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Underwater nodes are prone to drift under the water flow action, which makes the topological structure of underwater wireless sensor networks (UWSNs) have great uncertainty. It is bound to bring about the occurrence of node nonuniform distribution over time. Given the obstacles and boundaries constrained areas, the intention of this paper is to redeploy the drifted underwater nodes for regaining higher coverage rate. Hence, we propose a three-dimensional virtual force coverage algorithm (3D-VFCA). Inspired by the physical water flow action, the position evolution model of drifted underwater nodes is derived under the continuous gravity, buoyancy, propulsion and resistance forces, which can reveal the mechanism of UWSNs deformation and coverage holes caused by the underwater node drift. Then, the coverage problem of dense and sparse node distributions is transformed into the optimization problem of weighted distance between node positions and clustering centers in the pre-coverage process, which can reduce large moving distances caused by node blind movements and solve the problem of inaccurate clustering centers caused by outliers. Furthermore, the improved virtual force algorithm is designed in consideration of underwater nodes, obstacles and boundaries, which can drive the pre-covered underwater nodes to more optimal positions based on the adaptive moving distance per step. Finally, coverage performance evaluations of drifted underwater nodes are performed under different coverage algorithms, node numbers and obstacles. The experimental results indicate that the proposed 3D-VFCA can improve the coverage rates in UWSNs.

To provide accurate path tracking and satisfactory soil loading and unloading for a global navigation satellite system (GNSS) based tractor-scraper land levelling system, a novel model-based control method with slip compensation was developed. A sideslip estimator was developed and adapted into a tractor-scraper kinematic model. The steering angle control law was calculated based on the kinematic model, and its stability was verified using Lyapunov functions. The method was validated via field experiments in which a GNSS-based tractor-scraper land levelling system was controlled to track planned paths in a wet field to complete an autonomous land levelling operation. The experimental results showed that compared with path tracking performance of a pure pursuit control (PPC) method, the root mean square errors (RMSEs) of the total lateral deviations of the tractor and the scraper under the proposed method were reduced by 0.433 and 0.169 m respectively in headland turning. Moreover, the elevation distribution (i.e. evenness) of the field increased by 33.314% after the autonomous land levelling operation driven by the proposed method. The results showed that the proposed method could realise accurate path tracking and satisfactory land evenness. It can also be integrated into current GNSS-based tractor-scraper land levelling systems to realise autonomous land levelling with improved performance.

The low-speed high-torque permanent magnet synchronous motor (PMSM) drive system is a kind of typical nonlinear, strong-coupling and easy-parameter perturbation electromechanical coupling system. The control system is uncertainties and subject to unknown external interferences as well. In this paper, a disturbance rejection control method combining robust speed controller and load observer is proposed for low-speed high-torque PMSM. The robust speed controller combines the composite nonlinear feedback (CNF) which has advantage in improving the transient responsive performance and the integral sliding mode (ISM) advancing in improving system robustness. Subsequently, the effects of unknown external interferences are avoided by using a sliding mode observer (SMO), in which the chattering is reduced by introducing fuzzy control, and the observation is used for feed-forward compensation. The proposed robust speed controller solves the contradiction between the rapidity and overshoot of the traditional control method, and combines the load observer to compensate the influence of the load mutations and wide range of the load changes on the control system. Finally, the numerical simulation and experiments demonstrate that the proposed speed control method is able to achieve good transient performance in inhibiting system overshoot and reducing stable state error. Additionally, it successfully suppresses the influence of load disturbances and mutations, and shows the proposed method has better robustness.

Improper use of high beams can cause glare interference to oncoming drivers or pedestrian. In this article, an adaptive high-beam controlling system is designed to avoid glare interference. The adaptive high beams are illuminated by group light-emitting diode (LED) particles as the light source. The luminous intensity distribution of single LED particle is determined along with the lighting zones of group LED particles. The oncoming and same direction vehicles are detected by positioning their headlights and tail lights via the received images from the on-board camera. The detected vehicles are mapped to the corresponding LED illumination zones. The adaptive high-beam controlling system turns OFF the LED particles in corresponding with vehicle areas and keeps the remaining LED particles ON. The novelty of our work is that an adaptive high-beam controlling system is developed under the integration of controlled LED particles, an image processor, and a lamp controller. The benefit of proposed adaptive high beams is to avoid glare interference for improving night driving safety.

There is a class of heritage buildings, such as museums, palaces and castles, which are closed buildings with multiple layers and exits. There are a lot of precious heritage and cultural relics in heritage buildings, and a large number of tourists need to be received every day. When suffering from emergencies such as fires, heritage buildings will be confronted with serious consequences if individuals cannot evacuate in time. In this paper, the path intelligent optimization for dense crowd emergency evacuation is proposed in heritage buildings. Firstly, based on Artificial Fish Swarm Algorithm (AFSA), agglomeration model is used when there is no guidance of guide personnel. Secondly, single-layer evacuation model is used to classify individuals into different categories based on improved K-Nearest Neighbor (KNN) algorithm. The model can help individuals choose the nearest stair and save escape time. Thirdly, based on Dijkstra shortest path algorithm, multi-layer evacuation model is used to find the shortest distance and the optimal path for individuals to reach the exit. These models are applied to the Louvre in order to verify the rationality by calculating the approximate evacuation time. In case of emergency, the algorithm proposed in this paper can be modified according to the actual heritage buildings.

In view of the characteristics of the small amplitude of a current signal, an all-optical fibre small-current sensor for partial discharge is devised. A novel type of sensor head with electric winding light is proposed. A complete optical model of the optical fibre current sensor is developed under the effect of polarization error factors. In addition, the effect of polarization error factors on the optical fibre sensing system is simulated. The simulation results show that as the extinction ratio increases, the output relative error of the optical fibre small-current sensor decreases. The effect of the fast and slow axis misalignment errors of a polarizing beam splitter is similar to the effect of the alignment angle error of an optical fibre polarizer. It is observed that the relative error curve follows a sine function. Meanwhile, the output relative error increases first and then oscillates with an increase in the linear birefringence. We conclude that the linear birefringence of the sensing optical fibre is the main error source for the measurement accuracy of the sensing system. Finally, an experimental system for the optical fibre small-current sensor is implemented. The experimental results show that the effect of linear birefringence on the fibre current sensing system can be suppressed by introducing a large amount of circular birefringence into the sensing optical fibre. The theoretical mechanism of these errors is analysed, which can be applied to implement corresponding measures to reduce the effect of error factors on the sensing system and further improve the measurement accuracy of the optical fibre small-current sensor.

Aiming at the characteristic of small amplitude of partial discharge current signal, all-optical fiber current sensing system is designed. Two kinds of optical fiber current sensor structures are proposed, which are linear and reflective. The optical path systems of two kinds of fiber optical current sensor are modeled based on Jones matrix. In addition, the output sensitivities of two structures are compared. The results indicate that the sensitivity of the two current sensor structures can be improved, and the output sensitivity of the reflective fiber optical current sensor is twice as high as that of the linear sensor. At the same time, the complete optical path model of optical fiber sensing system is established under the influence of fiber birefringence errors. Finally, the birefringence errors of sensing fiber are simulated. It is concluded that the reflective fiber optical current sensor can suppress the influence of linear birefringence on the accuracy of the sensing system by introducing a large amount of circular birefringence in the sensing fiber.

Under the action of water erosion and self-aging, reservoir dams are prone to develop cracks, which affect safe operation. Underwater visual imaging can be used to detect dam surface cracks, but spalling, aquatic plants and suspended sediments result in low image contrast and complex backgrounds. With the use of unsupervised machine learning, this paper proposes a fine segmentation and extraction algorithm for image-based dam surface cracks. First, adaptive histogram equalization is used to change the uneven illumination areas of underwater surface images intoeven illumination areas, whose statistical characteristics are calculated under linear spatial filtering. Second, the extraction problem of crack areas of interest after dodging preprocessing is transformed into calculating the distance of the image block cluster center, which can distinguish the image blocks of crack features from the background interference features. Third, the fine extraction of crack images is carried out by considering the connected domains and morphological features, and the posterior probability of an image sample category is obtained based on the soft clustering of a Gaussian mixed model. Finally, different extraction algorithms related to surface cracks are evaluated in extensive experiments. The results validate the superior performance of the proposed extraction algorithm with 90.1% extraction accuracy, 6.5% missing alarm rate and 7.2% false alarm rate.

Microwave photonic filter (MPF) based interrogation solutions have attracted considerable research interest, through the sensing information conversion from the optical domain to the microwave domain and high-resolution electrical spectrum analyzing processing techniques. In order to overcome the trade-off between measurement accuracy and interrogation speed existing in the traditional direct-detection method, an efficient machine learning algorithm is introduced into the MPF-based interrogation system. Compared with the traditional direct-detection method, the proposed method can achieve better measurement accuracy under the sparsely sampled frequency response, whilst the interrogation speed is greatly improved. In addition, the well-trained model has strong adaptability to the amplitude variation of the microwave photonic filtering response.

To solve the problem of grain spatter during harvest by combine harvester headers, a new clamping and conveying device of the sunflower oil harvester header was designed. We investigated plant states during the conveying process. To optimize the parameters of the sunflower oil harvester header, a test bench was built to simulate sunflower plant harvest. The influence of the clamping gap, clamping speed ratio, and clamping length on conveying success rate was explored by a single-factor experiment. Based on this experiment, the secondary regression orthogonal rotation test was carried out. The optimal structural parameter combination was obtained as follows: the clamping gap was 20 mm, the clamping speed ratio was 1.3, and the clamping length was 345 mm. Under this combination parameter condition, the corresponding conveying success rate reached up to 85.16% and the minimum value of conveying grain loss rate was 1.57%. To verify the effect of parameter optimization, a verification test and a comparison test were performed. Results showed that the actual conveying success rate was 83.50% and the actual grain loss rate was 1.49%, which were close to the optimized parameter value. The comparison test showed that the conveying success rate of the flexible clamping and conveying device was 83.50% with a grain loss rate of 1.49%, and that of the rigid clamping and conveying device was 55% with a grain loss rate of 5.17%. This study provides a theoretical basis for the design of a low-loss sunflower oil combine harvester header.

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

The accurate stable measurement of position for a shearer is a key technology in realizing an automated fully mechanized coal mining face. Using as a base of SINS/UWB integrated positioning system, the signal interference and node damage of UWB often cause an invalid integrated positioning system, because of the bad working environment of the shearer in the coal mine. Consequently, the positioning accuracy of the SINS/UWB integrated system decreases quickly, due to the drift error of SINS. Aiming at two invalid styles include partial anchor node failure and all anchor nodes failure of UWB, this paper proposes a stable SINS/UWB integrated positioning system of a shearer using the multi-model intelligent switching method based on a tightly coupled integrated model and a decision tree fault-tolerant model. First of all, the tightly coupled integrated model and the decision tree fault-tolerant model are built using measured distance and the final position of UWB, respectively. Second, the multi-model intelligent switching algorithm can be established based on the shearer Markoff movement state and residual threshold judgment of a Kalman Filter. Finally, the experimental results show that the multi-model intelligent switching fault-tolerant positioning algorithm can effectively overcome increased positioning error of the tightly coupled model and the decision tree model based on these two invalid styles. The maximum position error with the positioning model proposed in this paper is 0.93 m in 327 s, and the positioning accuracy with a multi-model intelligent switching model is much greater than that with tightly coupled integration and the decision tree fault tolerant models.

Multi-AUV cooperative target search problem in unknown 3D underwater environment is not only a research hot spot but also a challenging task. To complete this task, each autonomous underwater vehicle (AUV) needs to move quickly without collision and cooperate with other AUVs to find the target. In this paper, an improved dolphin swarm algorithm- (DSA-) based approach is proposed, and the search problem is divided into three stages, namely, random cruise, dynamic alliance, and team search. In the proposed approach, the Levy flight method is used to provide a random walk for AUV to detect the target information in the random cruise stage. Then the self-organizing map (SOM) neural network is used to build dynamic alliances in real time. Finally, an improved DSA algorithm is presented to realize the team search. Furthermore, some simulations are conducted, and the results show that the proposed approach is capable of guiding multi-AUVs to achieve the target search task in unknown 3D underwater environment efficiently.

Form trimming is an important practice in horticulture. Currently, handheld trimming tools are the most commonly used in China, which presents certain disadvantages including high human labor input, low productivity and inconsistent performance. In this work, a wheeled form trimming machine was designed for shrub plants with the aim of reducing labor input, increasing efficiency and improving trimming performance. The machine was mainly composed of three parts: a supporting frame, a rotary base and a knife system. The design and construction of the key components of the machine were introduced. The knife system was a combination of multiple cutter units with reciprocating motions. The number of units and their connecting angles could be adjusted to realize different trimming shapes. The knife system was carried by the rotary base and could realize 360° rotations to cut the plants into a desired form. Experiments were performed to determine the optimal working parameters (cutting frequency of the cutter unit and rotating speed of the rotary base). The similarity between the plant profile after trimming and the profile of the knife system and the consumed time in each operation were chosen as two evaluation indexes. Results showed that when the cutting frequency was 16.7 Hz and the rotating speed of the rotary base was 13.5 r/min, the trimming operation could be completed by two circles, and the time consumption was 8.89 s. Furthermore, to test the adaptability of the machine, five different shrub plants were chosen and trimmed by the machine, and results showed that the overall similarity was above 93%. Therefore, the form trimming machine developed could meet the requirements of shrub trimming in horticulture with desirable precision and adaptability.

A powerful machine learning tool Gaussian Process Regression (GPR) is introduced into microwave photonic filtering interrogation for displacement sensing with improved demodulation speed and accuracy.

This paper focuses on the detection and tracking of mobile light spots in dim light environment, and the pixel discrimination method and morphological matching method are proposed. The image sensor installed on the vehicle is used as the acquisition unit. The mobile light spots recognition in dim light environment and the dynamic tracking detection of moving and static targets are carried out to realize the mobile light spots recognition based on intelligent image perception technology. At the same time, the open source function library of OpenCV is used to study the system of detecting and recognizing the characteristics of night lights in C++ environment. The results show that the detection and tracking of mobile light spots can effectively capture and recognize the spot.

To improve the overall mechanization level of rapeseed production in China, especially in some hilly regions where ground machinery cannot enter the fields or can only enter with very low economic benefits, a special aerial seeding system for rapeseed based on a miniature air-assisted centralized metering device was designed and tested in this study. Unlike existing commercial aerial seeding systems, the proposed seed meter was a miniaturized version derived from the traditional air-feeding seed meter on ground planters. The new version contained a redesigned seed feeding component to overcome problems of serious air backflow to the seed box and difficult seed feeding after miniaturization. Three groups of experiments were designed and conducted to optimize the parameters of the seed meter and test its performance. Results from the orthogonal experiment showed that the seed feeding component performed best when the seed layer thickness was 45 mm, the rotational speed of the gear disc was 45 r/min, and the airflow pressure was 2450 Pa. Results from the static workbench test showed that the designed seeding system had a maximum average total sowing efficiency of 537.17 g/min, with the maximum values of the stability variation coefficient of total seeding rate (seven ports) and the consistency variation coefficient between each port was 2.37% and 4.89%, respectively. Field tests further proved that the designed aerial seeding system could work stably, uniformly, and efficiently, so that the agronomic requirements of rape crop planting could be well met.

A novel two-dimensional (2D) temperature-insensitive tilt sensor is proposed, utilizing four fibers containing two FBGs to form a multifiber bundle. 2-D tilt angle can be determined by monitoring the wavelength separations of the split peak in two FBGs. © 2020 The Author(s)

An automatic guidance and control system for the stationary operating mode of an agricultural Wide-Span Implement Carrier (WSIC) was developed and tested on a robotic experimental platform. Control algorithms for the parallel point-to-point tracking problem (forward point tracking and periodic operation process) were designed. Two robots were instrumented with a dual-rover Real-Time Kinematic Global Positioning System, inertial measurement units, XBee-PRO wireless communication modules, and control processors. The experimental platform executes the control sequence in a master-slave cooperative manner. Results of the single point tracking experiments show that the designed velocity and steering angle control laws can effectively guide the robots from a start point to a target. The average and root-mean-square of the offset errors were less than 0.04 m for both the 2-m and 3-m implement width experiments. The lateral and orientation errors were less than 0.16 m and 11° respectively. The sequential point-to-point tracking experiments confirmed the overall effectiveness of the point tracking control and the master-slave cooperative control. The motions of the master and slave robots were in good synchronization, which can permit high efficiency during autonomous WSIC operations.

Localization of autonomous underwater vehicles (AUVs) is a very important and challenging task for the AUVs applications. In long baseline underwater acoustic localization networks, the accuracy of single-way range measurements is the key factor for the precision of localization of AUVs, whether it is based on the way of time of arrival (TOA), time difference of arrival (TDOA), or angle of arrival (AOA). The single-way range measurements do not depend on water quality and can be taken from long distances; however, there are some limitations which exist in these measurements, such as the disturbance of the unknown current velocity and the outliers caused by sensors and errors of algorithm. To deal with these problems, an AUV self-localization algorithm based on particle swarm optimization (PSO) of outliers elimination is proposed, which improves the performance of angle of arrival (AOA) localization algorithm by taking account of effects of the current on the positioning accuracy and eliminating possible outliers during the localization process. Some simulation experiments are carried out to illustrate the performance of the proposed method compared with another localization algorithm.

The mobile operating mode of agricultural Wide-Span Implement Carriers (WSICs) requires accurate synchronization of the forward motions of the two supporting tractors. The guidance task for WSIC operations is currently performed by human operators, which is labor-intensive and can be inaccurate. To replace the traditional human steering and control process, an automatic guidance system adapted to the mobile mode of the WSIC was designed and tested. A master-slave cooperative control method was applied, and the steering angle control law for both the master and slave vehicles and the velocity control law for the slave vehicle were derived. A scaled-down robotic platform was developed by integrating two heavy-duty mobile robots, a dual-rover real-time kinematic GPS (RTK-GPS), two inertial measurement units (IMUs), two XBee-PRO wireless communication modules, and two groups of control processors. Validation experiments were performed using the robotic platform on a flat and firm surface. Results of the single-robot path-following experiment verified the effectiveness of the steering angle control law. Results of the two synchronous tracking experiments, one with a constant reference velocity of 0.3 m s-1 and one with a varying reference velocity of 0.2 to 0.6 m s-1, showed that the two robots could perform their individual path-following tasks accurately while keeping their relative offset to less than 0.1 m. In the experiment with a constant reference velocity, the average, root mean square (RMS), maximum lead, and maximum lag of the offset errors were 0.01, 0.03, 0.05, and 0.06 m, respectively. Slightly larger average, RMS, and maximum lead offset errors were observed in the experiment with a varying reference velocity, which were 0.02, 0.04, and 0.09 m, respectively. These experimental results confirmed that the designed control laws for the synchronous tracking of the WSIC vehicles were effective and the developed automatic guidance system was reliable and applicable. Keywords: Automatic guidance, Master-slave control, Synchronous tracking, Wide-Span Implement Carrier (WSIC).

Underwater node coverage is the basis of various applications in underwater wireless sensor network (UWSN). It is easy to cause the coordinates of underwater nodes drift affected by water flow action. Some sparsely deployed underwater nodes may form coverage holes, which makes it impossible to locate underwater targets effectively. Combined with the water flow situation, this paper proposes an improved brain storm optimization integrated with virtual force algorithm (IBSO-VFA) for improving UWSN coverage performance. After analyzing the water jet model and the resultant force along Z axis, the velocity components of east, north and depth directions can be derived, which can reveal the coordinate evolution model of drifted underwater nodes under water flow action. Underwater nodes present non-uniform distribution of partly sparse and partly dense coverage, even with a lot of coverage holes. Inspired by the virtual force algorithm, the drifted underwater nodes are driven to their relative communicable positions. Meanwhile, the brain storm optimization has been improved and applied to avoid falling into local coverage optimum by pure VFA. Based on the node maximum coverage, the dual mapping of signal domain and localizability domain is established in consideration of ranging and coordinate errors. Finally, a comprehensive performance test is conducted to evaluate IBSO-VFA performance in terms of coverage rates, k-coverage and localizability area. The results indicate that the IBSO-VFA can maximize the UWSN coverage and localizability performance. The proposed IBSO-VFA can provide a close-to-practical coverage model for drifted underwater nodes, and can provide a theoretical basis for ocean information perception in UWSN.

Mobile vehicle positioning can provide the reference to navigation, tracking and multi vehicles collaboration. Applying spatiotemporal distribution characteristics of positioning errors between strap-down inertial navigation system (SINS) and wireless sensor network (WSN) approaches, a mobile vehicle positioning is proposed as a component of heterogeneous sensor networks (HSN). The attitude, velocity and position equations of mobile vehicle are derived based on the kinematics parameter constraints and inertial parameter errors. Meanwhile, WSN approach can provide position estimation using inaccurate anchor nodes. However, SINS is known for its cumulative errors over long time, while WSN approach can have large positioning errors in certain areas. As an effort to overcome the limitations of pure SINS or WSN approach, an integrated SINS and WSN approach is proposed to form a self-repairing HSN approach, which can provide sound position and attitude for mobile vehicle. Then, multi-parameter interaction and cooperative correction strategy are explored when SINS or WSN measurement is abnormal. Finally, a comprehensive set of experiments of position and attitude estimations for mobile vehicle are performed on the actual environment platform.

Purpose China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC. Design/methodology/approach Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years. Findings Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency. Originality/value A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.

The underwater environment is a class of typical enclosed environments, where the satellite signals are unavailable in underwater. To support the huge applications of underwater mobile targets, underwater wireless sensor networks (UWSNs) can be applied to monitor and locate these targets. As an effective and distributed sensor measurement method, anchor nodes inevitably have the coordinate drift caused by water flow effect. This paper proposes a coverage planning strategy to ensure network full coverage. Meanwhile, we outline the detail implementation of cooperative correction strategy about satellite signal water surface method and anchor node instable coordinate as simple as possible. The results show that the proposed underwater positioning methods can achieve real-time position, which can provide the reference for mobile targets navigation and path planning, and lay the basis for collaborative automation of mobile targets in the similar engineer applications.

Planetary gear is the key part of the transmission system for large complex electromechanical equipment, and in general, a series of degradation states are undergone and evolved into a local fatal fault in its full life cycle. So it is of great significance to recognize the degradation state of planetary gear for the purpose of maintenance repair, predicting development trend, and avoiding sudden fault. This paper proposed a degradation state recognition method of planetary gear based on multiscale information dimension of singular spectrum decomposition (SSD) and convolutional neural network (CNN). SSD can automatically realize the embedding dimension selection and component grouping segmentation, and the original vibration signal being nonlinear and nonstationary can be decomposed into a series of singular spectrum decomposition components (SSDCs), adaptively. Then, the multiscale information dimension which combines multiscale analysis and fractal information dimension is proposed for quantifying and extracting the feature information contained in each SSDC. Finally, CNN is used to achieve the effective recognition of the degradation state of planetary gear. The experimental results show that the proposed method can accurately recognize the degradation state of planetary gear, and the overall recognition rate is up to 97.2%, of which the recognition rate of normal planetary gear reaches 100%.