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Aiming at the problem of low accuracy of unsupervised learning anomaly detection algorithm, a dual-input anomaly detection method based on deep reinforcement learning was proposed. The proposed model mainly consists of a feature extractor and anomaly detector. Based on the deep reinforcement learning framework, the feature extractor uses a dual-input deep neural network to form the current value network and the target value network, which are used to extract the low-dimensional feature vectors. Based on the 3 σ principle, the reward function of reinforcement learning is designed to reward and punish the output results of the model during training. The model was trained only with the normal data, and the extracted feature vector of the normal class was used as the input of the anomaly detector to complete the learning of the detector. During the test, the input anomaly detection was realized based on the dual-input convolutional neural network, and the anomaly detector was completed by learning. To illustrate the generality and generalization performance of the proposed method, four sets of image data and two sets of rolling bearing fault data in different fields were verified respectively. At the same time, the proposed method is applied to the fault detection of a real aero-engine rolling bearing.The results show that the proposed model has high anomaly detection accuracy, which is superior to the current optimal method. [ABSTRACT FROM AUTHOR]

An innovative deep-learning-based model, namely, deep anomaly detection with the same probability distribution (DADSPD) is proposed to improve the accuracy of anomaly detection (AD) of rolling bearings driven only by normal data. First, the main framework of feature extraction based on a residual network was established, and a three-layer encoder structure was used to extract multidimensional features. Second, a new loss function based on the same probability distribution is designed, and the function of its probability distribution is to complete the training of the model by calculating the similarity between the outputs. Subsequently, the vibration data were preprocessed using wavelet and envelope analysis, and the processed data are converted into two-dimensional image signals and used as the input of the DADSPD. Finally, the model is verified on three sets of run-to-failure experimental datasets of rolling bearing. The results demonstrate that the proposed DADSPD model reaches more than 99%, which indicates that the DADSPD model has a high fault early warning and AD capability. [ABSTRACT FROM AUTHOR]

In order to make full use of the advantages of various oil analysis methods and improve the accuracy of wear fault diagnosis of aero-engine rolling bearing, a fuzzy fusion diagnosis method for aero-engine wear fault was proposed. Firstly, according to the characteristics of the respective characteristics of spectral analysis, ferrographic analysis, particle count analysis, physical and chemical analysis data, the oil analysis data were fuzzified based on user-defined membership function, so as to obtain the evidence credibility and rule credibility of characterization spectral analysis, ferrographic analysis, particle count analysis, physical and chemical analysis data, and obtain the comprehensive credibility. Then the D-S evidence theory diagnosis of oil sample data was realized based on the D-S evidence theory. The proposed fusion diagnosis method was applied to aero-engine wear fault diagnosis. The algorithm was verified by using the oil simulation analysis data of a certain engine in different wear periods. The result shows that the proposed method has high diagnosis accuracy and sensitivity. [ABSTRACT FROM AUTHOR]

Inflammatory cascade and extracellular matrix remodeling have been identified as pivotal pathological factors in the progression of intervertebral disc degeneration (IDD), but the mechanisms underlying the aberrant activation of transcription during nucleus pulposus (NP) cell degeneration remain elusive. Super-enhancers (SEs) are large clusters of adjacent lone enhancers, which control expression modes of cellular fate and pathogenic genes. Here, we showed that SEs underwent tremendous remodeling during NP cell degeneration and that SE-related transcripts were most abundant in inflammatory cascade and extracellular matrix remodeling processes. Inhibition of cyclin-dependent kinase 7, a transcriptional kinase-mediated transcriptional initiation in trans-acting SE complex, constricted the transcription of inflammatory cascades, and extracellular matrix remodeling-related genes such as IL1β and MMP3 in NP cells, meanwhile, also restrained the transcription of Mmp16 , Tnfrsf21 , and Il11ra1 to retard IDD in rats. In summary, our findings clarify SEs control the transcription of genes associated with inflammatory cascade and extracellular matrix remodeling during NP cell degeneration and identify inhibition of the cyclin-dependent kinase 7, required for SE-mediated transcriptional activation, as a therapeutic option for IDD., (© 2023 Li et al.)

Long noncoding RNAs (lncRNAs) play critical roles in tumor progression regulation, including osteosarcoma. Evidence indicates that N 6 -methyladenosine (m 6 A) modification modulates mRNA stability to regulate osteosarcoma tumorigenesis. Here, present research aims to detect the roles of m 6 A-modified lncRNA FOXD2-AS1 in the osteosarcoma pathophysiological process. Clinical data unveiled that osteosarcoma patients with higher FOXD2-AS1 expression had a poorer overall survival rate compared to those with lower FOXD2-AS1 expression. Functional research illuminated that FOXD2-AS1 accelerated the migration, proliferation and tumor growth in vitro and in vivo. Mechanistically, a remarkable m 6 A-modified site was found on the 3'-UTR of FOXD2-AS1, and m 6 A methyltransferase WTAP (Wilms' tumor 1 associated protein) promoted the methylation modification, thus enhancing the stability of FOXD2-AS1 transcripts. Furthermore, FOXD2-AS1 interacted with downstream target FOXM1 mRNA through m 6 A sites, forming a FOXD2-AS1/m 6 A/FOXM1 complex to heighten FOXM1 mRNA stability. In conclusion, these findings propose a novel regulatory mechanism in which m 6 A-modified FOXD2-AS1 accelerates the osteosarcoma progression through m 6 A manner, which may provide new concepts for osteosarcoma tumorigenesis.