A life prediction method based on MDFF and DITCN-ABiGRU mixed network model.

A single network model exhibits limitations in the life prediction of rotating machinery for the various fault types and uncertain fault occurrence. Therefore, a network prediction model combining multi-domain feature fusion (MDFF) and distributed TCN-Attention-BiGRU (DITCN-ABiGRU) is proposed to enable a more accurate life prediction of rotating machinery. Firstly, the features of vibration signals collected from multiple sensors are extracted in the time, frequency, and time-frequency domains. Subsequently, dimensionality reduction optimization is conducted on these multi-domain features to eliminate useless information features. The temporal convolutional network (TCN) model is constructed to capture the critical information reflecting the fault characteristics of rotating machinery through the attention mechanism, and the dependencies of the whole training process are captured by the BiGRU network. Finally, precise prediction of the lifespan of rotating machinery is achieved by constructing a health indicator curve (HI). The proposed methods are verified through the life prediction of rolling bearings from the IEEE PHM Challenge 2012 dataset and ball screw pairs from a designed experiment. The experimental results show that the proposed MDFF and DITCN-ABiGRU model achieves a better score and lower error than the convolutional neural network (CNN) and GRU models.

Preliminary Study on the Formation Mechanism of Malformed Sweet Cherry (Prunus avium L.) Fruits in Southern China Using Transcriptome and Metabolome Data.

Gibberellin (GA) is an important plant hormone that is involved in various physiological processes during plant development. Sweet cherries planted in southern China have always encountered difficulty in bearing fruit. In recent years, gibberellin has successfully solved this problem, but there has also been an increase in malformed fruits. This study mainly explores the mechanism of malformed fruit formation in sweet cherries. By analyzing the synthesis pathway of gibberellin using metabolomics and transcriptomics, the relationship between gibberellin and the formation mechanism of deformed fruit was preliminarily determined. The results showed that the content of GA3 in malformed fruits was significantly higher than in normal fruits. The differentially expressed genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were mainly enriched in pathways such as "plant hormone signal transduction", "diterpenoid biosynthesis", and "carotenoid biosynthesis". Using Quantitative Real-Time Reverse Transcription PCR (qRT-PCR) analysis, the gibberellin hydrolase gene GA2ox and gibberellin synthase genes GA20ox and GA3ox were found to be significantly up-regulated. Therefore, we speculate that the formation of malformed fruits in sweet cherries may be related to the accumulation of GA3. This lays the foundation for further research on the mechanism of malformed sweet cherry fruits.

A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes.

The detection of broken wires in steel wire ropes is of great significance for the production safety. However, the existing identification techniques mainly focus on the external broken wires problem. Here, the artificial feature extraction is one of the most important method, while only the prior knowledge of the artificial feature extraction method is adequate, the identification precision can be satisfied. Therefore, it is still a challenge to realize intelligent diagnosis for the broken wires. Besides, the identification of internal broken wires problem is still not well solved. In this paper, a quantitative identification method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) is proposed to solve the internal and external broken wires identification problem. The key technology of this research is that the fault information from the time-frequency images converted by the magnetic flux leakage (MFL) signals can be automatically extracted through a designed CNN. The main innovation is that the complex signal processing work can be eliminated and the internal and external broken wires can be accurately identified simultaneously by combining CWT and CNN. The experimental results of a steel wire rope test rig are compared with the traditional recognition method, which shows that the proposed method achieved significant improvement on detection accuracy and recognition performance.

Metabolite profile comparison of a graft chimera 'Hongrou Huyou' (Citrus changshan-huyou + Citrus unshiu) and its two donor plants.

BACKGROUND: Chimeras synthesized artificially by grafting are crucial to the breeding of perennial woody plants. 'Hongrou Huyou' (Citrus changshan-huyou + Citrus unshiu) is a new graft chimera originating from the junction where a Citrus changshan-huyou ("C") scion was top-grafted onto a stock Satsuma mandarin 'Owari' (C. unshiu, "O"). The chimera was named OCC because the cell layer constitutions were O for Layer 1(L1) and C for L2 and L3. In this study, profiles of primary metabolites, volatiles and carotenoids derived from different tissues in OCC and the two donors were investigated, with the aim of determining the relationship between the layer donors and metabolites. RESULTS: The comparison of the metabolite profiles showed that the amount and composition of metabolites were different between the peels and the juice sacs, as well as between OCC and each of the two donors. The absence or presence of specific metabolites (such as the carotenoids violaxanthin and ß-cryptoxanthin, the volatile hydrocarbon germacrene D, and the primary metabolites citric acid and sorbose) in each tissue was identified in the three phenotypes. According to principal component analysis (PCA), overall, the metabolites in the peel of the chimera were derived from donor C, whereas those in the juice sac of the chimera came from donor O. CONCLUSION: The profiles of primary metabolites, volatiles and carotenoids derived from the peels and juice sacs of OCC and the two donors were systematically compared. The content and composition of metabolites were different between the tissues and between OCC and the each of the two donors. A clear donor dominant pattern of metabolite inheritance was observed in the different tissues of OCC and was basically consistent with the layer origin; the peel of the chimera was derived from C, and the juice sacs of the chimera came from O. These profiles provide potential chemical markers for genotype differentiation, citrus breeding assessment, and donor selection during artificial chimera synthesis.

A Sensor for Broken Wire Detection of Steel Wire Ropes Based on the Magnetic Concentrating Principle.

Electromagnetic testing is the most widely used technique for the inspection of steel wire ropes. As one of the electromagnetic detecting approaches, the magnetic flux leakage (MFL) method has the best effect for the detection of broken wires. However, existing sensors based on MFL method still have some problems. (1) The size of the permanent magnet exciter is usually designed according to experience or rough calculation, and there is not enough depth analysis for its excitation performance; (2) Since the detectable angular range for a single Hall component is limited, Hall sensor arrays are often employed in the design of MFL sensors, which will increase the complexity of the subsequent signal processing due to the extensive use of Hall components; (3) Although the new magneto-resistance sensor has higher sensitivity, it is difficult to be applied in practice because of the requirement of the micron-level lift-off. To solve these problems, a sensor for the detection of broken wires of steel wire ropes based on the principle of magnetic concentration is developed. A circumferential multi-circuit permanent magnet exciter (CMPME) is employed to magnetize the wire rope to saturation. The traditional Hall sensor array is replaced by a magnetic concentrator to collect MFL. The structural parameters of the CMPME are optimized and the performance of the magnetic concentrator is analyzed by the finite element method. Finally, the effectiveness of the designed sensor is verified by wire breaking experiment. 1-5 external broken wires, handcrafted on the wire rope with a diameter of 24 mm, can be clearly identified, which shows great potential for the inspection of steel wire ropes.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.