To meet the autonomous navigation requirements of "Xin Hong Zhuan", a research and training i-Ship, the motion model of the dual-podded-propulsion ship is constructed through mechanistic modeling and data-driven methods, and course-keeping control is then carried out. The MMG model structure is adopted, and empirical formulas are used to obtain partial coefficients. Using the real ship sea trial records and the dual-pods open-water test data, the direct sailing resistance formula is obtained by fitting. Based on the simulated turning circle test, PSO, and sea trial data, partial hydrodynamic coefficients are obtained and the estimated results from empirical formulas are corrected. The second-order linear active disturbance rejection controller is designed based on the proposed model. Simulations show that the performance indicator of the model is similar to that of the real ship, and the model can effectively follow the sinusoidal desired heading with an RMSE of 0.342deg. This paper provides a reference for the research on autonomous navigation of dual-podded-propulsion ships such as "Xin Hong Zhuan".

A modified line-of-sight (LOS) guidance law that can be adaptively adjusted according to a reference path is presented for the development of smart ships and the realisation of autonomous navigation. A time varying lookahead distance and an advanced turning strategy, based on the path turning angle, are proposed to achieve flexible steering tracking capability. The path following control experiment was conducted with the container ship ZYHY LV SHUI 01 serving as the control object. The results of the simulation experiment demonstrate that the proposed MLOS algorithm is capable of adapting to changes in the path and achieving smooth steering, which enhances the accuracy of the tracking process, reduces energy consumption, and reduces the steering frequency. These outcomes are of great significance for smart ships to achieve autonomous navigation.

In autonomous ship navigation tasks, it is essential to conduct trajectory tracking control research to satisfy the varying reference speed or precision arrival time needs. Most control methods idealize the ship's actuators, treating thrust and torque as controllable inputs for direct control law design, resulting in relatively weak engineering realizability. In this paper, modeling the object ship based on engineering reality, a control method which combines virtual ship leading with integral line-of-sight methods is proposed. A virtual ship is first generated using the trajectory. Then the speed controller bases on estimation algorithm to synchronize the realistic ship with the virtual ship, and compensate for disturbances using correction. Meanwhile, the course controller uses the improved integral line-of-sight method to obtain the desired heading, which converges the transverse error, simplifying the problem to course keeping. In this way, ship trajectory tracking is realized. In addition, harmful high-frequency components of the command signal are suppressed. Simulation experiment results validate the effectiveness of the proposed control approach.

A fuzzy control improvement method is proposed with an integral line-of-sight (ILOS) guidance principle to meet the needs of autonomous navigation and high-precision control of ship trajectories. Firstly, a three-degree-of-freedom ship motion model was established with the battery-powered container ship ZYHY LVSHUI 01 built by the COSCO Shipping Group. Secondly, a ship path-following controller based on the ILOS algorithm was designed. To satisfy the time-varying demand of the look-ahead distance parameters during the following process, especially under different navigation conditions, fuzzy logic controllers were designed for different navigation conditions to automatically adjust the look-ahead distance parameters. Thirdly, a controller was applied that uses a five-state extended Kalman filter (EKF) to estimate the heading, speed, and heading rate based on the ship’s motion model with the assistance of Global Navigation Satellite System (GNSS) position measurements. This provides the necessary navigational information, reduces the algorithm’s dependence on sensors, and improves its generalizability. Finally, path-following experiments were carried out in the MATLAB experimental platform, and the results were compared with different following algorithms. The simulation results showed that the new algorithm has a better following performance, and it can maintain a smooth rudder angle output. The research results provide a reference for the path-following control of ships.

A fault prediction method for shafting of main engine is developed based on shaft vibration monitoring.Ensemble Empirical Mode Decomposition(EEMD)and Enhanced Intermittent Unknown Input Kalman Filter(EIIKF)are introduced into the fault prediction method.The vibration signal is mixed with a white noise before decomposition to prevent the modal mixing and improve the decomposability.The vibration signal,after filtering and reconstruction,is processed by sequential analysis to get the characteristic curve of the signal.EIIKF is used to analyze the characteristic curve and do working status prediction.In this processing,by introducing intermittent parameters,the uncertainty caused by some unknown input items is compensated.Fault diagnosis is carried out by checking the working status against a fault discrimination model.The method is verified with actual data from engine operation.The fault prediction capability of the method is seen better than that of conventional mode decomposition and Kalman filtering in terms of accuracy and timeliness.

The engine room equipment is an important part of the ship power system. It is of great significance to monitor, analyze and predict the status data of the engine room equipment to ensure the normal operation of the ship power system. Ship engine room is a variety of equipment such as pumps, diesel engine and the shafting of complex electromechanical system. Mostly rotating machinery equipment, the moving parts is much and complicated structure. In order to realize monitoring, analysis, forecast to the running condition of engine room equipment. The analysis and prediction of vibration signal of mechanical equipment is the key issue. For ship shafting vibration data, this paper presents a fault trend prediction method based on improved empirical mode decomposition and enhanced Intermittent unknown input Kalman filter. First, add white noise signals before modal decomposition, which can optimize the decomposability of the signal and avoid modal aliasing. Then, the characteristic curves of the vibration signals were obtained by sequential analysis of the filtered and reconstructed signals. EIIKF method was used to analyze and predict the characteristic curves, and intermittent parameters were added to compensate for the uncertainties caused by some unknown input items. On this basis, fault diagnosis is carried out by fault discrimination model, and fault prediction based on vibration signal of shaft system is realized. The 90-day measured data are verified by this method, and the sensitivity and accuracy of the prediction results are better than those predicted by general modal decomposition and Kalman filter. The effectiveness and superiority of the improved method are verified.

为解决船舶轴系振动信号中冗余信息过多、故障特征难以识别的问题,提出一种改进的包络分析方法.使用谱峭度(Spectrum Kurtosis,SK)选择包络分析的频带,应用峭度图确定最大SK对应最优频带参数,采用最优频带参数对振动信号进行滤波;对滤波后的信号使用小波包进一步降噪,并对处理后的信号进行包络分析,根据包络谱确定故障类型.采用该方法可以实现在强背景噪声条件下轴系故障的检测.

The marine shafting system may have many kinds of faults during the voyage. This paper proposed an improved envelope analysis method for marine shafting fault detection. The kurtogram was employed to maximize the spectral kurtosis (SK) of the signals and then SK was employed to select frequency bands for envelope analysis. The signals were bandpass filtered by the parameters determined by SK. Then wavelet packet was employed to denoise the filtered signals. The processed signal was analyzed by envelope spectrum which was obtained by the Hilbert transform. A bearing fault dataset, which offered by Society for Machinery Failure Prevention Technology (MFPT), and marine shafting test rig built in the laboratory were employed to validate the method. The results showed that the proposed method can be used to detect the faults of marine shafting under strong noise background.

The prediction of bearing degradation trend is important for remaining useful life (RUL) estimation. However, there are no clear indicators due to the presence of noise, and long step prediction is not effective enough. This paper proposed a predict method using LSTM in which the time domain and spectral kurtosis related features was selected by monotonicity. Then PCA was employed to fuse features. Health indicator was generated which was increasing as the bearing approaches to failure. A sequence-to-one regression LSTM network was employed to predict the trend of health indicator. Intelligent Maintenance Systems (IMS) bearing run to failure dataset was employed to verify the validity of the method. The result shows that proposed method can effectively predict the degradation trend. This method can be used to real-time estimate the RUL.

Wind turbine (WT) usually works under a poor condition, its operating speed varies almost all the time, therefore, the transmission mechanism of planetary gearbox tends to occur fault to a large extent, and an effective fault detection method for WT planetary gearbox is urgently needed. However, the traditional fault detection methods which are based on constant operating speed assumption will be invalid in such a complex situation, this paper provides a novel tacholess order tracking method based on generalized demodulation (GD) for WT fault detection. Firstly, the conventional GD is modified with dual path optimization ridge estimation (DPORE) strategy, and the main innovative idea of the proposed method is that the phase reference information is obtained from the generator shaft vibration signal through GD and Hilbert transform rather than the gearbox vibration signal. Then the raw gearbox vibration signal could be resampled with uniform angle. Finally, the envelope order spectrum is obtained and the fault characteristic order (FCO) related to the WT planetary gearbox fault can be identified without auxiliary sensors. The effectiveness of the proposed method is demonstrated by real-world WT vibration signals, compound-faults on planetary gearbox can be effectively uncovered and a better performance is obtained when compared with the conventional method. (C) 2019 Elsevier Ltd. All rights reserved.