Partial Domain Research Articles

A Three-Stage Cross Domain Intelligent Fault Diagnosis Method for Multiple New Faults

Abstract Due to the randomness and complexity of mechanical faults, the new fault modes usually occur unexpectedly in the actual scenarios. In response to the challenges, a three-stage crossing domain intelligent fault diagnosis method is presented in article. Firstly, the partial domain alignment is achieved based on improved target weighted mechanism, and the outlier identifier is constructed to automatically separate the new fault classes. Then, the unsupervised learning model with silhouette coefficient are built to determine the number of new fault categories. Lastly, the simulation signals are further adopted to determine the specific fault categories. Sufficient experiments on the axial piston pump and public bearing datasets validate that the proposed method could predict the number of new fault categories and judge the specific fault categories. The results indicate that the proposed method outperforms the other methods and has promising practical application in fault diagnosis with multiple new faults.

Characterisation of LGP2 complex multitranscript system in humans: role in the innate immune response and evolution from non-human primates.

Retinoic acid inducible gene I (RIG-I)-like receptors (RLRs), including RIG-I, MDA5 and LGP2, recognize viral RNA to mount an antiviral interferon (IFN) response RLRs share three different protein domains: C-terminal domain, DExD/H box RNA helicase domain, and an N-terminal domain with two tandem repeats (CARDs). LGP2 lacks tandem CARD and is not able to induce an IFN response. However, LGP2 positively enhances MDA5 and negatively regulates RIG-I signaling. In this study, we determined the LGP2 alternative transcripts in humans to further comprehend the mechanism of its regulation, their evolutionary origin, and the isoforms functionallity. The results showed new eight alternative transcripts in the samples tested. The presence of these transcripts demonstrated that the main mechanisms for the regulation of LGP2 expression are both by insertion of introns and by the loss of exons. The phylogenetic analysis of the comparison between sequences from exon 1 to exon 3 of humans and those previously described in non-human primates showed three well-differentiated groups (lineages) originating from gorillas, suggesting that the transspecies evolution has been maintained for 10 million years. The corresponding protein models (isoforms) were also established, obtaining four isoforms: one complete and three others lacking the C-terminal domain or this domain and the partial or total He2 Helicase domain, which would compromise the functionality of LGP2. In conclusion, this is the first study that elucidate the large genomic organization and complex transcriptional regulation of human LGP2, its pattern of sequence generation, and a mode of evolutionary inheritance across species.

Calorimetric Measurements of a High-Power RF Signal

Development of a high-power RF signal meter based on the method of calorimetric measurements, which in some cases is the only possible one is presented. The device is as a system of elements exchanging either energy or information, the operation and interconnection of which ensures the stability of its functioning. The main elements of the device are: a coordinated load and a two-circuit water-air cooling system that convert RF energy first into thermal electrical energy, and then into internal energy of the coolant (working fluid). The measurement and control system provides information display on the monitor and generation of internal control signals. The work focuses on the process of converting electromagnetic energy into internal energy of the coolant, since the matched RF load is integrated into the cooling system and requires special design and technological solutions to ensure efficient operation. A cooling and measurement system has been designed to test the developed RF absorber with a power of up to 5 kW. The parameters of the cooling system were determined based on minimizing the time to achieve thermal equilibrium (time to establish readings) and the set parameters (input temperature no more than 35 °С, temperature difference no more than 40 °С). Based on calculations of the electrodynamic model constructed by the partial domain method, the geometric dimensions were determined at which the voltage standing wave ratio of the load in the operating frequency range (from DC to 1300 MHz) will be the smallest. The article presents the results of designing a high-frequency wattmeter cooled by a liquid (water), examines the thermophysical processes occurring in it and the influence of the presence of a coolant in the cooling circuit.

Multisource partial domain adaptation for bearing fault diagnosis

Abstract Domain adaptation has been widely used in fault diagnosis and dealt with data distribution discrepancies, but the labels in source and target domains are usually assumed to be identical. The complexity of the working conditions, in reality, leads to the fact that the labels in target domains are often a subset of the labels in source domains. This special case is called the partial domain problem. However, most of the existing proposed methods for solving partial domain problems are limited to single-source-domain scenarios and fail to effectively integrate multisource knowledge. Hence, this study proposes a new approach of multisource domain obfuscation-subdomain alignment (MSDO-SA) for partial domain adaptation fault diagnosis in multisource domains. Through domain obfuscation, the multisource domains are converted into a single source domain. The subdomain alignment aims at improving the generalization ability and relevance of the model, and effectively alleviates the domain shift problem. Finally, multiple partial domain fault diagnosis tasks using the CWRU dataset validate the effectiveness, robustness, and superiority of the proposed method.

Joint weight optimization for partial domain adaptation via kernel statistical distance estimation

The goal of Partial Domain Adaptation (PDA) is to transfer a neural network from a source domain (joint source distribution) to a distinct target domain (joint target distribution), where the source label space subsumes the target label space. To address the PDA problem, existing works have proposed to learn the marginal source weights to match the weighted marginal source distribution to the marginal target distribution. However, this is sub-optimal, since the neural network’s target performance is concerned with the joint distribution disparity, not the marginal distribution disparity. In this paper, we propose a Joint Weight Optimization (JWO) approach that optimizes the joint source weights to match the weighted joint source distribution to the joint target distribution in the neural network’s feature space. To measure the joint distribution disparity, we exploit two statistical distances: the distribution-difference-based L2-distance and the distribution-ratio-based χ2-divergence. Since these two distances are unknown in practice, we propose a Kernel Statistical Distance Estimation (KSDE) method to estimate them from the weighted source data and the target data. Our KSDE method explicitly expresses the two estimated statistical distances as functions of the joint source weights. Therefore, we can optimize the joint weights to minimize the estimated distance functions and reduce the joint distribution disparity. Finally, we achieve the PDA goal by training the neural network on the weighted source data. Experiments on several popular datasets are conducted to demonstrate the effectiveness of our approach. Intro video and Pytorch code are available at https://github.com/sentaochen/Joint-Weight-Optimation. Interested readers can also visit https://github.com/sentaochen for more source codes of the related domain adaptation, multi-source domain adaptation, and domain generalization approaches.

A Partial Domain Generalization Method for Modeling Multiple Multistage Manufacturing Processes

In complex manufacturing systems, materials are processed by machines sequentially before final products are get, forming a multistage manufacturing process (MMP). In a modern massive production factory, it is common that multiple MMPs work simultaneously to fulfill productivity needs. Multiple MMPs may contain machines with different running time, processing the same types of products but with different specifications. In such a Multiple MMP(MMMP) system, it is critical for machines to learn from each other to gain optimal parameter settings of each. However, traditional machine learning methods usually fail to consider both similarities and unique features among multiple processes at the same time. To address this problem, a partial domain generalization method is proposed based on the thought of transfer learning to combine useful information from historical processes while maintaining features from the in-production process. The proposed method also suits for problems with the assumption that only partial input variables are available. Studies reveal that the proposed method has superior prediction performance over traditional machine learning methods and some widely-used transfer learning methods.

An Interpretable Multi-Model Machine Learning Approach for Spatial Mapping of Deep-Sea Polymetallic Nodule Occurrences

AbstractHigh-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km2 of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor ( < 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response.

Attention guided partial domain adaptation for interpretable transfer diagnosis of rotating machinery

Domain adaptation (DA) is a well-established method to tackle the transfer diagnosis of rotating machinery. However, current DA methods encounter challenges in partial-set transfer diagnosis scenarios, where the target domain only possesses a subset of the source fault classes, due to several limitations: (1) assuming identical fault classes between the target and source domains, (2) neglecting the fine-grained differences among individual instances, and (3) lacking interpretability for transfer logic. To address these problems, this paper proposes an attention-guided partial domain adaptation (AGPDA) method for interpretable transfer diagnosis of rotating machinery. Firstly, to exclude outlier classes in the source domain, a transferability evaluator is presented to evaluate the potential benefits of source instances for domain alignment. Secondly, a novel cross-domain attention (CDA) mechanism is further developed, which leverages instance-wise attention to highlight relevant samples, enabling differentiated and effective knowledge transfer to various target samples. Finally, the CDA mechanism is implemented via a new interpretable linear cosine attention, which is specially designed to facilitate the exploration of the transfer logic. Comparison experiments demonstrate the superiority of AGPDA in both closed-set and partial-set transfer diagnosis. Furthermore, dual-perspective visualization of CDA reveals that target fault recognition primarily relies on source samples in the same class.

Comparison of Eye Axial Length Measurements Taken Using Partial Coherence Interferometry and OCT Biometry.

This study evaluates the inter-device measurement properties of partial coherence interferometry (PCI) and spectral domain optical coherence tomography (SD-OCT) in measuring axial length, particularly for myopia management. We recruited 82 eyes from 41 adult participants with a mean age of 31.0 ± 17.6 years and a mean spherical equivalent of -2.20 ± 2.28 D. Axial length was measured using SD-OCT and PCI for both the right and left eyes. Agreement between the two measurements was assessed using Bland-Altman analysis, and graphs and values were compared with linear mixed models. The results show a near-to-zero and non-significant bias between measurements. The 95% limits of agreement showed a value of 0.06 mm. Both devices can accurately measure the axial length. OCT biometry performed with SD-OCT can be successfully interchanged with partial coherence interferometry, but they should be cautiously interchanged when performing longitudinal comparisons.

Cross-Domain Feature Disentanglement for Interpretable Modeling of Tumor Microenvironment Impact on Drug Response.

High-throughput screening technology has enabled the generation of large-scale drug responses across hundreds of cancer cell lines. There remains a significant gap between in vitro cell lines and actual tumors in vivo in terms of their response to drug treatments yet. This is because tumors consist of a complex cellular composition and histopathology structure, known as the tumor microenvironment (TME), which greatly impacts the drug cytotoxicity against tumor cells. To date, no study has focused on modeling the impact of the TME on clinical drug response. In this study, we postulated that the intricate complexity of an actual tumor can be conceptually simplified into two separable components: cancerous cells and the tumor microenvironment. This assumption allowed us to model the influence of these two constituent parts on drug response through feature disentanglement. We employed a domain adaptation network to decouple and extract features from tumor transcriptional profiles. Specifically, two denoising autoencoders were separately used to extract features from cell lines (source domain) and tumors (target domain) for partial domain alignment and feature decoupling. The private encoder was enforced to extract information only about the TME. Moreover, to ensure generalizability to novel drugs, we employed a graph attention network to learn the latent representation of drugs, enabling us to linearly model the drug perturbation on cellular state in latent space. We validated our model on a benchmark dataset and demonstrated its superior performance in predicting clinical drug response and dissecting the influence of the TME on drug efficacy.

Gradient Alignment based Partial Domain Adaptation (GAPDA) using a domain knowledge filter for fault diagnosis of bearing

Fault diagnosis of rolling element bearings is essential to ensure the safety and reliability of industrial sites. However, changes in operating conditions can lead to variations in the distributions of the data that is collected for fault diagnosis. This, in turn, decreases the performance of deep-learning-based fault-diagnosis methods. In addition, most data in industrial settings are unlabeled, which leads to ineffectiveness of the supervised learning method. To address the issues of domain shift and unlabeled data, numerous studies have been conducted to reduce distribution discrepancies when using unlabeled data. Still, most of these studies assume that the number of labels in the training and test data are identical; this is not always true for data from industrial sites. Thus, the research outlined in this paper was pursued to address the partial domain adaptation problem, which occurs when there are fewer labels in the test data than in the training data. The proposed approach suggests two methods for applying partial domain adaptation in mechanical systems: i) a domain knowledge filter is proposed, which reflects fault characteristics in the original signal for effective feature extraction in the mechanical engineering domain, and ii) a gradient alignment module is defined to align the gradient of the statistical loss function. The method proposed herein was validated using two open-source datasets; the approach demonstrated high performance and low uncertainty, as compared to other prior methods. Additionally, physical analysis of the domain knowledge filter was conducted in this work.

WNK1 Interaction with KEAP1 Promotes NRF2 Stabilization to Enhance the Oxidative Stress Response in Hepatocellular Carcinoma.

Cellular oxidative stress plays a key role in the development and progression of hepatocellular carcinoma (HCC). A better understanding of the processes that regulate reactive oxygen species (ROS) homeostasis could uncover improved strategies for treating HCC. Herein, we identified protein kinase with-no-lysine kinase 1 (WNK1) as an antioxidative factor and therapeutic target in HCC. In human HCC, WNK1 expression was increased and correlated with poor patient prognosis. WNK1 knockdown significantly inhibited cell proliferation and xenograft tumor growth. Mechanistically, WNK1 competed with nuclear factor erythroid 2-related factor 2 (NRF2) for binding with the partial Kelch domain of Kelch-like ECH-associated protein 1 (KEAP1), reducing NRF2 ubiquitination and promoting NRF2 accumulation and nuclear translocation to increase antioxidant response. WNK1 silencing increased H2O2-induced apoptosis and inhibited cell growth by elevating ROS levels, which could be rescued by treatment with the antioxidant N-acetylcysteine and NRF2 activator tert-butylhydroquinone. Liver-specific WNK1 knockout mouse models of HCC substantiated that WNK1 promoted HCC development by regulating ROS levels. WNK463, an inhibitor of the WNK kinase family, suppressed HCC progression and altered the redox status. These findings suggest that WNK1 plays a critical role in HCC development and progression and that the WNK1-oxidative stress axis may be a promising therapeutic target for HCC. Significance: Inhibiting WNK1 induces NRF2 degradation and reduces the oxidative stress response to suppress hepatocellular carcinoma growth, indicating that targeting the WNK1-KEAP1-NRF2 axis is a potential strategy to treat liver cancer.

A temporal partial domain adaptation network for transferable prognostics across working conditions with insufficient data

Variations in equipment working conditions cause distribution drifts in condition monitoring data, thus impeding the accuracy of remaining useful life (RUL) prediction. Although numerous transfer learning methods have been developed for cross-domain prognostics, it is still challenging to collect enough target domain data to meet training demands because of economic and safety concerns. Therefore, a temporal partial domain adaptation network (TPDAN) is proposed for transferable prognostics across working conditions without run-to-failure data in the target domain. Firstly, the domain discrepancy is appropriately measured by a stage-weighted maximum mean discrepancy (SW-MMD), allowing for the inconsistency of the RUL distribution of available data between domains. Secondly, the ranking-based feature regularization is developed to align the relative position of samples in the feature space and the label space. It facilitates the exploitation of temporal information inherent in the samples and restrains the destruction of the degradation properties of features. By performing domain adaptation based on the above tools, domain-invariant and degradation-aware features can be extracted by the TPDAN to provide reliable knowledge transfer across working conditions. The effectiveness of TPDAN has been validated, and experimental results demonstrate the compelling transferable RUL prediction performance even when run-to-failure data is unavailable in the target domain.

Partial Domain Adaptation in Remaining Useful Life Prediction With Incomplete Target Data

Partial Domain Adaptation in Remaining Useful Life Prediction With Incomplete Target Data

Maximum likelihood weight estimation for partial domain adaptation

Partial Domain Adaptation (PDA) aims to generalize a classification model from a labeled source domain to an unlabeled target domain, where the source label space contains the target label space. There are two main challenges in PDA that weaken the model's classification performance in the target domain: (i) the joint distribution of the source domain is related but different from that of the target domain, and (ii) the source outlier data, whose labels do not belong to the target label space, have a negative impact on learning the target classification model. To tackle these challenges, we propose a Maximum Likelihood Weight Estimation (MLWE) approach to estimate a weight function for the source domain. The weight function matches the joint source distribution of the relevant part to the joint target distribution, and reduces the negative impact of the source outlier data. To be specific, our approach estimates the weight function by maximizing a likelihood function, and the estimation leads to a nice convex optimization problem that has a global optimal solution. In the experiments, our approach demonstrates superior performance on popular benchmark datasets. Intro video and PyTorch code are available at https://github.com/sentaochen/Maximum-Likelihood-Weight-Estimation.

Identification of the hybrid gene LILRB5-3 by long-read sequencing and implication of its novel signaling function.

Leukocyte immunoglobulin (Ig)-like receptors (LILRs) on human chromosome 19q13.4 encode 11 immunoglobulin superfamily receptors, exhibiting genetic diversity within and between human populations. Among the LILR genes, the genomic region surrounding LILRB3 and LILRA6 has yet to be fully characterized due to their significant sequence homology, which makes it difficult to differentiate between them. To examine the LILRB3 and LILRA6 genomic region, a tool named JoGo-LILR CN Caller, which can call copy number from short-read whole genome sequencing (srWGS) data, was applied to an extensive international srWGS dataset comprising 2,504 samples. During this process, a previously unreported loss of both LILRB3 and LILRA6 was detected in three samples. Using long-read sequencing of these samples, we have discovered a novel large deletion (33,692 bp) in the LILRB3 and LILRA6 genomic regions in the Japanese population. This deletion spanned three genes, LILRB3, LILRA6, and LILRB5, resulting in LILRB3 exons 12-13 being located immediately downstream of LILRB5 exons 1-12 with the loss of LILRA6, suggesting the potential expression of a hybrid gene between LILRB5 and LILRB3 (LILRB5-3). Transcription and subsequent translation of the LILRB5-3 hybrid gene were also verified. The hybrid junction was located within the intracellular domain, resulting in an LILRB5 extracellular domain fused to a partial LILRB3 intracellular domain with three immunoreceptor tyrosine-based inhibitory motifs (ITIMs), suggesting that LILRB5-3 acquired a novel signaling function. Further application of the JoGo-LILR tool to srWGS samples suggested the presence of the LILRB5-3 hybrid gene in the CEU population. Our findings provide insight into the genetic and functional diversity of the LILR family.

Coarse-grained molecular dynamics model of AB diblock copolymers composed of blocks with different Lennard-Jones parameters forming lamellar structures

In thermoplastic elastomers, the structure formation of hard and soft domains and the mechanical response to deformation remain vital areas of interest. To realize the coarse-grained molecular dynamics (CGMD) simulations of systems incorporating the difference in glass transition temperature Tg between domains, we studied AB diblock copolymers (di-BCPs) with asymmetric Lennard-Jones (LJ) parameters (the strength coefficient εLJ and cutoff length rc). For instance, hard and soft domains were realized by assigning the cutoff lengths of each segment to rc,(A,A) = 2.5 (high Tg) and rc,(B,B) = 1.5 (low Tg), respectively. To identify how the cutoff length rc,(B,B) corresponds to the strength coefficient ε(B,B) in the phase separation behavior against the fixed LJ parameters for the A block (rc,(A,A) = 2.5 and ε(A,A) = 1.0), we focused on lamellar structures in which the A and B blocks possess equal segment numbers. For the asymmetric cases with rc,(B,B) < rc,(A,A), we estimated the effective ε(B,B)' on the basis of the DA/DB ratio of the partial domain spacings. We confirmed that the scaling relations (D ∼ Δε1/6, D/N0.7 ∼ Δε, and D/N1/2 ∼ ΔεN) hold, where ε(A,A) = 1, Δε = ((1 + ε(B,B)')/2 – ε(A,B)), and N denotes the number of segments per AB di-BCP chain. Therefore, this work will provide valuable insights for guiding applied research on BCPs.

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

In recent years, domain adaptation techniques have garnered significant attention in the field of intelligent fault diagnosis for mechanical equipment. Domain adaptation techniques can effectively enable the reuse of diagnostic knowledge, thereby improving the generalization performance of the trained model. However, many intelligent diagnostic models are tailored to specific diagnostic scenarios, including closed-set domain adaptation, partial domain adaptation, and open-set domain adaptation. When these models built for specific scenarios are applied to other scenarios, their diagnostic performance will deteriorate catastrophically. This study proposes an auto-regulated universal domain adaptation network (AUDAN) for uncertain diagnostic scenarios of rotating machinery, which can handle multiple diagnostic scenarios and adaptively set threshold boundaries for each fault category based on data distribution structure to distinguish known and unknown samples. Specifically, a novel integration loss function is designed to train the proposed AUDAN. The self-adaptive reweight module is introduced into the proposed method to distribute the relative weight for each loss function automatically. Experiments on two rotating machinery datasets are carried out for validations. The experimental results demonstrate that the proposed AUDAN performs effectively in uncertain diagnostic scenarios and is promising in addressing the fault classification tasks in real industries.

A dual-weighted adversarial network for partial domain fault diagnosis of machinery

Domain adaptation provides a promising approach to cross-domain fault diagnosis of rotating machinery. While many current methods focus on scenarios where the source and target domains share identical label spaces, a prevalent situation in industrial production involves the target domain being a subset of the source domain, known as partial domain adaptation (PDA). The main challenge in PDA is the label mismatches caused by outlier classes, making the alignment between domains particularly difficult. To this end, a dual-weighted adversarial network is proposed in this paper. Specifically, a bilateral class weighting strategy (BCWS) is developed, which can effectively suppress negative migration at the decision boundary while improving the robustness of adversarial training by applying bilateral weighting to both the source and target domains. Moreover, a collaborative framework is developed to facilitate positive migration. The constructed global perceptual module (GPM) is highly correlated with the improved adversarial loss function, which adaptively adjusts the input feature map according to the accuracy of the target domain classification and pays more attention to the domain-invariant features. Experimental results for two cases, namely across different operating conditions and across different rotating components, verify the effectiveness and superiority of the proposed method for the PDA problem.

Addressing the Overfitting in Partial Domain Adaptation With Self-Training and Contrastive Learning

Addressing the Overfitting in Partial Domain Adaptation With Self-Training and Contrastive Learning