76 research outputs found
Utilizing Multiple Inputs Autoregressive Models for Bearing Remaining Useful Life Prediction
Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is
crucial in industrial production, yet existing models often struggle with
limited generalization capabilities due to their inability to fully process all
vibration signal patterns. We introduce a novel multi-input autoregressive
model to address this challenge in RUL prediction for bearings. Our approach
uniquely integrates vibration signals with previously predicted Health
Indicator (HI) values, employing feature fusion to output current window HI
values. Through autoregressive iterations, the model attains a global receptive
field, effectively overcoming the limitations in generalization. Furthermore,
we innovatively incorporate a segmentation method and multiple training
iterations to mitigate error accumulation in autoregressive models. Empirical
evaluation on the PMH2012 dataset demonstrates that our model, compared to
other backbone networks using similar autoregressive approaches, achieves
significantly lower Root Mean Square Error (RMSE) and Score. Notably, it
outperforms traditional autoregressive models that use label values as inputs
and non-autoregressive networks, showing superior generalization abilities with
a marked lead in RMSE and Score metrics
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL
The prediction of the remaining useful life (RUL) of rolling bearings is a
pivotal issue in industrial production. A crucial approach to tackling this
issue involves transforming vibration signals into health indicators (HI) to
aid model training. This paper presents an end-to-end HI construction method,
vector quantised variational autoencoder (VQ-VAE), which addresses the need for
dimensionality reduction of latent variables in traditional unsupervised
learning methods such as autoencoder. Moreover, concerning the inadequacy of
traditional statistical metrics in reflecting curve fluctuations accurately,
two novel statistical metrics, mean absolute distance (MAD) and mean variance
(MV), are introduced. These metrics accurately depict the fluctuation patterns
in the curves, thereby indicating the model's accuracy in discerning similar
features. On the PMH2012 dataset, methods employing VQ-VAE for label
construction achieved lower values for MAD and MV. Furthermore, the ASTCN
prediction model trained with VQ-VAE labels demonstrated commendable
performance, attaining the lowest values for MAD and MV.Comment: 17 figure
Utilizing Autoregressive Networks for Full Lifecycle Data Generation of Rolling Bearings for RUL Prediction
The prediction of rolling bearing lifespan is of significant importance in
industrial production. However, the scarcity of high-quality, full lifecycle
data has been a major constraint in achieving precise predictions. To address
this challenge, this paper introduces the CVGAN model, a novel framework
capable of generating one-dimensional vibration signals in both horizontal and
vertical directions, conditioned on historical vibration data and remaining
useful life. In addition, we propose an autoregressive generation method that
can iteratively utilize previously generated vibration information to guide the
generation of current signals. The effectiveness of the CVGAN model is
validated through experiments conducted on the PHM 2012 dataset. Our findings
demonstrate that the CVGAN model, in terms of both MMD and FID metrics,
outperforms many advanced methods in both autoregressive and non-autoregressive
generation modes. Notably, training using the full lifecycle data generated by
the CVGAN model significantly improves the performance of the predictive model.
This result highlights the effectiveness of the data generated by CVGans in
enhancing the predictive power of these models
A Data-Driven Reliability Estimation Approach for Phased-Mission Systems
We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example
Analyses of MicroRNA and mRNA Expression Profiles Reveal the Crucial Interaction Networks and Pathways for Regulation of Chicken Breast Muscle Development
There is a lack of understanding surrounding the molecular mechanisms involved in the development of chicken skeletal muscle in the late postnatal stage, especially in the regulation of breast muscle development related genes, pathways, miRNAs and other factors. In this study, 12 cDNA libraries and 4 small RNA libraries were constructed from Gushi chicken breast muscle samples from 6, 14, 22, and 30 weeks. A total of 15,508 known transcripts, 25,718 novel transcripts, 388 known miRNAs and 31 novel miRNAs were identified by RNA-seq in breast muscle at the four developmental stages. Through correlation analysis of miRNA and mRNA expression profiles, it was found that 417, 370, 240, 1,418, 496, and 363 negatively correlated miRNA–mRNA pairs of W14 vs. W6, W22 vs. W6, W22 vs. W14, W30 vs. W6, W30 vs. W14, and W30 vs. W22 comparisons, respectively. Based on the annotation analysis of these miRNA–mRNA pairs, we constructed the miRNA–mRNA interaction network related to biological processes, such as muscle cell differentiation, striated muscle tissue development and skeletal muscle cell differentiation. The interaction networks for signaling pathways related to five KEGG pathways (the focal adhesion, ECM-receptor interaction, FoxO signaling, cell cycle, and p53 signaling pathways) and PPI networks were also constructed. We found that ANKRD1, EYA2, JSC, AGT, MYBPC3, MYH11, ACTC1, FHL2, RCAN1, FOS, EGR1, and FOXO3, PTEN, AKT1, GADD45, PLK1, CCNB2, CCNB3 and other genes were the key core nodes of these networks, most of which are targets of miRNAs. The FoxO signaling pathway was in the center of the five pathway-related networks. In the PPI network, there was a clear interaction among PLK1 and CDK1, CCNB2, CDK1, and GADD45B, and CDC45, ORC1 and MCM3 genes. These results increase the understanding for the molecular mechanisms of chicken breast muscle development, and also provide a basis for studying the interactions between genes and miRNAs, as well as the functions of the pathways involved in postnatal developmental regulation of chicken breast muscle
Transcriptome Analysis of the Breast Muscle of Xichuan Black-Bone Chickens Under Tyrosine Supplementation Revealed the Mechanism of Tyrosine-Induced Melanin Deposition
The Xichuan black-bone chicken, which is a rare local chicken species in China, is an important genetic resource of black-bone chickens. Tyrosine can affect melanin production, but the molecular mechanism underlying tyrosine-induced melanin deposition in Xichuan black-bone chickens is poorly understood. Here, the blackness degree and melanin content of the breast muscle of Xichuan black-bone chickens fed a basic diet with five levels of added tyrosine (i.e., 0.2, 0.4, 0.6, 0.8, and 1.0%; these groups were denoted test groups I-V, respectively) were assessed, and the results showed that 0.8% tyrosine was the optimal level of added tyrosine. Moreover, the effects of tyrosine supplementation on the proliferation and tyrosinase content of melanocytes in Xichuan black-bone chickens were evaluated. The results revealed a dose-dependent relationship between tyrosine supplementation and melanocyte proliferation. In addition, 417 differentially expressed genes (DEGs), including 160 upregulated genes and 257 downregulated genes, were identified in a comparative analysis of the transcriptome profiles constructed using the pooled total RNA from breast muscle tissues of the control group and test group IV, respectively (fold change ≥2.0, P < 0.05). These DEGs were mainly involved in melanogenesis, the calcium signaling pathway, the Wnt signaling pathway, the mTOR signaling pathway, and vascular smooth muscle contraction. The pathway analysis of the DEGs identified some key genes associated with pigmentation, such as DCT and EDNRB2. In summary, the melanin content of breast muscle could be markedly enhanced by adding an appropriate amount of tyrosine to the diet of Xichuan black-bone chickens, and the EDNRB2-mediated molecular regulatory network could play a key role in the biological process of tyrosine-induced melanin deposition. These results have deepened the understanding of the molecular regulatory mechanism of melanin deposition in black-bone chickens and provide a basis for the regulation of nutrition and genetic breeding associated with melanin deposition in Xichuan black-bone chickens
Numerical and experimental evaluation of nasopharyngeal aerosol administration methods in children with adenoid hypertrophy
Article no 12390
- …