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Fault detection is an important part of aeroengine health management. Intelligent fault detection methods represented by machine learning have been widely studied. However, most studies assume that training and test data follow the same distribution, which is unrealistic. Due to the degradation of engine performance or change of engine operating environment, the historical operation data of aeroengines are different from the current operation data of the engine. If the engine history operation data are directly used to train a fault detection model, the fault detection of the current engine may lead to low efficiency and affect the reliability of fault detection. In order to overcome this problem, transfer learning is introduced into aircraft engine fault detection in this paper. This paper combines transfer learning with support vector data description (SVDD), a common fault detection algorithm, and proposes SVDD-based transfer learning (SVDD-TL). This algorithm takes the spherical center of the SVDD as the knowledge structure to transfer from the source domain to the target domain, which can improve the detection accuracy of the model in the target domain. A fault detection experiment for an aeroengine was designed. Single and mixed fault data were used in the experiment, and the variation of fault data quantity was considered. Experimental results showed that the proposed method can improve the fault detection accuracy of the model in the target domain and still have good detection performance when the amount of fault data changes. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are mediators of the aging process. The purpose of this work was to analyze the miRNA expression profiles of spermatozoa from men of different ages with normal fertility. Twenty-seven donors were divided into three groups by age (Group A, n = 8, age: 20–30 years; Group B, n = 10, age: 31–40 years; and Group C, n = 9, age: 41–55 years) for high-throughput sequencing analysis. Samples from 65 individuals (22, 22, and 21 in Groups A, B, and C, respectively) were used for validation by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2160 miRNAs were detected: 1223 were known, 937 were newly discovered and unnamed, of which 191 were expressed in all donors. A total of 7, 5, and 17 differentially expressed microRNAs (DEMs) were found in Group A vs B, Group B vs C, and Group A vs C comparisons, respectively. Twenty-two miRNAs were statistically correlated with age. Twelve miRNAs were identified as age-associated miRNAs, including hsa-miR-127-3p, mmu-miR-5100_L+2R-1, efu-miR-9226_L-2_1ss22GA, cgr-miR-1260_L+1, hsa-miR-652-3p_R+1, pal-miR-9993a-3p_L+2R-1, hsa-miR-7977_1ss6AG, hsa-miR-106b-3p_R-1, hsa-miR-186-5p, PC-3p-59611_111, hsa-miR-93-3p_R+1, and aeca-mir-8986a-p5_1ss1GA. There were 9165 target genes of age-associated miRNAs. Gene Ontology (GO) analysis of the target genes identified revealed enrichment of protein binding, membrane, cell cycle, and so on. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of age-related miRNAs for target genes revealed 139 enriched pathways, such as signaling pathways regulating stem cell pluripotency, metabolic pathways, and the Hippo signaling pathway. This suggests that miRNAs play a key role in male fertility changes with increasing age and provides new evidence for the study of the mechanism of age-related male fertility decline. [ABSTRACT FROM AUTHOR]

With the development of the Full Authority Digital Engine Controller (FADEC) technology, the aero-engine on-board model is widely used in Engine Health Management (EHM) and control. Due to the FADEC's limited computational capability and storage capacity, the model should not be very intricate; consequently, the interpolation model is widely utilized. Although the interpolation model's low precision precludes further development of on-board models for EHM and control. To address the trade-off between precision and complexity, a novel on-board modeling method is proposed based on the Nonlinear Autoregressive with Exogenous Inputs Backpropagation neural network (NARX-BPNN) trained using the mini-batch Levenberg–Marquardt (LM) algorithm on large Quick Access Recorder (QAR) data. The NARX model's features and time delay are chosen by referring to the line interpolation model, which gives interpretability for feature selection. The combination of a shallow neural network and big data training can guarantee the on-board model's real-time and storage requirements, as well as its generalizability. The mini-batch LM method can avoid both the local optimum problem in the shallow neural network and the storage difficulty associated with massive data while still achieving a rapid convergence rate due to the LM algorithm's global view. The NARX-BPNN models are compared to an existing line interpolation model using 100 different aero-engines' QAR data. The results reveal that accuracy may be increased by approximately 30% while maintaining superior dynamic performance and anti-noise capacity compared to the line interpolation approach. [ABSTRACT FROM AUTHOR]

Estimation of the aero-engine remaining useful life (RUL) is a significant part of prognostics and health management (PHM) and the basis of condition-based maintenance (CBM) which can improve the reliability and economy. Multiple operating conditions, nonlinear degradation, and early prediction are significant and distinctive issues compared with other prognostics problems. While these issues do not get enough attention and researches in aero-engine RUL estimation. In view of these points, three specific data preparation approaches and a novel loss function are introduced. The data preparation approaches can extract high-quality data for the long short-term memory (LSTM) neural network according to the characteristic of aero-engine degradation data. Among these approaches, operating condition normalization is an effective method to handle the multiple operating conditions problems, and RUL limitation identification is a novel method to identify the turning point of the nonlinear degradation process. The scoring function is an innovative loss function used to replace the mean square error (MSE) loss function which has a preference for early prediction. The comparisons with the original LSTM and some other approaches indicate that the combination of the data preparations and the scoring loss function is an effective solution for the above issues, and can achieve the best performance among the approaches. [ABSTRACT FROM AUTHOR]