A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines

Xu, Xiaobin; Yan, Xinping; Cui, Sheng; Yuan, Chengqing; Xu, Dong-Ling; Yang, Jian‐Bo

doi:10.1109/tsmc.2017.2759026

Cited by 120 publications

(32 citation statements)

References 35 publications

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“…The stability of the above studies are, respectively, observed from two different perspectives-aspects of visual function and disturbance suppression-but the environmental limitations are more prominent. Obviously, the nonlinear problem of the visual servoing system is inevitable [21][22][23]. Elastic objects will deform under the condition of complete constraint, which will bring more nonlinear problems to visual servo control.…”

Section: Literature Reviewmentioning

confidence: 99%

“…David [21] proposed an uncalibrated algorithm based on Lyapunov to estimate the visual Jacobian matrix in the deformed state; he applied this method to a clamping operation based on visual servo control, and combined the pose information of the gripper with the visual information to realize the recognition and control of its pose. Xu [22] extracted sensitive features from image information to meet the requirements of position and direction control. The direction and position of the target were controlled by the idea that the target size is sensitive to the depth of the image.…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

et al. 2020

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This paper proposes a visual servo scheme for hoisting positioning under disturbance conditions. In actual hoisting work, disturbances such as equipment and load vibration are inevitable, which brings challenges to the development of a visual servo for hoisting positioning. The main problems are as follows: (1) the correlation between visual error and disturbance is not considered or well resolved; (2) the disturbance has a great influence on the control stability, but it is difficult to model. At present, there is no detailed research on the above problems. In this paper, the visual error is defined by the image error of the feedback signal based on dynamic equations containing disturbances. An adaptive sliding mode control algorithm is employed to decrease the influence of external disturbance, and the coefficient of the slide surface is established based on the adaptive gain. In view of the belief that it is difficult to model disturbance terms, a nonlinear disturbance observer is introduced to obtain equivalent disturbance. On this basis, an adaptive control algorithm with disturbance compensation is proposed to improve the robustness of the visual servo system. We use Lyapunov’s method to analyze the stability conditions of the system. Compared with the other state-of-the-art methods, the simulation results show that our method has superior performance in convergence, accuracy, and restraining disturbance. Finally, the proposed algorithm is applied to the hoisting platform for experimental research, which proves the effectiveness of the controller.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

et al. 2020

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“…In the overall double-pendulum gantry crane control system as shown in Figure 4 Remark 1: The friction model (8) is used as the feed forward assembly to compensate for friction between the trolley and the tracks. For the sake of conciseness, we will focus on the development of the resultant force ( ) F t , because we can get the actuated force ( ) a F t directly from (7). Remark 1.…”

Section: Controller Designmentioning

confidence: 99%

“…Gantry cranes are typical representatives of symmetry underactuated systems. Among different types of nonlinear underactuated control systems [4][5][6][7], there are many unsolved challenges in gantry crane control.…”

Section: Introductionmentioning

confidence: 99%

Research on Nonlinear Coupling Anti-Swing Control Method of Double Pendulum Gantry Crane Based on Improved Energy

Shi

et al. 2019

Symmetry

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The double pendulum type gantry crane is a typical symmetry underactuated motion system. It has control problems in that the swing of the payload is difficult to suppress and the precise positioning of the trolley is not accurate. A new nonlinear coupling control method based on improved energy is proposed in this paper. We define coupled control signal among trolley, hook and payload. An improved energy storage function is established based on the new coupling control signal. Consequently, a nonlinear anti-swing controller is constructed straightforwardly, and the closed-loop system stability is subject to strict mathematics analysis by Lyapunov and LaSalle’ s theorem. Moreover, the new energy function based on the coupling behaving between the trolley motion and the payload swing leads to the improved control performance. Numerical simulation results show that the proposed method has better performance than traditional controllers. It not only effectively suppresses the swing of the load and the hook, but also precisely controls the displacement of the trolley. It has strong robustness to the displacement of the payload, the change of the gantry crane parameters and the external disturbance.

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“…However, the detection of faults and the diagnosis of diesel engines [3] are not simple tasks due to the complex structure and fickle working conditions. If the operating conditions are not considered in detection and diagnosis activities, it is likely to lead to false alarms or missed detection [4,5]. With the information of operating conditions, the engineering applicability of a fault detection and diagnosis method [6][7][8] can be improved to avoid fatal performance degradation and huge economic losses at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Jiang

Lai

Zhang

et al. 2019

Sensors

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For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.

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A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines

Cited by 120 publications

References 35 publications

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition

Research on Nonlinear Coupling Anti-Swing Control Method of Double Pendulum Gantry Crane Based on Improved Energy

Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

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