Search

Abstract Bayesian filtering provides an effective approach for the orbit determination of a non‐cooperative target using angle measurements from multiple CubeSats. However, existing methods face challenges such as low reliability and limited estimation accuracy. Two distributed filtering algorithms based on factor graphs employed in the sub‐parent and distributed cluster spacecraft architectures are proposed. Two appropriate factor graphs representing different cluster spacecraft structures are designed and implement distributed Bayesian filtering within these models. The Gaussian messages transmitted between nodes and the probability distributions of variable nodes are calculated using the derived non‐linear Gaussian belief propagation algorithm. Gaussian messages propagate from the deputy spacecraft to the chief spacecraft in the sub‐parent spacecraft architecture, demonstrating that the estimation accuracy converges to the centralised extended Kalman filter (EKF). Simulation results indicate that the algorithm enhances system robustness in observation node failures without compromising accuracy. In the distributed spacecraft architecture, neighbouring spacecraft iteratively exchanges Gaussian messages. The accuracy of the algorithm can rapidly approach the centralised EKF, benefiting from the efficient and unbiased transmission of observational information. Compared to existing distributed consensus filtering algorithms, the proposed algorithm improves estimation accuracy and reduces the number of iterations needed to achieve consensus.

According to the infrared patch image (IPI) model theory, the infrared image background has a low rank and the target is sparse. The low-rank model can be used to separate the background and identify the target. However, in a noisy environment, the recognition effect will be affected. The higher the noise, the harder it would be to detect a small target. The residual strong fault and background edges could reduce the detection rate and increase false alarms. The traditional IPI model is adaptable to the background with the lower noise. This paper combines weighted nuclear norm minimization (WNNM) optimization with sparse representation based on the local IPI model. The background details are described more prominently by improving the nuclear norm weighting factor. The target is much easier to detect under the specific bright clouds and ground buildings background with high noise. At the same time, post-processing with image local contrast analysis is performed to compare traditional spatial filtering and local infrared patch image model algorithms. Our method has a good suppression effect on complex noise backgrounds and achieves a higher signal to clutter ratio gain (SCRG). It could also improve the target detection rate and reduce false alarms.

With the intensification and large scale of coal mining, scraper conveyors are developing towards high power, large volume, long haul distance, and high reliability. It is more and more important to obtain the dynamic tension of the scraper chain with time-varying loads. The purpose of this paper is to obtain the dynamic tension of scraper chains of the scraper conveyor with time-varying loads. First, according to the structural characteristics and working environment of the scraper conveyor and the polygon effect of the sprocket, the scraper conveyor dynamic model under the action of the time-varying loads is established by the finite unit method. Second, the load variation rule of the bearing section is explored when the scraper conveyor and shearer work together. Furthermore, the element resistance of each discrete element, the stiffness coefficient of the scraper chain, and the influence of polygonal effect of sprocket wheel are analyzed. And then, the dynamic tension of the scraper chain is obtained by the Hilber–Hughes–Taylor (HHT) method. Finally, the validity of the scraper conveyor dynamic model is verified by the example analysis and field measurement experiment. Due to the influence of heavy starting, the maximum tension of the scraper chain during the starting process is 751.6 kN, which is 12.25% higher than the tension with smooth running (669.6 kN). During the startup stage, significant impacts occur on both motors and scraper chains which may lead to damage. To protect these components effectively, control strategies such as “soft start” or no-load start hold crucial engineering significance in achieving reliable operation of the scraper conveyor. The obtained results can provide a theoretical basis for the reliability evaluation and parameter optimization of the scraper conveyor.

The working environment of mining electric locomotives is wet and muddy coal mine roadway. Due to low friction between the wheel and rail and insufficient utilization of creep rate, there may be idling or slipping between the wheels and rails of mining electric locomotives. Therefore, it is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail under wet and slippery road conditions, it is concluded that the value of creep rate is an important factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous control method for mining electric locomotives based on creep control is proposed in this paper. Finally, the effectiveness of the proposed method is verified through simulation. The problem of wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining environments is effectively suppressed. Autonomous operation of vehicles with optimal driving efficiency can be achieved through quantitative control and utilization of the creep rate between wheels and rails.

Torsion deformable spatial beam and Costello theory are used to establish longitudinal-torsional-lateral coupled model of round balance rope. Based on Coulomb’s friction law and longitudinal-torsional-lateral coupled model, the nonlinear coupled dynamic model with friction constraint of round balance rope is established. Meanwhile, time-varying multi-segments non–equal-length element transformation method (TMN-ETM) is proposed to save computation time. Then, natural frequencies, lateral responses are calculated when the coupled stiffness coefficient is zero. And the calculations are compared with traditional solution method. The results show that only about few areas in the round balance rope loop have large stress norm value, while the rest parts have small stress values. Besides, dynamic responses of the balance rope with balance rope suspension rotor releasing are conducted. When the static friction is converted to dynamic friction, the friction torque and angle acceleration will be mutated at the switching instants, and slip-stick transition in angle acceleration occurs.

The algorithm for star identification is a crucial technology for determining the orientation of spacecraft using star sensors. Traditional star identification algorithms achieve matching by seeking a unique or a few optimal solutions. However, in high-noise environments, some solutions may be lost, which could result in matching failure. A new lost-in-space architecture algorithm aimed at rapid identification under high star position noise conditions by directly using star positions for final matching is proposed in this paper. The main idea of this algorithm is to construct sufficiently redundant navigation triangles, fully utilizing the physical relationships of the features and forming a screening method from high to low dimensions and from loose to strict. During identification, a multi-layer joint screening matching method is adopted to screen triangles as a whole, narrowing the range of matches quickly while retaining error tolerance. In a series of simulation experiments, this algorithm achieved identification rates of 99.51%, 99.06%, and 98.42% for 2.0 pixel star position noise, 1.0 Mv star magnitude noise, and 5 false stars, respectively. In terms of practical application, all 1000 star images taken by the star sensor in orbit have been successfully identified, and it only takes 28ms to identify each image. In addition, star images taken by consumer-grade cameras from the ground also show that the algorithm has strong robustness to star position noise, magnitude error and false star interference in more severe environments. This method provides partial algorithmic reference for non-specialized design of star sensors for low-cost, large-scale satellites in the future.

Abstract The fretting wear among the steel wires aggravates the wire rope’s fatigue damage, affects the service performance of the wire ropes, and threatens mine hoisting safety. In this paper, the practical friction behavior and wear mechanism among the wires in the wire rope are investigated. A series of tests were carried out on multiple steel wires in helical contact and tension-torsion coupling under different fretting parameters, twisting parameters, and lubrication conditions by self-made friction and wear testing machine. The results show that the coefficient of friction (COF) among the steel wires decreases slightly with increasing lateral loads and tension, and the effect of twist angle on the COF has opposite results under different lubrication conditions. Lateral loads, tension of the steel wires, twist angle, and lubrication condition all affect the fretting morphology among the steel wires. Fretting wear with larger twist angle structure leads to more energy loss. The energy loss of fretting is directly related to the fretting morphology among the contact surfaces, and the dissipated energy is lower in the two forms of complete slip and sticking. The wear depth and width increase with the increase of lateral loads, steel wire tension, and twist angle. And the wear width and depth under dry friction conditions are higher than those under oil lubrication conditions. In addition, the wear mechanism under dry friction conditions is mainly abrasive wear, adhesive wear, and fatigue wear. And the wear mechanism under oil lubrication conditions is mainly abrasive wear and fatigue wear.

The classical interactive multimodel (IMM) algorithm has some disadvantages in tracking a noncooperative continuous thrust maneuvering spacecraft, such as poor steady-state accuracy, difficult selection of subfilter parameters, and mismatched model jump. To address the abovementioned problems, a variable-dimensional adaptive IMM strong tracking filtering algorithm (VAIMM-STEKF) is proposed to estimate the spacecraft’s position, velocity, and maneuvering acceleration state. VAIMM-STEKF contains 2 models, model 1 and model 2, which correspond to the tracking of the spacecraft in maneuvering and nonmaneuvering situations. Model 1 estimates the position and velocity of the spacecraft to ensure tracking accuracy when no maneuver occurs. Model 2 is a strong tracking filter with an augmented state. The adaptive IMM algorithm adjusts the fixed Markov transfer matrix in real time according to the model output probability. According to the different states of the spacecraft, the corresponding model interactive fusion method, together with the strong tracking filter, is adopted to ensure fast tracking when the spacecraft state changes. This method can also adapt to continuous thrust maneuvering spacecraft with different orders of magnitude. Simulation results show that the position accuracy of VAIMM-STEKF can be improved by approximately 27% and the speed accuracy can be enhanced by approximately 17% under different levels of maneuvering acceleration compared with those of the IMM algorithm. The convergence speed of VAIMM-STEKF is also better than the IMM algorithm.

Space vehicles such as missiles and aircraft have relatively long tracking distances. Infrared (IR) detectors are used for small target detection. The target presents point target characteristics, which lack contour, shape, and texture information. The high-brightness cloud edge and high noise have an impact on the detection of small targets because of the complex background of the sky and ground environment. Traditional template-based filtering and local contrast-based methods do not distinguish between different complex background environments, and their strategy is to unify small-target template detection or to use absolute contrast differences; so, it is easy to have a high false alarm rate. It is necessary to study the detection and tracking methods in complex backgrounds and low signal-to-clutter ratios (SCRs). We use the complexity difference as a prior condition for detection in the background of thick clouds and ground highlight buildings. Then, we use the spatial domain filtering and improved local contrast joint algorithm to obtain a significant area. We also provide a new definition of gradient uniformity through the improvement of the local gradient method, which could further enhance the target contrast. It is important to distinguish between small targets, highlighted background edges, and noise. Furthermore, the method can be used for parallel computing. Compared with the traditional space filtering algorithm or local contrast algorithm, the flexible fusion strategy can achieve the rapid detection of small targets with a higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

The infrared detector is adopted to track and recognize targets, including missiles and airplanes. These targets are usually far from the detector. The pixel size is less than 9×9 when the image has 256×256 pixels. They are regarded as the IR small targets. Because of the complex space environment, the high-brightness clouds and noise have a bad effect on the real small target detection. We need to focus on the tracking and detection of a small target in IR images in the intricate background. This article utilizes the Laplacian of Gaussian (LOG) filter and an improved local contrast method to reduce the impact of the high-light corner and strip. Compared with traditional space filtering, frequency domain technology, or the single local contrast method, we can greatly improve the signal clutter ratio gain (SCRG) and background suppression factor (BSF) in the real complex background in IR image processing. Under the same false alarm rate, our method achieves a better detection rate.

Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.

In this paper, a modified double-rope winding hoisting system (MDWHS) is proposed for ultra-deep mines. Nevertheless, the MDWHS has wire rope tension imbalance issues due to lift inconsistency of two wire ropes. A combined control algorithm is designed for wire rope tension active control considering the nonlinear uncertainties subjected to the MDWHS, where a low gain state observer (LGSO) and a robust nonlinear adaptive controller are employed via backstepping theory. Firstly, to investigate the wire rope tension active control method, the dynamics model of the MDWHS is established. Then, the LGSO is employed to estimate unmeasured system state variables and to optimize perturbed system state variables. Finally, a robust nonlinear control method is employed to acquire prospective control input. The combined control algorithm can ensure the desired transient and final tracking accuracy, which is essential for tracking control of wire rope tension active adjustment. The stability of the overall closed-loop system with the presented control algorithm can be verified referring to the Lyapunov theory. Experimental results conducted on the established double-rope winding hoisting simulation experimental system demonstrate that the proposed active control method exhibit more advantageous performance on wire rope tension balance control compared with the conventional Proportion Integration (PI) and adaptive backstepping controller (ABC).

Fe-Ni matrix composites with varied amounts of graphite (Gr) were fabricated by spark plasma sintering system (SPS), and the microstructure and properties of composites were investigated in details. The results implied that Fe-Ni matrix composites were mainly composed of Fe, NiWCr, FeNi, FeCr, Fe3C, Fe3W3C, (Cr, Fe)7C3, cored-eutectic structure (pearlite/Fe3C) and Gr. The variation of the average microhardness of Fe-Ni matrix composites depended on the content of Gr. It initially increased, followed by a decreasing trend with the increasing Gr content. When the Gr addition was 3 wt.%, the average microhardness reached a maximum value of about 355.3HV0.5, which was about twice higher than that of 16Mn steel. Besides, the lowest wear rate of Fe-Ni matrix composites was also achieved with increasing Gr content to 3 wt.% under identical conditions due to the high hardness and the formation of a continuously low shear strength composite layer for the worn surface. Keywords: The middle chute, The scraper conveyor, Fe-Ni matrix composites

As an important equipment for deep well hoisting, the safe and stable operation of the Blair mine hoist is vital for the development and utilization of deep mineral resources. However, it is always a challenging task to keep consistent wire rope tension in the event of an actuator fault. In this study, an adaptive dynamic surface technology-based actuator fault-tolerant scheme is proposed. A fault observer with a neural network adaptation term is designed to estimate the loss of actuator efficiency caused by faults. Considering the redundant characteristic of the two actuators, a novel dynamic surface technology-based controller with a fuzzy assignment and state constraints is developed to eliminate the impact of fault. The stability of the closed-loop system under the proposed strategy is theoretically proved by rigorous Lyapunov analysis. Comparative experiments under various conditions are carried out on a xPC based mine hoist platform, and the results show the applicability together with the superiority of the proposed scheme.

In practical applications, the signal measured from a complex mechanical system is usually disturbed by various noises due to the compounded effect of interferences of other machine elements and background noises, especially under varying speed conditions. Resonance-based approaches have been proven to be effective methods to address this problem. However, even if the optimal resonance band is accurately determined, the in-band noise with frequency content in the range covered by the band-pass filter is not eliminated. To avoid missed diagnosis and misdiagnosis of faults in bearings, an iterated SVD (ISVD)-based in-band noise reduction method combined with envelope order spectrum analysis is proposed in this paper. First, the optimal frequency band of a vibrational signal is determined with the help of an enhanced wavelet packet transform kurtogram, in which the kurtosis of each node is calculated based on the envelope spectrum of a signal be reconstructed using the wavelet packet coefficients. The node with the maximum kurtosis value is used to reconstruct the signal. Second, the envelope of a reconstructed signal is calculated by Hilbert transform and the ISVD method is applied to it to reduce the in-band noise. To avoid the destruction of useful information caused by excessive iteration, a threshold is set to determine the number of iterations. After iterative processing, a de-noised signal is reconstructed based on the relationship between the singular value and a frequency component. Finally, the reconstructed signal is resampled and transformed into the fault characteristic order domain where the bearing fault type can be identified from the envelope order spectra. The simulations and experiments were used to validate the efficacy of the proposed method. Compared with the spectral subtraction method, the ISVD method can suppress in-band noise efficiently and beneficial to extract the fault characteristic order under variable speed conditions.

Overwinding protection devices are used to brake hoisting containers before these containers reach a limited height, thereby preventing the hoisting containers from impacting the hoisting system. However, in ultra-deep shafts (depth > 1000 m), traditional overwinding protection methods fail to protect the hoisting system, because this type of hoisting system has a greater mass, kinetic energy, and inertia than the traditional hoisting system, and also the environment of ultra-deep shafts is more complex. This paper presents a novel overwinding protection method that applies a linear permanent magnet eddy current brake (LPMECB) to the hoisting system in ultra-deep shafts. This paper also finds the optimum setting parameter of permanent magnets (PMs). First, an analytical model of the LPMECB is built, and the time-domain signals of the braking force are processed via fast Fourier transform, confirming the mechanism of the optimum setting parameter. Subsequently, the simulations are conducted by establishing a finite-element model of the LPMECB; the simulations prove the existence of the optimum setting parameter of PMs and demonstrate the influence of the air gap, velocity, and conductivity on this parameter. Finally, the experimental studies are carried out on a test bench of the LPMECB to validate the analytical model and the simulation results. The results show the existence of the optimum setting parameter of PMs and prove that the air gap has an effect on this parameter.

Quantum key distribution constellation is the key to achieve global quantum networking. However, the networking feasibility of quantum constellation that combines satellite-to-ground accesses selection and inter-satellite routing is faced with a lack of research. In this paper, satellite-to-ground accesses selection is modeled as problems to find the longest paths in directed acyclic graphs. The inter-satellite routing is interpreted as problems to find a maximum flow in graph theory. As far as we know, the above problems are initially understood from the perspective of graph theory. Corresponding algorithms to solve the problems are provided. Although the classical discrete variable quantum key distribution protocol, i.e., BB84 protocol, is applied in simulation, the methods proposed in our paper can also be used to solve other secure key distributions. The simulation results of a low-Earth-orbit constellation scenario show that the Sun is the leading factor in restricting the networking. Due to the solar influence, inter-planar links block the network periodically and, thus, the inter-continental delivery of keys is restricted significantly.

Rolling bearing is a key component of rotating machinery, and its remaining useful life (RUL) estimation is also a critical technique in prognostics health management activities. In this paper, we propose a two-stage strategy for bearing health monitoring, where the bearing health process is divided into two stages: normal stage and degeneration stage, and a new method is proposed to estimate the bearing RUL by combining a new health indicator (HI) and particle filtering (PF). First, the structural information of the spectrum (SIOS) algorithm is employed to construct the HI called SIOS-based indicator (SIOSI) for bearing degeneration monitoring. Second, the initial degenerate point (IDP) is evaluated by a novel index calculated with self-zero space observer in order to conduct the two-stage division of normal and degeneration stages. Third, after detecting the IDP, the bearing RUL is estimated using the SIOSI and the PF-based algorithm with the help of a degeneration model. The effectiveness of the proposed methodology is validated using vibration data collected from bearing run-to-failure tests. Experimental results have shown that the bearing RUL could be estimated acceptably with the proposed method, and its performance is greatly superior to that presented by L10 life formula.

Piezoelectric power generation is commonly used to power wireless sensor nodes in vibration environments. A common piezoelectric power generation device is the piezoelectric ceramic plate (PCP), which is often fixed in the form of a cantilever beam. However, PCP in the cantilever beam fixation mode (PCPCBFM) has the disadvantages of low maximum allowable vibration acceleration, low maximum open circuit voltage, low maximum average power, and a small electromechanical coupling coefficient. This paper proposes a new type of fixation mode, the symmetric fixation mode, in which the fixation mode is changed without altering the structure of the PCP. Simulations and experiments are conducted to study the properties of the PCP in the symmetric fixation mode (PCPSFM). Compared with the traditional PCPCBFM, the maximum allowable vibration acceleration of the PCPSFM is increased by more than a factor of five, the maximum open circuit voltage of the PCPSFM is increased by more than 4.5 V, the maximum average power of the PCPSFM is increased by more than a factor of four, and the electromechanical coupling coefficient is increased by more than 15%.

Transient impulse analysis is an effective way to detect the bearing fault at its early stage. However, it is hard to precisely extract these so-called transient impulses because these collected vibration signals usually are non-stationary, nonlinear, and drowned by heavy background noise. Variational mode decomposition (VMD) can play the role as an adaptive signal processing tool to reveal the weak transient impulses from complex vibration signals. However, its reasonable mode number is difficult to pre-set and this would make the loss of useful transient impulses. To solve this issue, an improved VMD strategy is presented in this paper . For this method, it can not only utilize the advantages of traditional VMD and empirical mode decomposition (EMD) but also adaptively select sensitive intrinsic mode function (IMF) components for fault component analysis by proposed indexed values. EMD is first used to process the collected vibration signal into a series of IMFs, and the so-called useful IMFs are then evaluated by a sensitive IMF evaluation index which is based on the conjoint analysis of relatedness and kurtosis. Afterward, VMD is further improved to effectively decompose the denoised signal reconstructed from these selected useful IMFs of traditional EMD. Finally, the improved VMD is used for incipient fault diagnosis by a defined transient impulse monitoring index and Hilbert envelope analysis. Experiments are performed to demonstrate the effectiveness of the proposed method. The experimental results confirm that the proposed method can accurately extract the features of an incipient fault of a bearing.

Electro-hydraulic shake table (EHST) is vital seismic testing equipment in earthquake engineering for the assessment of structures subject to dynamic vibration excitations. The EHST system can be generally simplified as an electro-hydraulic servo system with prominent acceleration replication requirement. In order to improve the acceleration tracking performance of a typical EHST system, a novel realtime acceleration tracking strategy combining inverse compensation technique and neural-based adaptive controller is presented in this paper. The traditional three variable controller (TVC) is applied to the EHST system for obtaining a preliminary acceleration tracking accuracy in advance, and then the multi-innovation stochastic gradient algorithm is utilized to estimate the discrete parametric transfer function of the TVC controlled EHST system. Next, the zero magnitude error tracking technique, which is capable of handling non-minimum phase zeros, is exploited to design a stable and casual inverse model, and subsequently the parametric inverse compensation technique for the EHST system is constructed. Finally, a neural-based online adaptive controller is incorporated to the offline designed parametric inverse compensator as an outer loop, and the side effects of the system's inherent nonlinearities, varying dynamics, and external uncertainties are further addressed. The proposed controller is successfully implemented in the control system of a unidirectional EHST test rig using xPC target rapid prototyping technique, and experimental results reveal that a superior acceleration replication performance is achieved in contrast to the other comparative controllers.

Crack initiation characteristics of ring chain of heavy-duty scraper conveyor under time-varying loads were investigated in this study. The dynamic tension of ring chain of the heavy-duty scraper conveyor was obtained using the time-varying dynamic analysis. Finite element analyses of three-dimensional contacts between adjacent chain rings at straight and bending segments were carried out to explore three-dimensional stress distributions of chain rings. The crack initiation life of chain ring was predicted employing the multiaxial fatigue theory. The results show that the ring chain is subjected to time-varying dynamic tensions during operation. During tension-tension contact fatigue, as compared to the straight segment of ring chain, the bending segment engaging in the sprocket presents an overall lower (or higher) equivalent stress distribution in the case of flat (or vertical) chain ring, respectively. Maximum equivalent stresses at the contact regions of adjacent chain rings both present time-varying dynamic characteristics similar to evolutions of dynamic tension. During tension-torsion contact fatigue, an increase in torsion angle level causes unobvious difference between equivalent stress distributions at contact regions of adjacent rings. Predicted crack initiation lives of chain rings during tension-tension contact fatigue indicates more severe fatigue damages of the vertical chain ring at the straight segment and of the flat chain ring at the right bending segment.

A scraper conveyor is a key component of large-scale mechanized coal mining equipment, and its failure patterns are mainly caused by chain jam and chain fracture. Due to the difficulties with direct measurement for multiple performance parameters of the scraper chain, this paper deals with a novel strategy for fault detection of the scraper chain based on vibration analysis of the chute. First, a chute vibration model (CVM) is applied for modal analysis, and the hammer impact test (HIT) is conducted to validate the accuracy of the CVM; second, the measuring points for vibration analysis of the chute are determined based on the modal assurance criterion (MAC); and third, to simulate the actual vibration properties of the chute, a dynamic transmission system model (DTSM) is constructed based on finite element modeling. The fixed-point experimental testing (FPET) is then conducted to indicate the correctness of simulation results. Subsequently, the DTSM-based vibration responses of the chute under different operating conditions are obtained. In this paper, the proposed strategy is employed to determine the occurrence of chain faults by amplitude comparisons, while failure patterns are distinguished by the adaptive optimal kernel time-frequency representation (AOKR).

Aiming at the task planning and scheduling problem of space object detection LEO constellation (SODLC) for detecting space objects in deep space background, a method of SODLC task satellite selection based on observation window projection analysis is proposed. This method projects the spatial relative relationships of the SODLC observation blind zone, observation range, and the initial spatial position of the objects onto the surface of the earth for detectable analysis of satellites and targets and binds the dynamic observation conditions to the satellite trajectory after projection calculation of the visible relationship between target changes. On this basis, combined with the features of SODLC with high orbital symmetry, the task satellite selection is divided into two steps: orbit plane selection and task satellite selection. The orbit planes are selected based on the longitude range of the ascending node with the geographic location of the targets, and the task satellites are selected according to the relative motion relationship between the satellites and the targets together with the constraints of observable conditions. The selection method simplifies the calculation process of scheduling and selecting task satellites. Simulation analysis prove the method has better task satellite selection efficiency. The method has high practical value for task planning and scheduling for event-driven SODLC.

When the double-rope winding hoisting system (DWHS) is in operation, tensions of two wire ropes of the DWHS will not be symmetrical because of some factors such as different manufacturing deviation between the twin winding drum and two wire ropes, different winding groove depths, the winding asynchronism of two wire ropes, and elastic modulus difference of two wire ropes and so on. Therefore, an electro-hydraulic servo system (EHSS) is employed to actively control two wire ropes tensions to guarantee operation security of the DWHS. Dynamic models of the hoisting system and the EHSS are introduced, of which dynamic model of the DWHS is expressed with state representation. The flatness-based controller (FBC) is designed for the hoisting system. A disturbance observer is utilized to deal with the external disturbance and unmodeled characteristics of the EHSS. Hence, a disturbance observer based integral backstepping controller (DO-BIBC) is designed for the EHSS. The stability of the overall control system is proved by de-fining an overall Lyapunov function. To investigate the property of the proposed controller, an experimental setup of the DWHS is established. As well, comparative experimental results indicate that the proposed controller exhibits a better performance on leveling control of the conveyance and tension coordination control on the two wire ropes than a conventional PI controller.

Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method based on image sparse representation is proposed. Original spectrum images are acquired through fast Fourier transformation. Sparse coefficient that reveals the underlying structure of spectrum image based on raw signals is extracted as the feature by implementing the orthogonal matching pursuit and K-singular value decomposition algorithm strategically, and then two-dimensional principal component analysis is applied for further processing of these features. Finally, fault types are identified based on a minimum distance strategy. The experimental results are given to demonstrate the effectiveness of the proposed method.

Vibration analysis is an effective way to accurately diagnose bearing faults, because it carries abundant information regarding mechanical health conditions. However, noise interference makes the features, extracted from vibration signals at different time periods, show randomness fluctuation that will reduce the bearing diagnostic accuracy. To solve this problem, this article proposes a noise reduction method in feature level and tries to use it in bearing fault diagnosis with principal component analysis and radial basis function neural network. First, original feature space, including time, frequency, and energy features, is constructed from these obtained vibration signals. Second, compendious feature sets of the considered bearing faults are created by principal component analysis and random statistical average algorithm. In this step, random statistical average is designed to weaken the influence of noise to features and principal component analysis is used to reduce the dimension of features for compendious feature sets. Then, radial basis function neural network, an artificial intelligence tool, is introduced to diagnose bearing faults by compendious feature sets. Finally, experiments on test bench are carried out to verify the reliability and validity of the proposed method. The experimental results show that the proposed method can accurately identify bearing faults.

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

In order to diagnose bearing faults under different operating state and limited sample condition, a fault diagnosis method based on adjusted spectrum image of vibration signal is proposed in this paper. Firstly, the Davies–Bouldin index (DBI) is employed to select a proper capture focus (CF) and image size, and the spectrum of vibration signal is computed via fast Fourier transformation (FFT) and adjusted according to the average rotating speed. Then, the spectrum is plotted and captured as a two-dimensional (2D) image with the optimized CF and image size. Two-dimensional principal component analysis (2DPCA) is used to reduce the dimension of images, and finally a nearest neighbour method is applied to classify the faults of bearings. Two experiments are carried out to validate the effectiveness of the proposed method. Besides, a further investigation on the effect of spectrum frequency resolution is conducted and a recommended selection method of frequency resolution is given based on the experimental performances. In our method, the training samples could be from only one operating condition, while the testing samples are from all possible operation conditions. All experiment results have demonstrated that the proposed method could achieve high classification accuracy even with very limited training samples.

Because of the closed geographic conditions and the effect of uncertain factors during a catastrophe in an underground coal mine, a high failure rate of the sensor nodes after a disaster is a common phenomenon. To ensure normal communications after a disaster, an impact cushioning device is designed to ensure survival of the sensor nodes during the disasters like the tunnel collapse, gas explosion, and so on. First, a model for the spring absorbing system is built; an analysis based on the model shows that the impact cushioning device can survive the free impact due to the extremely short impact time and the small displacement despite the large force from the instantaneous impact. Second, a collision model between the impact cushioning device and the rock is built; an analysis based on the model shows that both the circuit boards and the shell of the device can survive a tunnel collapse because the maximum stress values of the shell material and the circuit boards during the collision process are much less than their yield strength values. Finally, we use a triangular wave to simulate the shock wave used to test the collision model. An analysis of the test shows that the shell of the device and circuit boards are still safe and reliable under the effects of a gas explosion for the same reason that the maximum stress values of the shell material and the circuit boards in the gas explosion process are much lower than their yield strength values.

This article demonstrates the tension distribution estimation method for the ring chain transmission system of scraper conveyer, which is based on multi-body dynamics theory and linear state observer. As designed, a multi-body dynamic model of ring chain transmission system is constructed using ADAMS dynamic design software, and a mathematical model solved by MATLAB functions is established and adopted to verify system performance of the dynamic model by comparing the simulation results. In practical terms, tension monitoring of ring chains is virtually unreasonable due to difficulties of direct measurement and restrictions of high-cost tension sensors during the process of mechanized coal mining. Besides, the change law of tension distribution of the ring chain transmission system has mainly been obtained via heuristic experience, coupled with simple calculations. The proposed estimation method makes it feasible to obtain all the contact forces of ring chains based on the measurable state variables. For research purpose, the state-space modeling allows the reconstruction of the tension observer and flexible application for numerical solutions based on Runge–Kutta equations. However, the high-dimensional tension observer has inherent drawbacks of heavy computational loads. In such cases, a novel dimension-minimized algorithm is proposed for high-order matrices solution, which makes the observer very attractive for applications in dimensionality reduction. Furthermore, the practicability of the proposed estimation method is verified with simulation evaluation and deviation analysis.

Most fault detection methods based on the assumption of working in stationary or approximate stationary conditions are limited under varying operation conditions, for that the frequency aliasing phenomenon is inevitable in the spectrum. Therefore, in order to handle the problem of fault diagnosis under non-stationary conditions, researchers have proposed numerous methods and some achievements have been obtained. In this article, a new feature extraction method is proposed for fault diagnosis of rolling bearings under varying speed conditions. Based on the assumption that the energy will increase when balls cross over fault position, frequency values are divided by instantaneous speed and arranged in the descending order of corresponding amplitude to form a new fault feature array, that is, the ratio of frequency to instantaneous speed reconfiguration arrays. Thereafter, the Euclidean distance classifier is utilized for recognition. The efficacy of the proposed method is demonstrated by simulated and experimental data. Categorized results show that the new approach is capable of handling the bearing fault classification under varying speed conditions.

Aiming at addressing the load behavior of iron ore particles in a ball mill, a design of experimental method was used to define a series of discrete element method simulation conditions with two factors being the mill speed and lifter height. The key feature locations of impact toe, bulk toe, shoulder, and head positions were identified visually to determine the load behavior of the charge. To allow the irregular particle shape to be accurately determined, a quick and accurate sphere-clump method was employed in modeling the geometrical model of irregular shape. The results show that the dependence of the impact toe and head on mill speed is higher than its dependence on the lifter height with a correlation of −0.923 and 0.97, respectively. The shoulder changes approximately invariant with the mill speed and varies little with the lifter height. The bulk toe of particles appears to be invariant to the mill speed as well as the lifter height, resulting in the approximately same inclination of the chord joining the shoulder and bulk toe.

The incompletely restrained cable-suspension swing system driven by two cables is introduced in this article. Based on wrench of forces theory and Lagrange’s equation of first kind, the static and dynamics models of incompletely restrained cable-suspension swing system driven by two cables are established, respectively. In order to obtain an intuitive understanding of the trajectory analysis, a dynamics model consisting of governing equation and geometric constraint conditions which is a set of the mixed differential-algebraic equation in mathematics is established. A typical feedback controller and an inverse model were set up to estimate the driving function. The effective workspace, which is used to guarantee an efficient swing process, mostly depends on the geometrical shape rather than the volume itself which was calculated by trajectory analysis. In order to estimate system features and ensure a limited range of tension in underconstrained spatial cable system, the probable location of unbalanced loading was evaluated by pointwise evaluation techniques during normal work.

To investigate the impact of lifting velocity and container shape on angle of repose, the fixed-base cylinder method was performed using three types of container shape. The container shape was lifted a series of lifting velocities. Six size fractions of iron ore particles ranging from coarser to fine particles were used as the test materials. And the sand-pile calibration method was then used to calibrate the contact parameters of iron ore particles. Results show angle of repose decreased exponentially with the lifting velocity, while it appeared approximately to be invariant to particle shape, for all size fractions. The sand pile highly depends on the container shape at a low lifting velocity but appears to be invariant to particle size for a high lifting velocity. And then a predictive equation is established and a very close agreement between the predicted and measured angle of repose is attained. Finally, a series of DEM simulations considering the irregular particle shape are conducted by means of sphere clump method to calibrate the contact parameters and are in good visual agreement with the experimental results, indicating the “tuned” contact parameters as well as the applicability of the predicted equation.

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

Envelope analysis is a widely used method in fault diagnoses of rolling bearings. An optimal narrowband chosen for the envelope demodulation is critical to obtain high detection accuracy. To select the narrowband, the fast kurtogram (FK), which computes the kurtosis of a set of filtered signals, is introduced to detect cyclic transients in a signal, and the zone with the maximum kurtosis is the optimal frequency band. However, the kurtosis value is affected by rotating frequencies and is sensitive to large random impulses which normally occur in industrial applications. These factors weaken the performance of the FK for extracting weak fault features. To overcome these limitations, a novel feature named Order Spectrum Correlated Kurtosis (OSCK) is proposed, replacing the kurtosis index in the FK, to construct an improved kurtogram called Fast Order Spectrum Correlated Kurtogram (FOSCK). A band-pass filter is used to extract the optimal frequency band signal corresponding to the maximum OSCK. The envelope of the filtered signal is calculated using the Hilbert transform, and a low-pass filter is employed to eliminate the trend terms of the envelope. Then, the non-stationary filtered envelope is converted in the time domain into the stationary envelope in the angular domain via Computed Order Tracking (COT) to remove the effects of the speed fluctuation. The order structure of the angular domain envelope signal can then be used to determine the type of fault by identifying its characteristic order. This method offers several merits, such as fine order spectrum resolution and robustness to both random shock and heavy noise. Additionally, it can accurately locate the bearing fault resonance band within a relatively large speed fluctuation. The effectiveness of the proposed method is verified by a number of simulations and experimental bearing fault signals. The results are compared with several existing methods; the proposed method outperforms others in accurate bearing fault feature extraction under varying speed conditions.

Using the largest semi-autogenous grinding mill in China as a model, collision energy was analyzed on the basis of the multi-layer kinematics of the steel balls. First, the kinematic equation of the steel balls was obtained by considering the multi-layer characteristics of the steel balls. Second, the collision energy of the inner-layer steel balls was addressed according to its kinematic characteristics. Finally, the total collision energy per unit time was obtained. Results show that the leaving angle decreases as the mill speed ratio increases and as the radius ratio increases, but the leaving velocity increases linearly. Moreover, a discontinuity point of the tangential collision velocity occurs at an angle factor β = 0, and the angle factor β is divided into two intervals: [−3, 0] and [0, 1.5]. The leaving angles corresponding to a tangential velocity equal to zero are calculated to be 1.185 and 0.7854 rad in the two intervals. In addition, the sum collision velocity increases when β is less than zero, but it decreases sharply above zero. The maximum total collision energy per unit time occurs at 84.2% of the mill speed corresponding to the optimal mill speed ratio.

An electrohydraulic shaking table is an essential experimental facility in many industrial applications to real-time simulate actual vibration situations including structural vibration and earthquake. However, there is still a challenging area for its acceleration waveform replication because acceleration output responses of the electrohydraulic shaking table would not be as intended in magnitude and phase of an acceleration closed-loop system due to inherent hydraulic nonlinear dynamics of electrohydraulic servo systems. Thus, how to accurately and coordinately control parallel hydraulic actuators of the electrohydraulic shaking table is a critical issue; so, many control techniques have been developed to address the issue. Some currently used key techniques in this field are reviewed in the article, which are the objectives of academic investigations and industrial applications. The article reviews some new control algorithms for the electrohydraulic shaking table to obtain high-fidelity acceleration waveform replication accuracy.

The flexibility of the suspension multicables and driven length difference between two cables cause the translation and rotation of the platform in the incompletely restrained cable-suspended system driven by two cables (IRCSWs2), which are theoretically investigated in this paper. The suspension cables are spatially discretized using the assumed modes method (AMM) and the equations of motion are derived from Lagrange equations of the first kind. Considering all the geometric matching conditions are approximately linear with external actuator, the differential algebraic equations (DAEs) are transformed to a system of ordinary differential equations (ODEs). Using linear boundary conditions of the suspension cable, the current method can obtain not only the accurate longitudinal displacements of cable and posture of the platform, but also the tension between the platform and cables, and the current method is verified by ADAMS simulation.

This paper investigates dynamic simulation and trajectory tracking control of hybrid‐driven planar five‐bar parallel mechanism (HPPM). To begin with, a simulation model of dynamics based on MATLAB/SimMechanics is established. Then, traditional PD control and closed loop PD‐type iterative learning control of the HPPM are designed. At the end, the simulation based on SimMechanics is carried out, which acquires angular, angular velocity, angular acceleration of two driving links and constraint reaction of kinematic pairs at any time. In addition, the performance of the closed loop PD‐type iterative learning control is compared with that of the traditional PD controller through simulations of the HPPM in the presence of the model external disturbances. The simulation results indicate that a perfect trajectory tracking of end‐effector of the HPPM is achieved by the closed loop PD‐type iterative learning controller.

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

In order to simulate the swing conditions of a suspended platform of a construction shaft, marine ships, cars, etc., an incompletely restrained cable-suspension swing system driven by two cables (IRCSWs2) was designed and parameter trajectories of displacements, angles and tensions were systematically investigated. Firstly, the motion mechanism of the IRCSWs2 is described and the corresponding kinematic model is established. For further evaluating the analytical expressions, the ADAMS simulation model and the physical prototype experimental model were developed. The basic consistency and slight difference among the three models are illustrated by a comparison of different parameters. The approximately linear relationship between the driving displacements of two cables and the swing angles of the platform was obtained. Finally, the effects of various parameters on displacements, angles and tensions were analysed, and the results indicate that the translation of a suspended platform is slight during its swing and that the novel IRCSWs2 can be used to drive heavy loads using a relatively small driving force, which is useful for simulating swing environmental conditions long-term, in addition to being cost-effective.

For emergency information delivery of chain-typed multihop wireless sensor networks in closed long tunnel (CWSN-C), the scheme of sending the same packet multiple times is a good choice. In this paper, taking the application of rigid sliding guide obliquity monitoring in deep shaft as background, the source-aware redundant packet forwarding scheme for emergency information delivery in CWSN-C (SRPFEC) is proposed firstly. Secondly, more of comprehensive factors including application QoS and energy efficiency are considered for determining the number of redundant packets. Then, a method of solving for the number of redundant packets is proposed. Finally, Monte Carlo method and network simulation are employed to study the performances of proposed SRPFEC. The results show that (1) non-ACK scheme is highly suitable for the condition of large network size and hazardous wireless communication environment; (2) the non-source-aware solution is easy to use and is suitable for the occasion where the node could be replaced on demand expediently; (3) the source-aware solution has the better performance in energy efficiency and is suitable for the occasion where the network lifetime needs to be determined before deployment, and the node could not be replaced easily.

Harvesting the energy contained in the running environment of rotating machinery would be a good way to supplement energy to the wireless sensor. In this paper, we take piezoelectric bimorph cantilever beam with parallel connection mode as energy collector and analyze the factors which can influence the generation performance. First, a modal response theory model is built. Second, the static analysis, modal analysis, and piezoelectric harmonic response analysis of the wind-induced piezoelectric bimorph cantilever beam are given in detail. Finally, an experiment is also conducted. The results show that wind-induced piezoelectric bimorph cantilever beam has low resonant frequency and stable output under the first modal mode and can achieve the maximum output voltage under the resonant condition. The output voltage increases with the increase of the length and width of wind-induced piezoelectric bimorph cantilever beam, but the latter increasing amplitude is relatively smaller. In addition, the output voltage decreases with the increase of the thickness and the ratio of metal substrate to piezoelectric patches thickness. The experiment showed that the voltage amplitude generated by the piezoelectric bimorph cantilever beam can reach the value simulated in ANSYS, which is suitable for actual working conditions.

The behavior of rope-guided conveyances is so complicated that the rope-guided hoisting system hasn't been understood thoroughly so far. In this paper, with user-defined functions loaded, ANSYS FLUENT 14.5 was employed to simulate lateral motion of rope-guided conveyances in two typical kinds of shaft layouts. With rope-guided mine elevator and mine cages taken into account, results show that the lateral aerodynamic buffeting force is much larger than the Coriolis force, and the side aerodynamic force have the same order of magnitude as the Coriolis force. The lateral aerodynamic buffeting forces should also be considered especially when the conveyance moves along the ventilation air direction. The simulation shows that the closer size of the conveyances can weaken the transverse aerodynamic buffeting effect.

The test system for emulsion pump is facing serious challenges due to its huge energy consumption and waste nowadays. To settle this energy issue, a novel energy regenerative system (ERS) for emulsion pump tests is briefly introduced at first. Modeling such an ERS of multienergy domains needs a unified and systematic approach. Bond graph modeling is well suited for this task. The bond graph model of this ERS is developed by first considering the separate components before assembling them together and so is the state-space equation. Both numerical simulation and experiments are carried out to validate the bond graph model of this ERS. Moreover the simulation and experiments results show that this ERS not only satisfies the test requirements, but also could save at least 25% of energy consumption as compared to the original test system, demonstrating that it is a promising method of energy regeneration for emulsion pump tests.

Hydraulic excavator energy saving is important to relieve source shortage and protect environment. This paper mainly discusses the energy saving for the hybrid hydraulic excavator. By analyzing the excess energy of three hydraulic cylinders in the conventional hydraulic excavator, a new boom potential energy recovery system is proposed. The mathematical models of the main components including boom cylinder, hydraulic motor, and hydraulic accumulator are built. The natural frequency of the proposed energy recovery system is calculated based on the mathematical models. Meanwhile, the simulation models of the proposed system and a conventional energy recovery system are built by AMESim software. The results show that the proposed system is more effective than the conventional energy saving system. At last, the main components of the proposed energy recovery system including accumulator and hydraulic motor are analyzed for improving the energy recovery efficiency. The measures to improve the energy recovery efficiency of the proposed system are presented.