A Cross-domain Fault Diagnosis Method for Mixed-fusion Samples Based on Data Generation and Class-level Domain Adversary

Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems

Fault diagnosis of inter-shaft bearing is crucial for enhancing the reliability and safety of aero-engines. However, complex and variable working conditions, along with noise interference, make it challenging to ensure effective data collection and accurate fault diagnosis. To address the issue of inconsistent data distribution caused by varying working conditions, this paper proposed a deep transfer learning-based inter-shaft bearing fault diagnosis model called EPBSP. The model first used Continuous Wavelet Transform (CWT) to convert vibration signals from source and target conditions into time-frequency images. Then, a ResNet50-based feature extractor was constructed, incorporating a fusion attention mechanism residual block to effectively capture local and global information of fault features. Simultaneously, a joint loss function was constructed by combining Batch Spectral Penalization (BSP) regularization method with Domain-Adversarial Neural Network (DANN) for adversarial training, reducing the feature distribution difference between source and target domains and improving the model’s domain adaptation capability. Finally, the proposed method was analyzed using datasets from Harbin Institute of Technology and a self-built test rig, achieving fault diagnosis accuracies of 99.19% and 99%, respectively. The method outperformed existing fault diagnosis models in terms of both diagnostic accuracy and generalization ability.

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis

A new indirect transfer fault diagnosis method based on feature separation

Due to the changeable operating conditions of rotating machinery, the feature distributions of fault are usually changed. Most current cross-domain intelligent fault diagnosis methods only achieve global domain alignment, while ignoring the class discrepancy, resulting in the misclassification of the target domain samples near the class boundary. In this article, a novel joint domain alignment and class alignment (JDACA) method is proposed for cross-domain fault diagnosis of rotating machinery. In JDACA, the strategy of synchronously implementing global domain alignment and class alignment is innovatively proposed. First, a feature extractor and two discrepant classifiers are established to extract high-level features and output predicted results. Then, the maximum mean discrepancy (MMD) loss is used to reduce the marginal distribution discrepancy of high-level features between the source domain and target domain. Finally, the classifier discrepancy loss and the contrastive loss are creatively combined for class alignment learning, which can effectively reduce the conditional probability discrepancy between the source domain and target domain. Moreover, two experiment cases demonstrate the effectiveness of the proposed cross-domain diagnostic method.

The excellent performance of current intelligent fault diagnosis methods based on deep learning is attributed to the availability of large amounts of labeled data. However, in practical bearing fault diagnosis, the high cost of large sample data and changes in operating conditions lead to the scarcity of available training data, which limits the engineering application of intelligent bearing fault diagnosis. To solve this problem, this paper proposes a cross-domain fault diagnosis method based on multisource subdomain adaptation networks (MSDAN). First, the data from multiple source domains are simultaneously input to a shared feature extractor composed of a one-dimensional residual network. Then, the private feature extractor is used to learn features from different source domains and reduce the domain shifts of each source and target domain using the local maximum mean discrepancy. Finally, the different classifier outputs of the target domain samples are aligned. The highlight of MSDAN is to obtain diagnostic knowledge from multiple source domains and further divide the subdomains using the categories as criteria, which not only aligns the global distribution of the source and target domain but also performs a more refined subdomain alignment. The method effectively alleviates the negative transfer phenomenon caused by insufficient domain alignment in multisource transfer diagnosis. The effectiveness and superiority of the proposed MSDAN method are verified by constructing seven multisource transfer tasks with two bearing fault diagnosis cases, including cross-operating-condition and cross-machine.

Cross-condition fault diagnosis of chillers based on an ensemble approach with adaptive weight allocation

Effective fault diagnosis methods can ensure the safe and reliable operation of the machines. In recent years, deep learning technology has been applied to diagnose various mechanical equipment faults. However, in real industries, the data distribution under different working conditions is often different, which leads to serious degradation of diagnostic performance. In order to solve the issue, this study proposes a new deep convolutional domain adaptation network (DCDAN) method for bearing fault diagnosis. This method implements cross-domain fault diagnosis by using the labeled source domain data and the unlabeled target domain data as training data. In DCDAN, firstly, a convolutional neural network is applied to extract features of source domain data and target domain data. Then, the domain distribution discrepancy is reduced through minimizing probability distribution distance of multiple kernel maximum mean discrepancies (MK-MMD) and maximizing the domain recognition error of domain classifier. Finally, the source domain classification error is minimized. Extensive experiments on two rolling bearing datasets verify that the proposed method can implement accurate cross-domain fault diagnosis under different working conditions. The study may provide a promising tool for bearing fault diagnosis under different working conditions.

A novel inter-domain attention-based adversarial network for aero-engine partial unsupervised cross-domain fault diagnosis

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning

Intelligent fault diagnosis of rotors always requires a large amount of labeled samples, but insufficient vibration signals can be obtained in operational rotor systems for detecting the fault modes. To solve this problem, a domain-adaptive transfer learning model based on a small number of samples is proposed. Time-domain vibration signals are collected by overlapping sampling and converted into time-frequency diagrams by using short-time Fourier transform (STFT) and characteristics in the time domain and frequency domain of vibration signals are reserved. The features of source domain and target domain are projected into the same feature space through a domain-adversarial neural network (DANN). This method is verified by a simulated gas generator rotor and experimental rig of rotor. Both the transfer in the identical machine (TIM) and transfer across different machines (TDM) are realized. The results show that this method has high diagnosis accuracy and good robustness for different types of faults. By training a large number of simulation samples and a small number of experimental samples in TDM, high fault diagnosis accuracy is achieved, avoiding collecting a large amount of experimental data as the source domain to train the fault diagnosis model. Then, the problem of insufficient rotor fault samples can be solved.

Deep domain adaptation (DA) methods are widely employed to address data distribution inconsistencies in engineering scenarios. Although DA methods have demonstrated effectiveness in cross-domain fault diagnosis, several limitations remain. Most studies primarily focus on aligning marginal distributions while often overlooking the alignment of conditional distributions, which can lead to inaccurate class alignment. To enhance the alignment between source and target domains and reduce class confusion at the decision boundary, an attention-guided joint distribution domain adaptation method (DRCDA) is proposed. First, domain adversarial neural networks (DANN) are utilized to align marginal distributions between the source and target domains. Next, a class alignment module is introduced to align conditional distributions by computing inter-class distances and applying multi-kernel maximum mean discrepancy (MK-MMD). This method incorporates pseudo-labels from the target domain alongside true labels from the source domain, facilitating compact clustering of similar features while ensuring separation between different classes. Additionally, an adaptive threshold pseudo-labeling strategy is designed to address the issue of low-quality pseudo-labels in the target domain. To further mitigate negative transfer effects, an inter-domain attention module is proposed to explore transferable contextual information and model the correlation between the source and target domains. The effectiveness of the DRCDA method is evaluated using two bearing datasets and one gearbox dataset. Experimental results confirm the superiority of the proposed method in cross-condition diagnostic tasks.

Domain adaptation (DA) is crucial for effective bearing fault diagnosis, as it ensures reliable performance across varying operational conditions. Current multisource DA (MSDA) methods mainly focus on overall source domain alignment, neglecting individual fault type alignment, which leads to inadequate information extraction and suboptimal fault diagnosis accuracy. To address the aforementioned problems, this article innovatively proposes a novel MSDA strategy based on attention mechanism. By utilizing a multibranch adversarial fault diagnosis network with a partially shared structure, this study enables the simultaneous alignment of multiple source domains with the target domain. Additionally, the method uses a pseudo-label training strategy and an attention mechanism to align fault types between source and target domains, rather than applying an average weight across all fault types indiscriminately. This nuanced alignment significantly facilitates efficient fusion of multisource domains and fully leverages information from the source domains. The method’s effectiveness and superiority were validated through experimental analysis involving four distinct bearing conditions. Compared to the latest research methods, this approach achieves superior diagnostic performance and provides a new perspective for MSDA in fault diagnosis.