Playing an important role in electromechanical systems, hydraulic servo system is crucial to mechanical systems like engineering machinery, metallurgical machinery, ships, and other equipment. Fault diagnosis based on monitoring and sensory signals plays an important role in avoiding catastrophic accidents and enormous economic losses. This study presents a fault diagnosis scheme for hydraulic servo system using compressed random subspace based ReliefF method. From the point of view of feature selection, the scheme utilizes CRSR method to determine the most stable feature (...) combination that contains the most adequate information simultaneously. Based on the feature selection structure of ReliefF, CRSR employs feature integration rules in the compressed domain. Meanwhile, CRSR substitutes information entropy and fuzzy membership for traditional distance measurement index. The proposed CRSR method is able to enhance the robustness of the feature information against interference while selecting the feature combination with balanced information expressing ability. To demonstrate the effectiveness of the proposed CRSR method, a hydraulic servo system joint simulation model is constructed by HyPneu and Simulink, and three fault modes are injected to generate the validation data. (shrink)

A new edge detection technique based on the texture feature coding method is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to (...) construct an edge detector. In particular, the texture feature number of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such as the Sobel, Prewitt, Canny, and Canny–Deriche edge detectors. The obtained results were evaluated quantitatively with the Figure of Merit criterion. The experimental results demonstrated that our proposed method improved the edge detection performance greatly. We further implemented the proposed edge detector with a hardware system. To this end, a field programmable gate array chip was used. The related simulations were carried out with the MATLAB Simulink tool. Both software and hardware implementations demonstrated the efficiency of the proposed edge detector. (shrink)

Computational intelligence techniques are being investigated as an extension of the traditional fault diagnosis methods. This article presents, for the first time, a scheme for fault detection and isolation via artificial neural networks and fuzzy logic. It deals with the sensor fault of a three-link selective compliance assembly robot arm robot. A second scheme is proposed for fault detection and accommodation via analytical redundancy, and it deals with the sensor fault of a three-link SCARA robot. These proposed FDI approaches are (...) implemented on Matlab/simulink software and tested under several types of faults. The results show the importance of this process. Then, the sensor faults are detected and isolated successfully. Also, the actuator faults are detected and a fault tolerance strategy is used for reconfigurable control using a sliding-mode observer. (shrink)

To meet the stability requirements for moving quadruped robots, it is important to design a rational structure for a single leg and plan the trajectory of the foot. First, a novel electrically driven leg mechanism for a quadruped robot is designed in this paper to reduce the inertia of the leg swing. Second, a modified foot trajectory based on a compound cycloid is proposed, which has swing-back and retraction motion and continuous velocity in the x-axis direction. Third, a Simulink (...) platform is built to verify the correctness of the proposed foot trajectory. The simulation result shows that when the flight phase and the stand phase switch, the impact of torque is smaller than the foot trajectory before modification. Finally, an experimental platform is constructed, and a control algorithm is written into the controller to realize the foot proposed trajectory. The results of the experiment prove the feasibility of the leg mechanism and the rationality of the proposed foot trajectory. (shrink)

The vehicle suspension system is represented by a complex group of components which connect the wheels to the frame or body. Its primary function is to reduce or absorb various vehicle vibrations generated by road disturbances, providing comfort and safety for passengers. Most modern vehicles have independent, active, or semiactive front and rear suspensions which allow the use of electronic actuation. For this reason, automotive engineers conduct research on the active suspension model to determine the most suitable control algorithm. Three (...) active suspension models are intensely used within simulations: the quarter-car, the half-car, and the full-car models. This paper proposes an adaptive harmonic control for a half-car active suspension system. The mathematical model of the suspension system is implemented in the MATLAB/Simulink environment. The control approach is tested via simulation, and several comparisons with the classical proportional-integral controller are provided. The simulation results show that the controller behaves quite similarly on the half-car model as it did on a quarter-car model. Additionally, an improvement to the harmonic control algorithm has been accomplished. (shrink)

Floods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years. These models are proposed and built-in proportion for risk reduction, policy proposition, loss of human lives, and property damages associated with floods. However, flood status prediction is a complex process and demands extensive analyses on the factors leading to the occurrence (...) of flooding. Consequently, this research proposes an Internet of Things-based flood status prediction (IoT-FSP) model that is used to facilitate the prediction of the rivers flood situation. The IoT-FSP model applies the Internet of Things architecture to facilitate the flood data acquisition process and three machine learning (ML) algorithms, which are Decision Tree (DT), Decision Jungle, and Random Forest, for the flood prediction process. The IoT-FSP model is implemented in MATLAB and Simulink as development platforms. The results show that the IoT-FSP model successfully performs the data acquisition and prediction tasks and achieves an average accuracy of 85.72% for the three-fold cross-validation results. The research finding shows that the DT scores the highest accuracy of 93.22%, precision of 92.85, and recall of 92.81 among the three ML algorithms. The ability of the ML algorithm to handle multivariate outputs of 13 different flood textual statuses provides the means of manifesting explainable artificial intelligence and enables the IoT-FSP model to act as an early warning and flood monitoring system. (shrink)

This article deals with detection and accommodation of sensor faults in longitudinal dynamics of an F8 aircraft model. Both the detection of the fault and reconfiguration of the failed sensor are done with the help of neural network-based models. Detection of a sensor fault is done with the help of knowledge-based neural network fault detection. Apart from KBNNFD, another neural network model is developed in this article for the reconfiguration of the failed sensor. A model-based approach of the neural network (...) is developed, which uses the radial basis function of the neural network. MBNN successfully does the task of providing analytical redundancy for the aircraft sensor. In this work, both detection and reconfiguration of a fault is done using neural networks. Hence, the control system becomes robust for handling sensor failures near steady state and reconfiguration is also faster. A generalized regression neural network, which is a type of radial basis network, is used for MBNN, which gives very efficient results for function approximation. An F8 aircraft model and C-Star controller, which improves its handling quality, are used for validation of the method involved. Models of F8 aircraft, C-Star controller, KBNNFD, and MBNN were developed using MATLAB/simulink. Successful implementation and simulation results are shown and discussed using Simulink. (shrink)

The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network and a fault detection algorithm based on discrete wavelet transform are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals (...) are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance. (shrink)