Deep subdomain adversarial network with self-supervised learning for aero-engine high speed bearing fault diagnosis with unknown working conditions

The concept of Industry 4.0 uses cyber-physical systems and the Internet of Things to create "smart factories" that enable automated and connected production. However, the complex and diverse nature of cyber-physical systems makes them vulnerable to physical threats, where any fault in their physical components can lead to serious consequences, from financial losses to human safety. To reduce the risk of such failures, it is essential to have techniques in place to monitor the condition of these physical components closely. In recent years, data-driven predictive maintenance methods have become common due to the growth in data and advancements in machine learning. This thesis focuses on fault diagnosis, a key part of predictive maintenance, which aims to detect and identify faults in physical components. Incorporating advanced techniques, including machine learning algorithms and sensor data analysis, enhanced fault diagnosis systems' accuracy and efficiency. However, several real-world barriers challenge the effectiveness of these techniques. This research is structured around three primary objectives. The first objective aims to establish an efficient fault diagnosis system that is highly sensitive to fault occurrences while being resilient to uncertainties such as variability in fault patterns and data imbalance. To address variability in fault patterns, we developed a multi-step approach integrating clustering techniques and an adaptive neuro-fuzzy inference system, which significantly improved diagnosis robustness even under noisy conditions. For imbalanced datasets, a two-step method was introduced, combining permutation entropy for fault detection and envelope analysis for fault isolation. This approach was enhanced by an automatic frequency band selection method leveraging wavelet transform and statistical criteria, resulting in high fault diagnosis accuracy and reliability, even with limited labeled faulty data. The second objective focuses on developing fault diagnosis systems that can maintain high performance despite domain shifts and limited training data in both source and target domains. To address the challenge of domain shifts, a transfer learning technique was introduced, and a systematic literature review was conducted. This study bridges a technical gap by providing a comprehensive taxonomy, state-of-the-art methods, and insights into the challenges of applying transfer learning to predictive maintenance. Additionally, a conceptual framework was proposed to explore scenarios for transferring diagnostic knowledge from different source domains to target domains with insufficient training datasets. In a subsequent study, a new diagnostic framework was developed using digital twins and partial transfer learning to address the lack of real training datasets in both source and target domains as well as label space inconsistencies between them. The framework includes a general approach for developing digital twins of real machines to generate synthetic diagnostic data as source domain data. Furthermore, by incorporating a deep learning network and a double-layer attention mechanism, a partial transfer learning diagnostic model was developed to transfer diagnostic knowledge from the digital twin to the real target domain despite label space inconsistencies. The proposed framework was validated through an industrial case study involving rolling bearings in rotating equipment. The third objective focuses on developing an efficient fault diagnosis system capable of maintaining reliability under unseen and evolving working conditions. To achieve this, a novel self-adaptive fault diagnosis system was proposed, incorporating the MAPE-K loop, a domain generalization network model, and digital twin technologies. This self-adaptive fault diagnosis system leverages the MAPE-K loop to adapt in real time to evolving working conditions. Digital twins were employed to continuously generate data aligned with the real system’s operational context, enabling accurate fault diagnosis in dynamic environments. The domain generalization network model enhances generalization to unseen target working conditions by addressing overfitting and feature loss through multi-domain data augmentation, adversarial learning, and domain discrepancy modules. Validation using multiple rotating machinery datasets demonstrated that the developed fault diagnosis system outperformed state-of-the-art methods across various scenarios. In summary, this dissertation proposes a comprehensive and practical approach to addressing variability in fault patterns, imbalance datasets, data scarcity, domain shifts, and unseen and evolving working conditions challenges in existing fault diagnosis systems.

This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between the source and target domains, the proposed method is more flexible to capture individual class variations across domains. By adopting a natural and widely used assumption that the data samples from the same class should lay on an intrinsic low-dimensional subspace, even if they come from different domains, the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the joint subspaces of the source and target domains. Specifically, given labeled samples in the source domain, we construct a subspace for each of the classes. Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and are highly likely to belong to the same class. The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across the source and target domains, and within the anchor subspaces, respectively. We further combine the anchor subspaces to the corresponding source subspaces to construct the joint subspaces. Subsequently, one-versus-rest support vector machine classifiers are trained using the data samples belonging to the same joint subspaces and applied to unlabeled data in the target domain. We evaluate the proposed method on two widely used datasets: 1) object recognition dataset for computer vision tasks and 2) sentiment classification dataset for natural language processing tasks. Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.

Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.

Recently, domain adaptation has received extensive attention for solving intelligent fault diagnosis problems. It aims to reduce the distribution discrepancy between the source domain and target domain through learning domain-invariant features. However, most existing domain adaptation methods mainly focus on global domain adaptation and overlook subdomain adaptation, which results in the loss of fine-grained information and discriminative features. To address this problem, in this article, a deep adversarial subdomain adaptation network is proposed. This network aligns the relevant distributions of subdomains by minimizing the local maximum mean discrepancy loss of the same categories in the source domain and target domain. Under the constraints of global domain adaptation and subdomain adaptation, the distribution discrepancy is reduced from the domain and category levels. Four transfer tasks under different machine rotating speeds and six transfer tasks on different but related machines were used to evaluate the effectiveness of the proposed method. The results demonstrated the robustness and superiority of the proposed method over five other methods.

Traditional cross-domain transfer learning faces significant challenges in rotating machinery fault diagnosis due to the general scarcity of fault samples. Furthermore, under complex operating conditions, differences in data distribution between the source domain and target domain can also cause negative migration. To overcome these limitations, this paper proposes a Multi-Source Contrastive Learning Domain Adaptation (MSCLDA) method. Firstly, the MSCLDA approach employs a Wasserstein Generative Adversarial Network (WGAN) to synthesize minority-class fault samples. Subsequently, it integrates a Supervised Contrastive Learning (SCL) strategy to optimize feature consistency between real and synthetic samples, thereby effectively mitigating the training bias caused by category imbalance. Furthermore, we devise a prototype-based multi-pseudo label self-correction mechanism. By incorporating this mechanism with a multiple pseudo-label-guided local maximum mean discrepancy (MP-LMMD) strategy, we achieve precise alignment of feature distributions between the source domains and target subdomains. Finally, an adaptive weighting mechanism is introduced to assign higher weights to source domains more relevant to the target domain, thereby reducing the adverse impact of less relevant domains and mitigating negative transfer. Cross-domain experiments on current signals from industrial robots and vibration signals from bearings show that MSCLDA achieves superior fault identification performance under various working conditions, with an average accuracy of up to 99.05%, and effectively suppresses negative transfer.

In real-world industrial environments, frequent condition changes, mechanical degradation, and incomplete fault labeling often lead to data distribution shifts and label space asymmetry between the source and target domains. Moreover, compounded by the emergence of previously unseen fault types, severely undermine the generalization capability of conventional intelligent diagnostic methods. Although existing open-set domain adaptation methods attempt to address unknown classes, most still rely on the assumption of full label space consistency, limiting their applicability under complex and uncertain industrial conditions. To overcome these limitations, this paper proposes a novel model for generalized open-set cross-working condition fault diagnosis, named Multi-Strategy Progressive Adaptation Network (MSPAN). The model allows for partial class overlap between source and target domains, each containing private classes, thereby reducing reliance on strict label alignment, complete class coverage, and prior knowledge of the target domain. This significantly enhances the model’s adaptability and flexibility under realistic operating conditions. MSPAN integrates three core strategies: Centroid-Guided Expansion (CGE), Progressive Consensus Filtering (PCF), and Localized Knowledge Integration (LKI). CGE expands the label space with pseudo-target domain samples, alleviating interference from private classes in the source domain; PCF combines Dual-Measure Driven Ranking and Consensus-Aware Estimation to adaptively filter unknown classes in the target domain; LKI focuses on learning unknown-class representations while enhancing the transferability of general diagnostic knowledge across domains. Extensive experiments on the PU and NLN-ESP public datasets demonstrate that MSPAN achieves both accurate shared-class diagnosis and effective unknown-class separation under varying degrees of class overlap and openness. These results validate its robustness, generalization ability, and practical potential for deployment in complex industrial fault diagnosis scenarios.

Deep Joint Semantic Adaptation Network for Multi-source Unsupervised Domain Adaptation

To address the challenges of fault diagnosis in wind turbine planetary gearboxes under strong noise and limited labeled target-domain data, this paper proposes a novel intelligent diagnostic method integrating multi-source feature fusion with domain adaptation transfer learning. A Multi-source Feature Attention Fusion Convolutional Neural Network (MSFAF-CNN) is constructed, which dynamically fuses vibration signals from multiple measurement points using a channel attention mechanism to assign optimal weights to the most discriminative features. Furthermore, an improved Multi-source Local Maximum Mean Discrepancy (MS-LMMD) loss is introduced, establishing a hierarchical domain adaptation framework that enables fine-grained alignment of feature distributions between the labeled source and unlabeled target domains. Experimental results under the challenging condition of −4 dB noise demonstrate the superiority of the proposed approach: the cross-condition transfer task (A→B) achieves an accuracy of 95.32%, outperforming the conventional LMMD method by 1.05%. Finally, t-SNE-based visualization confirms that the method enhances cross-domain feature compactness, enabling direct processing of raw vibration signals without manual feature extraction. The findings indicate that the proposed approach offers a highly robust solution for fault diagnosis in drive systems under low signal-to-noise ratios and unlabeled operating conditions.

Curriculum learning-based domain generalization for cross-domain fault diagnosis with category shift

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. Recently, multisource domain adaptation (MDA) has begun to attract attention. Its performance should go beyond simply mixing all source domains together for knowledge transfer. In this article, we propose a novel prototype-based method for MDA. Specifically, for solving the problem that the target domain has no label, we use the prototype to transfer the semantic category information from source domains to target domain. First, a feature extraction network is applied to both source and target domains to obtain the extracted features from which the domain-invariant features and domain-specific features will be disentangled. Then, based on these two kinds of features, the named inherent class prototypes and domain prototypes are estimated, respectively. Then a prototype mapping to the extracted feature space is learned in the feature reconstruction process. Thus, the class prototypes for all source and target domains can be constructed in the extracted feature space based on the previous domain prototypes and inherent class prototypes. By forcing the extracted features are close to the corresponding class prototypes for all domains, the feature extraction network is progressively adjusted. In the end, the inherent class prototypes are used as a classifier in the target domain. Our contribution is that through the inherent class prototypes and domain prototypes, the semantic category information from source domains is transformed into the target domain by constructing the corresponding class prototypes. In our method, all source and target domains are aligned twice at the feature level for better domain-invariant features and more closer features to the class prototypes, respectively. Several experiments on public data sets also prove the effectiveness of our method.

In traditional machine learning algorithms, more labeled data are required in model training for better classification accuracy. However, collecting labeled data is an expensive task for real applications. In this paper, a transfer support vector machine (TSVM) technique is introduced to tackle this problem with application to the fault diagnosis of delta 3D printers. Transfer component analysis is first proposed to capture shared features for representing the source and target domains, by cross-domain feature extraction from less labeled data in the source domain and massive unlabeled data in the target domain. Support vector machine is then applied to the fault diagnosis using the transferred features of attitude signals. In the experiments, fault classification rate achieves 83.79% using only 6.7% of the dataset for model training. Compared with peer methods, the results show that the present TSVM exhibits the best performance in extracting the cross-domain feature and improves the accuracy of the 3D printer fault diagnosis.

This research investigates the intelligent fault diagnosis of rotating machinery in a cross domain context. Due to the interference of operating condition or structural differences of mechanical components, there is a certain deviation between the source and target domain dataset, manifested in different input feature spaces or edge probability distributions, which leads to significant errors in the pattern recognition under target domain. In order to address the above issues, this research proposes an intelligent fault diagnosis method based on class confusion constraint mechanism and transferable batch normalization algorithm. Introduce transferable batch normalization algorithm to differentiate the distribution characteristics of source domain data and target domain data, and performs flexible data standardization to preserve the intrinsic features of different domain data. In addition, a target domain classification confusion constraint is formulated. While seeking to align the feature subspaces of the source and target domains, the discriminative information in the target domain samples that is conducive to fault mode classification is maximally preserved, thereby achieving more accurate classification and identification of rolling bearing faults.

Fault diagnosis is very important for condition based maintenance. Recently, deep learning models are introduced to learn hierarchical representations from raw data instead of using hand-crafted features, which exhibit excellent performance. The success of current deep learning lies in: 1) the training (source domain) and testing (target domain) datasets are from the same feature distribution; 2) Enough labeled data with fault information exist. However, because the machine operates under a non-stationary working condition, the trained model built on the source domain can not be directly applied on the target domain. Moreover, since no sufficient labeled or even unlabeled data are available in target domain, collecting the labeled data and building the model from scratch is time-consuming and expensive. Motivated by transfer learning (TL), we present a new fault diagnosis method, which generalizes convolutional neural network (CNN) to TL scenario. Two layers with regard to task-specific features are adapted in a layer-wise way to regularize the parameters of CNN. What's more, the domain loss is calculated by a linear combination of multiple Gaussian kernels so that the ability of adaptation is enhanced compared to single kernel. Through these two means, the distribution discrepancy is reduced and the transferable features are learned. The proposed method is validated by transfer fault diagnosis experiments. Compared to CNN without domain adaptation and shallow transfer learning methods, the proposed method gets the best performance for fault classification.

As an important and widely studied research topic, the detection of fresh milk plays a crucial role in protecting consumer health. However, due to the diversity of milk brands and environmental conditions in real-world application scenarios, the accuracy of existing detection models often drops significantly when applied to new domains. A significant amount of new data needs to be gathered in order to retrain the model for new domain, which greatly increases both time and labor costs. To tackle this problem, this paper introduces a transfer learning-based cross-domain milk freshness detection method. The method uses transfer learning to generate data for unknown categories within the target domain. Specifically, the transfer generation model trains on data from both the target and source domain categories. With the proposed model, data from other categories in the source domain can be utilized to generate corresponding target domain data. This method comprehensively considers data generation from multiple perspectives of time, frequency, and spatial, to enhance the authenticity of the generated data. It uses a transfer generation architecture, consisting of a transfer network and a decoder, to learn the mapping relationship between the source and target domain, helping to narrow the gap between domains. Additionally, a feature subspace decomposition method is introduced into the decoder, and a cross-domain consistency loss function is formulated to strengthen the model’s learning capability. The proposed method is shown to generate high-quality data for unknown target-domain categories in multiple cross-domain settings, leading to a performance improvement ranging from 17.64% to 32.44% in the detection of cross-domain milk freshness.

Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy.