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

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

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains