Vote-Based Feature Selection Method for Stratigraphic Recognition in Tunnelling Process of Shield Machine

Shield machines are currently the main tool for underground tunnel construction. Due to the complexity and variability of the underground construction environment, it is necessary to accurately identify the ground in real-time during the tunnel construction process to match and adjust the tunnel parameters according to the geological conditions to ensure construction safety. Compared with the traditional method of stratum identifcation based on staged drilling sampling, the real-time stratum identifcation method based on construction data has the advantages of low cost and high precision. Due to the huge amount of sensor data of the ultra-large diameter mud-water balance shield machine, in order to balance the identifcation time and recognition accuracy of the formation, it is necessary to screen the multivariate data features collected by hundreds of sensors. In response to this problem, this paper proposes a voting-based feature extraction method (VFS), which integrates multiple feature extraction algorithms FSM, and the frequency of each feature in all feature extraction algorithms is the basis for voting. At the same time, in order to verify the wide applicability of the method, several commonly used classifcation models are used to train and test the obtained efective feature data, and the model accuracy and recognition time are used as evaluation indicators, and the classifcation with the best combination with VFS is obtained. The experimental results of shield machine data of 6 diferent geological structures show that the average accuracy of 13 features obtained by VFS combined with diferent classifcation algorithms is 91%;among them, the random forest model takes less time and has the highest recognition accuracy, reaching 93%, showing best compatibility with VFS. Therefore, the VFS algorithm proposed in this paper has high reliability and wide applicability for stratum identifcation in the process of tunnel construction, and can be matched with a variety of classifer algorithms. By combining 13 features selected from shield machine data features with random forest, the identifcation of the construction stratum environment of shield tunnels can be well realized, and further theoretical guidance for underground engineering construction can be provided.

Gas Leakage Detection and Pressure Difference Identification by Asymmetric Differential Pressure Method

Currently,the measurement methods for pneumatic system leakage include bubbling,ultrasonic,and pressure detection methods.These methods are sensitive to high-precision sensors,long detection times,and stable external environments.The traditional differential pressure method involves severe differential pressure fluctuations caused by environmental pressure fluctuations or electromagnetic noise interference of sensors,leading to inaccurate detection.In this paper,a differential pressure fitting method for an asymmetric differential pressure cylinder is proposed.It overcomes the limitation of the detection efficiency caused by the asynchronous temperature recovery of the two chambers in the asymmetric differential pressure method and uses the differential pressure substitution equation to replace the differential calculation of the differential pressure.The improved differential pressure method proposes an innovation based on the detection principle and calculation method.Additionally,the influence of the parameters in the differential pressure substitution equation on the leakage calculation results was simulated,and the specific physical significance of the parameters of the differential pressure substitution equation was explained.The experiments verified the fitting effect and proved the accuracy of this method.Compared with the traditional differential pressure method,the maximum leakage deviation of inhibition was 0.5 L/min.Therefore,this method can be used to detect leaks in air tanks.

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN

In satellite anomaly detection,there are some problems such as unbalanced sample distribution,fewer fault samples,and unobvious anomaly characteristics.These problems cause the extisted anomaly detection methods are difficult to train accurate classification model,and the accuracy of anomaly detection is hard to improve.At the same time,the monitoring data of satellite has high dimension and is difficult to extract effective features.Based on the DTW over-sampling method,this paper realizes the over-sampling of fault samples in satellite time series,and constructs a distributed and balanced time series data set.The Fast-DTW method is applied to calculate the distance between different time series,which can improve the speed of similarity calculation.KNN(K-Nearest Neighbor)method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k.The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.

A novel low pressure-difference fluctuation electro-hydraulic large flowrate control valve for fuel flowrate control of aeroengine afterburner system

To address the control accuracy of large fuel flowrate during pressure fluctuation,a novel electro-hydraulic fuel metering unit(FMU)is constructed for afterburner fuel system of military aeroengine.Different from the previous FMU,the proposed FMU can achieve the higher precision opening control by a new metering valve with double control chambers(MVDCC),and realize the lower pressure difference fluctuation regulating by a novel two-stage constant pressure difference compensated valve(CPDCV)with dynamic damping orifice and damping piston.The experimental and AMESim simulation results verify the validity and superiority of the novel FMU.Since the temperature-induced variation in fuel properties and device capabilities may degrade or even impair the properties of novel FMU,the discharge flowrate is analyzed by global sensitivity analysis to research the effect proportion of each factor,the temperature effect is explored to ensure the working reliability in long-span temperature variation.Finally,the optimization of structure parameters for novel CPDCV can further reduce pressure difference fluctuation during pressure regulation,and the overshoot,adjust time and the integral of time multiplied by absolute value of error(ITAE)can be reduced by 24%,30%and 26%,respectively.This paper provides a reference for improving the stability of large flowrate during pressure fluctuation.

Multiobjective trajectory optimization of intelligent electro-hydraulic shovel

Multiobjective trajectory planning is still face challenges due to certain practical requirements and mul-tiple contradicting objectives optimized simultaneously.In this paper,a multiobjective trajectory optimization approach that sets energy consumption,execution time,and excavation volume as the objective functions is presented for the electro-hydraulic shovel(EHS).The proposed cubic polynomial S-curve is employed to plan the crowd and hoist speed of EHS.Then,a novel hybrid constrained multiobjective evolutionary algorithm based on decomposition is proposed to deal with this constrained multiobjective optimization problem.The normalization of objectives is introduced to minimize the unfavorable effect of orders of magnitude.A novel hybrid constraint handling approach based onε-constraint and the adaptive penalty function method is utilized to discover infeasible solution information and improve population diversity.Finally,the entropy weight technique for order preference by similarity to an ideal solution method is used to select the most satisfied solution from the Pareto optimal set.The performance of the proposed strategy is validated and analyzed by a series of simulation and experimental studies.Results show that the proposed approach can provide the high-quality Pareto optimal solutions and outperforms other trajectory optimization schemes investigated in this article.