Complex Nonlinear Characteristics Research Articles

Wholesale price forecasts of green grams using the neural network

PurposeAgriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.Design/methodology/approachIn order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.FindingsOur model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.Originality/valueUtilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

Although the simulated moving bed (SMB) process boasts advantages such as high productivity and low consumption, the cost of obtaining optimized parameters through practical experiments to control the separation process can be enormous due to its complex nonlinear characteristics. Consequently, the successful transformation of the SMB separation process into a mathematical dynamic model for computer simulation would greatly reduce the research costs associated with experimental studies. In this study, the Crank–Nicolson method was employed to discretize and dynamize the SMB process, enabling the simulation of processes under both linear and Langmuir isotherms. The results of the simulation experiments demonstrated the feasibility and high efficiency of this approach, thereby establishing a solid foundation for further advancements in online control strategies.

Prediction of Diffuse Attenuation Coefficient Based on Informer: A Case Study of Hangzhou Bay and Beibu Gulf

Marine water quality significantly impacts human livelihoods and production such as fisheries, aquaculture, and tourism. Satellite remote sensing facilitates the predictions of large-area marine water quality without the need for frequent field work and sampling. Prediction of diffuse attenuation coefficient (Kd), which describes the speed at which light decays as it travels through water, obtained from satellite-derived ocean color products can reflect the overall water quality trends. However, current models inadequately explore the complex nonlinear features of Kd, and there are difficulties in achieving accurate long-term predictions and optimal computational efficiency. This study innovatively proposes a model called Remote Sensing-Informer-based Kd Prediction (RSIKP). The proposed RSIKP is characterized by a distinctive Multi-head ProbSparse self-attention mechanism and generative decoding structure. It is designed to comprehensively and accurately capture the long-term variation characteristics of Kd in complex water environments while avoiding error accumulation, which has a significant advantage in multi-dataset experiments due to its high efficiency in long-term prediction. A multi-dataset experiment is conducted at different prediction steps, using 70 datasets corresponding to 70 study areas in Hangzhou Bay and Beibu Gulf. The results show that RSIKP outperforms the five prediction models based on Artificial Neural Networks (ANN, Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Long Short-Term Memory Networks (LSTM)). RSIKP captures the complex influences on Kd more effectively to achieve higher prediction accuracy compared to other models. It shows a mean improvement of 20.6%, 31.1%, and 22.9% on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Particularly notable is its outstanding performance in the long time-series predictions of 60 days. This study develops a cost-effective and accurate method of marine water quality prediction, providing an effective prediction tool for marine water quality management.

Development and Implementation of a Multilayer Deep Learning-Based Bank Credit Risk Forecasting System

The complexity of the financial environment and the international community makes the capital flow face various challenges, and it is difficult to obtain accurate credit prediction results in the actual application environment. Considering the complex non-linear characteristics of customer information, the Analytic Hierarchy Process is studied to meet the needs of bank credit risk assessment. On this basis, a depth neural network with different complexities was selected for the three indicators built to classify the features. The composition of the neural network module and the number of neurons were determined by experiment, and Dropout was used to prevent overfitting of the test dataset. Stability and ablation experiments showed that the model can control the error between datasets to 0.021. The ablation experiment showed that the numbers of hidden layers and neurons were the best. Simulation tests showed that the sensitivity and accuracy of this method were 85.25% and 92.55%, respectively, which were superior to other classification methods. The real data of banks in the past four years were tested. The results could accurately classify the risks of enterprises and individual customers, and the results of stress test showed that the model is stable. It is found that traditional credit risk assessment models rely on statistical means and rule decisions, and these methods may not fully reveal the complex non-linear relationship and the internal relationship of financial indicators in high-dimensional data. The combination of deep learning technology and hierarchical analysis can better deal with and explain the complex non-linear problems in bank risk assessment.

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME’s center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.

Dynamic coil switching strategies for significant efficiency increases in electromagnetic energy generators

The development of self-powering systems has been recognized as critical such that innovative stand-alone emerging technologies can operate sustainably from scavenged ambient energy. Electromagnetic generators (EMGs) using magnetic levitation architectures for mechanical vibration energy harvesting are a promising technology that can be tailored to specific needs and provide low-cost electric powering for both small-scale and large-scale devices. They also present non-complex design, with low maintenance requirements and can operate with stable performance for long periods of time. Despite these prominent features, their complex non-linear and hysteresis-based resonant characteristics makes performance optimization hard to achieve and still needs to be addressed as a function of the input excitation. Numerical and experimental results are here provided to demonstrate the effectiveness of a new concept of EMG that aims to dynamically adapt the coil-array architecture throughout its operation to ensure maximum harvested power and to optimize the transduction mechanism efficiency. The self-adaptive motion-driven levitation-based autonomously rearranges each coil independently as a function of the instantaneous time-varying characteristics of the levitating magnet (LM) position. The mechanism features two dynamic coil switching strategies: (i) on/off switching, by short circuiting, with transmission gate switches, the coils without influence on the electromotive force; and (ii) reversing polarity switching, to avoid the sum of electromotive forces cancels each other. Average output powers of 635 mW (up to ∼4.1 W of peak power) were obtained with only the 4-centre (out of 14) permanently active coils, while only 292 mW (up to 833 mW of peak power) were achieved with the 14-coils permanently connected under optimal load conditions and harmonic translational input excitations with 15 Hz frequency and 20 mm amplitude. However, the adaptive generator was able to provide an impressive average power output of 3 W under the same conditions. Up to 14-fold larger output average power and 5.5-fold larger electric efficiency demonstrate the potential of the proposed coil switching self-adaptation system for enhancing the total energy conversion from general widespread mechanical vibrations.

Fracture identification of carbonate reservoirs by deep forest model: An example from the D oilfield in Zagros Basin

Identifying fractures along a well trajectory is of immense significance in determining the subsurface fracture network distribution. Typically, conventional logs exhibit responses in fracture zones, and almost all wells have such logs. However, detecting fractures through logging responses can be challenging since the log response intensity is weak and complex. To address this problem, we propose a deep learning model for fracture identification using deep forest, which is based on a cascade structure comprising multi-layer random forests. Deep forest can extract complex nonlinear features of fractures in conventional logs through ensemble learning and deep learning. The proposed approach is tested using a dataset from the Oligocene to Miocene tight carbonate reservoirs in D oilfield, Zagros Basin, Middle East, and eight logs are selected to construct the fracture identification model based on sensitivity analysis of logging curves against fractures. The log package includes the gamma-ray, caliper, density, compensated neutron, acoustic transit time, and shallow, deep, and flushed zone resistivity logs. Experiments have shown that the deep forest obtains high recall and accuracy (>92%). In a blind well test, results from the deep forest learning model have a good correlation with fracture observation from cores. Compared to the random forest method, a widely used ensemble learning method, the proposed deep forest model improves accuracy by approximately 4.6%.

How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective

The multi-modal combination of "dockless bikesharing (DBs) + metro" can effectively addresses the "last mile" challenge, and its importance to improve the quality of metro integration and residents' commuting efficiency is becoming increasingly prominent. As one of the important determining factors of transportation, the urban built environment directly affects the integration travel behavior of residents. To better understand this issue, it is necessary to conduct in-depth research on the influence mechanism of urban built environment on DBs-metro integration cycling (DBsMIC), and reveal its complex nonlinear characteristics and spatial heterogeneity. Therefore, taking Shenzhen as a case study, this study first introduced the OLS regression model to detect the significant built environmental impact factors of DBsMIC. Secondly, the random forest regression model was used to analyze its nonlinear influence. Finally, aiming at the problem of spatial non-stationarity of influencing effects, the spatial heterogeneity is analyzed from both global and local perspectives based on the MGWR model. The results show that the significant impact indices of the built environment of the integration cycling volume vary in different time periods. Overall, seven factors such as road and residential density have a significant impact on DBsMIC. Among them, the job-housing balance and the bus line density have a negative impact, while the rest have a positive impact. Meanwhile, the nonlinear effect of the built environment on DBsMIC is obvious, and there is an obvious marginal effect. In addition, the nonlinear impact of the built environment on DBsMIC in different time periods has spatial heterogeneity and spatial marginal effects. For example, the influence of road density shows a global positive correlation effect in multiple time periods, and the promotion effect increases from southwest to northeast. This study can provide auxiliary decision-making support for the construction of an efficient, green and sustainable urban transportation system according to local conditions.

Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height

Accurate forecasting of significant wave heights (SWH) plays an important role in the generation and conversion of ocean wave energy, being a promising clean energy source, while a challenging task since the SWH is produced by a complex natural process. Additionally, some deep forecasting models integrate parallel and recursive structures via unexplained ways to present intelligent fitting, leading to insufficient inheritance of hybrid advantages. Since biological multisensory systems automatically cope with complex time series, this paper explores a bio-multisensory-inspired gate-attention coordination network (BGC-Net) for forecasting SWH. Specifically, inspired by the multisensory cognitive process, the BGC-Net includes sensory capture, brain memory, and forecasting components, and their collaboration offers a clear multi-level handling process. In internal implementations, through mimicking that multiple senses independently capture external stimuli, the first component adopts several activation functions for suppressing data fluctuations and detecting the complex nonlinear and linear features. Through mimicking that the left and right hemispheres interact and collaborate electrical impulses, the second component integrates both gate-driven recursive and self-attention-driven parallel structures to extract multivariate and temporal dependencies at the feature level. Through mimicking that a higher cerebral cortex fuses extracted information for the final cognition, the third component proposes three feature fusion ways of concatenate, attention, and gated fusion, and takes the best one to generate SWH forecasts. In summary, the BGC-Net not only has interpretability at the biological level but also effectively integrates the advantages of parallel and recursive structures. Five experiments and six analyses all indicate that the BGC-Net exceeds 17 baselines, has root mean squared error (RMSE) average improvements with 22.30% and 19.22% for two datasets, and provides references to support SWH forecasting.

Expendable Conductivity–Temperature–Depth-Assisted Fast Underwater Sound Speed Estimation by Convolutional Neural Network with Reduced Fully Connected Layers

Obtaining accurate sound speed profiles (SSPs) in near-real-time is of great significance for ocean exploration, underwater communication and improving the performance of sonar systems. In response to the problem that traditional sound speed estimation methods cannot obtain real-time sound speed distribution or rely too much on sonar observation data, we propose an SSP estimation method based on a convolutional neural network with reduced fully connected layers (RFC-CNN) in this paper. This method utilizes neural networks to extract the complex nonlinear features of various types of data. With the help of the historical SSPs and shallow seawater sound speed and temperature data obtained by expendable conductivity–temperature–depth probes (XCTDs), a more accurate estimation of the regional sound speed distribution can be realized quickly. This approach can save the observation cost and significantly improve the real-time performance of SSP estimation.

Nonlinear vibration control of magnetic bearing system considering positive and negative stiffness

The nonlinear dynamics are studied for an active magnetic bearing (AMB) system under the low-frequency sinusoidal excitation, which can affect the operational stability of a high-speed magnetic bearing. A single-degree-of-freedom nonlinear dynamics model of magnetic bearing was developed with a c-type active magnetic bearing as the research target. The characteristics of the magnetic bearing system show positive and negative stiffness for different feedback gains. The amplitude–frequency response curves of the vibration characteristics of the positive stiffness magnetic bearing system were obtained by using the method of multiple scales. The influence of each parameter on the resonance amplitude and nonlinearity of the magnetic bearing system is understood by analyzing the amplitude–frequency response curve. The bifurcation diagram for the rotor with rotor mass, and control parameters is derived from the numerical simulation of the negative stiffness magnetic bearing system. The parameter intervals that cause single-cycle vibration, double-cycle vibration, and chaotic state of the magnetic bearing rotor are discovered by analyzing the bifurcation diagrams of each parameter. The simulation results show that the magnetic bearing system exhibits complex nonlinear dynamic characteristics when it is a negative stiffness system.

Nonlinear vibrations and control of PFG-GR laminated composite cantilever rectangular variable cross-section plate with NPPF controller

This paper studies the nonlinear vibrations and control of the piezoelectric functionally graded graphene-reinforced laminated composite cantilever rectangular (PFG-GRLCCR) variable cross-section plate with the NPPF controller under the simultaneous resonant cases. The nonlinear dynamic governing equations of motion for the PFG-GRLCCR variable cross-section plate are obtained by using the extended Halpin-Tsai mode, linear constitutive equations of the piezoelectric materials, classical laminated plate theory, von Karman large deformation theory, and Hamilton principle. Considering Galerkin method, the nonlinear ordinary differential governing equations are obtained. The nonlinear positive position feedback (NPPF) controllers are designed according to the similarity principle. The multiple scale method is used to investigate the nonlinear vibration characteristics of the stable motion for the PFG-GRLCCR variable cross-section plate with the NPPF controllers. The amplitude-frequency response curves, force-amplitude response curves, time histories, phase portraits, and bifurcation diagrams are given to investigate the nonlinear dynamic characteristics of the PFG-GRLCCR variable cross-section plate with the NPPF controller. The results demonstrate that the NPPF controller can suppress the nonlinear vibrations of the structure well, and the effectiveness of the controllers is best when the NPPF controller has a similar nonlinear characteristic with the structure. The complex nonlinear vibration characteristics appear for the PFG-GRLCCR variable cross-section plate with the NPPF controller.

Peak load estimation of renewable energy generation based on imitator dynamic algorithm

AbstractIn the field of renewable energy generation forecasting, it is crucial to accurately estimate the peak load. However, due to the complex nonlinear characteristics of the data, the traditional long short-term memory network performs poorly in processing these data. This study introduces the imitator dynamic algorithm, which is able to generate samples close to the real situation by learning the change pattern of the data. Extensive experimental tests show that with the number of iterations increasing to 200, the prediction accuracy of the model reaches 62.35%, which is significantly better than that of the long short-term memory network, although it is decreased compared with the initial iteration. The imitator dynamic algorithm accurately learns the unknown data distribution according to two metrics of probability density and cumulative distribution within 5% error, demonstrating good generalization ability and robustness. These research results are of great significance for predicting the actual generation capacity of renewable energy. It not only enables grid operators to accurately predict and schedule power generation, but also supports sustainable energy development by improving grid stability and promoting the use of renewable energy.

Critical penetrating vibration evolution behaviors of the gas-liquid coupled vortex flow

The gas-liquid coupled vortex flow (GCVF), as a complex physical phenomenon, has been encountered in some sustainable productions, such as chemical cleaner production, continuous steel pouring processes, and energy conversion in the tidal power plant. Its critical penetrating state recognition is essential to improve sustainable production efficiency and equipment lifetime. Due to the gas-liquid coupling transport and fluid nonlinear excitation, the state recognition oriented to the GCVF critical penetrating still faces significant challenges. To address the above matter, a fluid-structure coupling modeling and solution method based on the level set and residue theorems is put forward to obtain the GCVF-induced vibration evolution mechanism at the critical penetrating state. A wavelet packet-based signal processing method is proposed to obtain the evolution law of the peak component of the GCVF-induced vibration. Research results found that the proposed modeling and method can better reveal the GCVF dynamic evolution regularities in the critical penetrating state. The GCVF-induced vibration exhibits energy peak and complex nonlinear pulse characteristics in the critical penetrating process. The relevant research work can provide multi-source sensing data support for the research and development of a fluid-induced vibration detection system, laying the foundation for sustainable metallurgy production, chemical cleaner extraction, and hydroelectric energy conversion.

A novel real-time multi-step forecasting system with a three-stage data preprocessing strategy for containerized freight market

The management and decision-making of the shipping market rely heavily on the accurate forecasting of the China Containerized Freight Index (CCFI); however, this is still a challenging task. Furthermore, CCFI is influenced by many factors and exhibits complex nonlinear characteristics, which may cause the evolution of its distributions over time, i.e., the concept drift issue. However, previous studies mainly focused on the simple application of single data preprocessing and machine learning, while ignoring the significance of real-time and multi-step forecasting, which may not effectively deal with the concept drift issue and result in unsatisfactory results. Additionally, some studies disregarded the data leakage problem caused by the one-time decomposition of all datasets. Therefore, a novel real-time multi-step forecasting system with multiple rolling decomposition strategy based on streaming data is designed. It includes data preprocessing, forecasting, and evaluation modules. A three-stage data preprocessing strategy is proposed in the first module to overcome the single preprocessing defect. It can eliminate the adverse effects of outliers, extract main features, and reduce system complexity. Moreover, the forecasting and evaluation modules are designed to enhance the forecasting performance and verify the system’s validity. Considering the obvious concept drift problem of CCFI during the Coronavirus Disease 2019 (COVID-19) epidemic, datasets from the COVID-19 epidemic are selected for experimental verification to verify the system’s ability to solve this problem. Empirical studies demonstrate the superiority of the developed system over the compared models in terms of universality and robustness. Therefore, the proposed system is an effective prediction method for the containerized freight market in the post epidemic period and an alternative for forecasting other streaming data with the concept drift issue.

Nonlinear Dynamic Granger Causality Analysis Framework for Root-Cause Diagnosis of Quality-Related Faults in Manufacturing Processes

Root-cause diagnosis of quality-related faults plays a crucial role in ensuring stable product quality and high-efficient production for modern manufacturing processes. However, there exists complex nonlinear and dynamic sequential characteristics for process and quality data before and after faults, which contain important fault information. How to fully explore and use this information to locate the root-cause and identify the propagation path is a hot topic. Thus, a new nonlinear dynamic Granger causality analysis method is developed for root-cause diagnosis, which will provide timely and useful reference information to take reasonable measures for field engineers. First, an optimal variable division based on minimal redundancy maximal relevance algorithm is presented to get the quality-related variables. Then, a new attention-based random disturbance gated recurrent unit is designed for nonlinear dynamic Granger causality analysis, aiming at locating the root-cause and identifying the propagation path of quality-related faults. A typical manufacturing process, the hot strip mill process is used for verification. The results show the practicability of the proposed method, and its strong robustness to noise. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Reasonable and feasible root-cause diagnosis technology will provide timely reference information for field operators to take maintenance measures. However, the nonlinear and dynamic characteristics of time-series data before and after quality-related faults have brought great challenges to the traditional root-cause diagnosis techniques. To address this issue, a practical nonlinear dynamic Granger causality analysis method is proposed, which is purely data driven without knowing complex mechanism knowledge. It will provide feasible solutions for safety monitoring of hot strip mill process as the representative of manufacturing processes.

An adaptive deep multi-task learning approach for citywide travel time collaborative estimation

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network.

Characterizing the mode transition features of a two-phase thermofluidic oscillator

Two-phase thermofluidic oscillator (TPTO) is a promising technology for harvesting low-grade heat. The multi-physics coupling within TPTO adds to the complex nonlinear dynamic characteristics that affect the operation, while not yet sufficiently analyzed. The mode transition features of a TPTO were experimentally characterized via nonlinear dynamic analysis. The bifurcations were studied with regards to the effect of temperature ratio and relative thermal penetration depth. Mass-spring and acoustic-electric analogy models were developed to predict the incommensurate resonant frequencies, which were then experimentally verified. The experimental results show that the TPTO experiences a series of bifurcations by increasing the temperature ratio, leading to the occurrence of oscillation modes such as the high-frequency limit cycle (HFLC) oscillation, quasiperiodic oscillation and low-frequency limit cycle (LFLC) oscillation. The high frequency in HFLC is approximately 3.1 Hz, which is the natural frequency of the TPTO. The low frequency close to 0.9 Hz in LFLC is the intrinsic frequency of the looped tube. The stability curves of the 2nd, 3rd, and 4th bifurcations show a trend of first growing and then dropping with the increasing relative thermal penetration depth of working fluid along the regenerator. The potential applications of TPTO are then discussed. This study offers insights for comprehending the mechanism of mode transition and provides recommendations for the design and operation of TPTO.

Power load combination forecasting system based on longitudinal data selection

In the current situation of energy supply shortage and surging demand, effective and stable load forecasting is essential to ensure reliable power supply and the security of the power system. However, due to some factors such as periodicity and seasonality, the power load sequence shows complex nonlinear characteristics. Meanwhile, the current load forecasting lacks the ability to explore the data deeply, and it is difficult to accurately predict the short-term trend and fluctuation range. To remedy these limitations, this study proposes a hybrid point-interval prediction system (HPILS). The system integrates data preprocessing, optimal model selection, multi-objective optimization combination and interval prediction modules. To verify the performance of the proposed system, four load data sets in Australia are used as examples to conduct experiments. The experimental results demonstrate that HPILS can effectively provide the predicting power load trend changes and fluctuation ranges. Specifically, compared with the benchmark model, HPILS has a 13.47% ∼ 67.89% improvement in point prediction and a 1.67% ∼ 72.08% improvement in interval prediction. In addition, a series of discussion tests are performed to verify the superiority of the proposed system and further confirm the validity of our proposed system.

A novelty motion reliability analysis method for locking mechanism of folding wing

The locking mechanism of folding wing is investigated as an individual object while the correlation between the locking performance and the deployment process is neglected. Its performance function frequently exhibits complex nonlinear dynamic characteristics and implicity during locking process, making it described by time-consuming dynamic simulation model rather than analytical expressions. Accordingly, when it comes to motion reliability, the physical model requires frequent manual modifications, and the numerous time-consuming simulation models need to be calculated which results in low computational efficiency in motion reliability analysis, especially, in case of small failure probability. Focusing on these problems, a hybrid model, which is based on integrating ADAMS with MATALAB, is established to understand the kinematic and dynamic characteristics of the locking mechanism of folding wing. The hybrid model is composed of a control system and a mechanical subsystem, and can synchronously consider the influences on the locking performance of various critical parameters belonging to the control and controlled systems. The computational efficiency and accuracy of the hybrid model are both verified by comparing with physical and simulation models. A 1D–3D coupling reliability methodology, which is based on the hybrid model, is proposed to analyze the motion reliability for locking mechanism of folding wing. With dividing samples and constructing parallel computation on multi-machine with multiple windows, the motion reliability and sensitivity for locking mechanism of folding wing can be obtained quickly and accurately by the novelty motion reliability analysis method.