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

Data-driven methodologies are indispensable for early satellite anomaly detection. However, their effectiveness in capturing temporal dependencies within non-stationary telemetry data is still not ideal. In the field of satellite anomaly detection using reconstruction theory, this limitation can make it unclear whether the distribution shifts of reconstruction errors are caused by inadequate training or the actual emergence of real anomalies. To address these challenges, we propose the Seasonal Decomposition Cross Attention Transformer (SDCA-Former), a new Transformer architecture specifically designed to effectively extract temporal dependencies in non-stationary satellite telemetry data, thereby enabling more precise early warning of satellite anomalies. Firstly, building upon the foundational architecture of the Vanilla Transformer, the SDCA-Former integrates a series of enhancements, including the Temporal Convolutional Network-based Position Encoding (TCNPE), Encoder architecture refinements, and the Decoder utilizing SDCA modules. Secondly, we developed a comprehensive satellite anomaly detection framework centered on the SDCA-Former, including technical components such as telemetry data feature engineering, anomaly scoring, and determination of anomaly thresholds. Lastly, we verified the effectiveness and superiority of the proposed method on a dataset composed of telemetry data from a Geostationary Earth Orbit (GEO) satellite. Notably, our approach achieved an early warning for anomalies up to four months in advance. 

Satellite Early Anomaly Detection Using an Advanced Transformer Architecture for Non-Stationary Telemetry Data

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

Failure of cooling fan will have a great impact on the operation of the air-conditioning cooling system. Traditional anomaly detection methods for fans rely on manual feature extraction, which is easy to lose effective information. Deep learning-based methods have potential to overcome such a problem, however, it is difficult to train the network without enough monitoring data. In operation, cooling usually worked at high speed, and its monitoring data contain much high-speed rotational noise. This will confuse the networks to focus on high-energy noise while lose the weak but useful information. To solve these problems, we propose a semi-supervised multi-scale deep adversarial model (SMDAM) for fan anomaly detection. In SMDAM, we propose the Linear Spectral Line Elimination (LSLE) technique to reduce the interference of rotational noise spectral lines on network training. Subsequently, a multi-scale feature extraction module is introduced to enrich diagnostic information, which extracts features from different scales without increasing the depth of the convolutional layer. Finally, two engineering data sets are used to verify the superiority of the proposed method. The results show that SMDAM is superior to other methods. 

A Semi-Supervised Multi-Scale Deep Adversarial Model for Fan Anomaly Detection

Recently, with the large-scale application of solid-state drives (SSDs), the failure of SSDs has become the main reliability issue in data centers. SSD manufacturers developed self-monitoring, analysis, and reporting technology (SMART) to indicate the health status of SSDs. However, for the newly enabled and new types of SSDs in the data centers, lack of enough labeled data will be a critical problem, making failure prediction for SSDs a difficult task. To this end, this article proposes a multi-instance adversarial learning domain adaptation network (MALDAN) for coping with this task. A multi-instance learning method with an attention mechanism is designed to solve the problem of features extraction from unlabeled data by assigning weights to features over the lifespan. Moreover, the distribution differences between different SSD models prevent the knowledge of the labeled information from being used for failure prediction of the unlabeled data, and an adversarial domain adaptation (DA) method is used to align the distributions. Finally, the proposed method is verified on Alibaba’s dataset and shows much better performance than other methods.

Multi-Instance Adversarial Learning Domain Adaptation Network for Failure Prediction of Unlabeled Solid-State Drives

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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