Fault vibration model driven fault-aware domain generalization framework for bearing fault diagnosis

Uncertainty quantification (UQ) is essential for understanding and mitigating the impact of pervasive uncertainties in engineering systems, playing a crucial role in modern engineering practice. As engineering products grow increasingly complex and the demand for highly accurate UQ results intensifies, the need for efficient UQ methods has become paramount. Recently, machine learning (ML) techniques, including Gaussian process regression, artificial neural networks, physics-informed neural networks, and many others, have garnered significant attention in both theoretical research and practical applications. The exceptional capability of ML methods to address highly complex problems has positioned them as leading solutions for UQ challenges. This work provides a comprehensive survey of recent advancements in ML-assisted UQ approaches, covering ML techniques for forward UQ analysis, inverse UQ analysis, fault diagnosis, resilience assessment, and management planning. The most prominent and effective ML techniques such as the physics-informed, physics-guided, physics-enhanced, physics-corrected, and physics-constrained approaches, along with advanced strategies and frameworks, are thoroughly reviewed and summarized. Additionally, this paper discusses the challenges facing the field and outlines potential directions for future research. This work serves as a valuable resource for researchers and engineers seeking an in-depth understanding of state-of-the-art ML approaches to UQ tasks of engineering systems.

A Survey on Machine Learning Approaches for Uncertainty Quantification of Engineering Systems

Domain adaptation (DA)-based methods have been successfully applied in fault diagnosis, but their effectiveness relies on the assumption that the source and target label spaces are identical, which does not always hold in industrial scenarios. A more realistic scenario, named partial transfer diagnosis where the target label space is a subset of the source label space, is explored in this study. To this end, a multi-level weighted dynamic adversarial adaptation network (MWDAAN) is proposed. To prevent the negative effects of outlier data, a parallel sample filtering scheme based on the multi-level weighting strategy is designed, which enhances the effect of correcting noise weights and adequately evaluates the transferability of source samples. On this basis, a dynamic domain adversarial framework embedded with the sample filtering scheme is constructed to maximize the positive transfer of shared classes. Extensive experimental results on four cases demonstrate the effectiveness and superiority of the proposed method. 

Multi-level weighted dynamic adversarial adaptation network for partial set cross-domain fault diagnosis

Permeability prediction is a crucial aspect of investigating fluid flow capacity in porous media, and rapidly predicting the permeability of rock thin sections aids in reservoir assessment. In recent years, significant progress has been made regarding the use of deep learning for predicting porous media permeability, effectively overcoming the limitations associated with time-consuming conventional permeability determination methods. To address the issues of low prediction accuracy and insufficient generalization capability of previous permeability prediction models, this study proposes an improved convolutional neural network for porous media permeability and applies it to predict the permeability of rock thin sections. Given the difficulty of obtaining a large amount of rock thin section data, we synthetically generated 1000 binary images of porous media and calculated the corresponding permeability labels using computational fluid dynamics. These data were used as foundational data for training the model. The proposed model achieved accurate and efficient permeability prediction of porous media with a correlation of 0.9667 on testing set. Simultaneously, we employed class activation mapping technology to visualize the permeability prediction process of the neural network, thereby elucidating the working principles of the neural network during porous media permeability prediction. Subsequently, based on an optimally pretrained neural network, transfer learning was utilized to predict the permeability of rock thin sections, which achieved a correlation of 0.9032. Our study extends the rock thin section applications and provides technical support for reservoir evaluation. 

An improved convolutional neural network for predicting porous media permeability from rock thin sections

The main challenges for partial set cross-domain fault diagnosis problems, where the target label space is only a subset of the source label space, are to facilitate positive transfer between shared classes and avoid negative transfer caused by unrelated classes. To address the above challenges, a prototype-driven class-wise adversarial transfer network (PCATN) is proposed in this study. First, aiming to enhance the classification robustness, a fault prototype-based discrimination method without learnable parameters is designed to replace the traditional classifier for health state recognition. Then, based on the intrinsic similarity between the target samples and the fault prototypes, a novel prototype similarity-based weighting mechanism is proposed to precisely measure the transferability of each source class, thus decreasing the contribution of unrelated source class samples. Finally, the proposed class-wise adversarial adaptation framework facilitates fine-grained knowledge transfer between shared classes and enhances domain adaptation (DA) performance. The experimental results show that the proposed method outperforms all the comparison methods, achieving over 10% improvement in average diagnostic accuracy on the two rolling bearing datasets and maintaining over 90% overall diagnostic accuracy.

Prototype-Driven Class-Wise Adversarial Transfer Networks for Partial Domain Fault Diagnosis of Rolling Bearings

Bearing is a common rotating component, the health status of bearing affects the operation and maintenance of equipment. Thus, the prediction of bearing remaining useful life is of great significance. The remaining useful life prediction based on neural network has black box property, which makes the prediction result may be contrary to the actual physical law. In this paper, a physics guided long short-term memory (LSTM) network is proposed based on the change trend of the time-frequency domain feature indicators of bearings in the process of degradation. Specifically, indexes such as monotonicity are used to select feature indicators that are highly trendy in the process of bearings degradation. On this basis, a regularization term based on the consistent variation of the feature indicators and the remaining useful life (RUL) in the process of bearing degradation is constructed to make the result of the network more consistent with the actual physical law. Meanwhile, the variation of feature indicators is used as dynamic weight to enhance the potential physical consistency. The experimental comparison results show that the prediction results of the network are more accurate and consistent with the actual physical laws with the guidance of physical prior knowledge. 

Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process

Cross-machine domain-generalization-based fault diagnosis (DGFD) methods have shown promising prospects because they assume that the model trained on available source machines can generalize to unseen target machines. However, there are still some problems remaining unsolved. First, most existing methods learned domain-invariant representations from raw vibrational signals directly, and it makes the task challenging because the raw signals contain lots of domain-related information coming from measurement differences (e.g., sampling frequencies and sensor types, rotating speeds, and resonance frequencies). Second, existing cross-machine DGFD methods learn domain-invariant representation by aligning distributions at the class level but failing to suppress domain-specific information at the domain level. Third, the difficulty level of learning to classify samples from different domains varies for a neural network model, leading to the domain imbalance problem, which may damage the model’s generalization ability. To solve the measurement issue, a novel signal preprocessing module is first devised to mitigate the negative effects of raw vibrational signals. Then, this article proposes a dynamic balanced dual prototypical network (DBDP-Net). In DBDP-Net, the dual prototype loss based on class and domain prototypes is designed to learn domain-invariant feature representation by reducing distribution discrepancy at class and domain levels. Finally, to reduce the domain imbalance problem, we propose a dynamic weighted strategy to balance the feature learning process of different domains. Extensive experiments are conducted on five bearing fault datasets collected from different machines, and the results show that the proposed DBDP-Net is superior to the state-of-the-art (SOTA) DGFD methods. The source code will be available at https://github.com/ShaneSpace/MyResearchWorksPublic.

Dynamic Balanced Dual Prototypical Domain Generalization for Cross-Machine Fault Diagnosis

Consumer technology has undergone an evolution from simple manually operated devices to complex intelligent systems that integrate seamlessly into daily life. The rapid proliferation and integration of consumer technology in our daily lives has brought about the need for more elaborative development of health management systems to ensure device reliability, resilience, and longevity. Unexpected failures of consumer technology, including both hardware and software, can lead to significant inconvenience in daily lives and work, financial costs, health and safety risks for users, increased warranty and repair costs, brand reputation damage, and legal issues for manufacturers.

Guest Editorial of the Special Section on Advances in Neural Computing-Enabled Device Health Management for Consumer Technology

Recently, domain adaptation has been widely investigated for solving domain shift problems in mechanical fault diagnosis. Generally, domain adaptation-based diagnosis methods assume that the source and target domains have identical label space. However, a more realistic scenario is that the label space of the target domain is a subset of the source domain, which may introduce two problems: mismatching caused by the occurrence of outlier classes and misalignment caused by overweighting of the uncertain samples near the classification boundary. To address the above problems, a balanced adversarial domain adaptation network (BADAN) is proposed for intelligent fault diagnosis tasks under partial transfer scenarios. A balanced strategy is introduced to augment classes in the target domain using source samples. On this basis, an adversarial domain adaptation method with class-level weight is designed to avoid negative transfer by filtering outlier classes and promote positive transfer by mitigating the distribution discrepancy of shared classes. Moreover, to alleviate the misalignment problem, a complement objective function for ensuring alignment direction toward the support of the source classes is derived by minimizing their predicted scores of the incorrect classes rather than ground-truth classes. Extensive partial transfer diagnosis tasks constructed on two machines are used to demonstrate the robust and superior performance of BADAN.

A Balanced Adversarial Domain Adaptation Method for Partial Transfer Intelligent Fault Diagnosis

Multi-modal contrastive causal consistency fusion for anomaly detection in additive manufacturing

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