. Then, adversarial learning and the gradient reverse layer are utilized to achieve model training and parameters backpropagation. Finally, the known fault types are recognized in the known fault classifier and the new fault type is identified in the new fault classifier. The proposed method is verified by a famous bearing dataset. The experiment results show that the proposed method not only able to effectively obtain the known fault categories transfer, but also separate the new fault type from the target domain samples.

title: Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis
abstract: Numerous intelligent methods have been developed to approach the challenges of fault diagnosis. However, due to the different distributions of training samples and test samples, and the lack of information on test samples, most of these methods cannot directly handle the unsupervised cross domain fault diagnosis issues. In this paper, a joint distribution adaptation network with adversarial learning is developed to effectively tackle the mentioned fault diagnosis issues. Firstly, deep convolutional neural network (CNN) is constructed to extract the features of training samples and test samples. Secondly, since the joint maximum mean discrepancy (JMMD) cannot precisely measure the joint distribution discrepancy between different domains, an improved joint maximum mean discrepancy (IJMMD) is proposed to accurately match the feature distributions. Finally, adversarial domain adaptation is also developed to help the constructed CNN to extract the domain invariant features. Therefore, the proposed method can achieve precisely distribution matching, and extract the category discriminative and domain invariant features between the source and target domains. Substantial transfer fault diagnosis cases based on three rolling bearing datasets fully demonstrate the effectiveness and generalization ability of the proposed method.

title: Multi weight Domain Adversarial Network for Partial set Transfer Diagnosis
abstract: To realize fault identification of unlabeled data and improve model generalization capability, domain adaptation has been increasingly applied to intelligent fault diagnosis of machinery. Nevertheless, traditional domain adaptation diagnosis models generally constrain different domains to have the same label space, which is not always hold in complex industrial scenarios. Consequently, a more practical scenario, i.e. partial set transfer diagnosis (PSTD) is explored in this work, where the target label space is a subspace of source domain. A multi weight domain adversarial network (MWDAN) is proposed to solve this issue, in which class level and instance level weighted mechanisms are jointly designed to quantify the transferability and importance of data example. Based on the proposed strategy, the positive transfer between shared classes is promoted while the negative effect caused by outlier classes is circumvented. As a result, MWDAN can learn discriminative representations for accurate fault diagnosis in target domain. Extensive experiments constructed on two mechanical systems demonstrate the outstanding performance of MWDAN.

title: A multi source dense adaptation adversarial network for fault diagnosis of machinery
abstract: Deep learning (DL) theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC) Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multi source information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problem, a novel multi source dense adaptation adversarial network (MDAAN) is proposed, which leverages multi sensor vibration information and classification label information. Specifically, the frequency spectrum of multi sensor data are firstly employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state of the art performances, which has shown great promise as an intelligent fault diagnosis method.

title: Deep Adversarial Capsule Network for Compound Fault Diagnosis of Machinery Toward Multidomain Generalization Task
abstract: With advanced measurement technologies and signal analytics algorithms developed rapidly, the past decades have witnessed large amount of successful breakthroughs and applications in the field of intelligent fault diagnosis (IFD) However, the historical IFD methods have difficulties for compound fault diagnosis, when labeled data cannot be collected in advance for new or extreme working conditions. Facing with such challenges, a deep adversarial capsule network (DACN) is proposed to embed multidomain generalization into the intelligent compound fault diagnosis, so that the DACN not only can intelligently decouple the compound fault into multiple single faults for industrial equipment (IE) but also can be generalized from certain working conditions to another new. First, a DACN including feature extractor (FE) decoupling classifier (DC) and multidomain classifier (MC) is constructed for feature learning, fault decoupling, and unsupervised multidomain adaptation, respectively. Second, adversarial training is introduced into the DACN in the training stage via a gradient reversal layer that can build the connection between the FE and MC, which can force the DACN to learn the domain invariance features. Finally, the DACN is trained using the single fault data collected under multiple working conditions, and then applied to monitor the health condition of IE under new working conditions. The cross validation experiments have been implemented on an automobile transmission (AT) which illustrates that the DACN obtains an optimum performance with the highest average accuracy of 97.65% for compound fault diagnosis of IE under multidomain generalization task and outperforms other related methods.