A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.
This article aims to address problems in the current clustering process of low-energy adaptive clustering hierarchy (LEACH) in the wireless sensor networks, such as strong randomness and local optimum in the path optimization. This article proposes an optimal combined weighting (OCW) and improved ant colony optimization (IACO) algorithm for the LEACH protocol optimization. First, cluster head nodes are updated via a dynamic replacement mechanism of the whole network cluster head nodes to reduce the network energy consumption. In order to improve the quality of the selected cluster head nodes, this article proposes the OCW method to dynamically change the weight according to the importance of the cluster head node in different regions, in accordance with the three impact factors of the node residual energy, density, and distance between the node and the sink node in different regions. Second, the network is partitioned and the transmission path among the clusters can be optimized by the transfer probability in IACO with combined local and global pheromone update mechanism. The efficacy of the proposed LEACH protocol optimization method has been verified with MATLAB simulation experiments.
Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive learning particle swarm optimized fuzzy Petri net (CLPSO-FPN) algorithm is proposed for motor-bearing fault diagnosis. CLPSO is employed to obtain an adaptive system parameter set to reduce the fault-diagnosis error caused by human subjective factors. Moreover, a new proposed concept of the transition influence factor replaces the traditional transition confidence to improve the nonlinear expression ability of traditional Petri nets, which suppresses the space explosion problem of the fault-diagnosis model. Finally, experiments are implemented on a dataset of motor bearings. Compared with traditional faults diagnosis methods, the proposed method realized better performance in the fault location and prediction functions of motor bearings, which is beneficial for troubleshooting and motor maintenance.
This study aimed to improve the application of fuzzy Petri net to fault diagnosis of motor systems. An adaptive Neural Fuzzy Petri Network Algorithm based on the traditional Petri net theory, fuzzy theory, and neural network algorithm is proposed and applied to the diagnosis of motor faults. The transition confidence is replaced by a Gaussian function to solve the uncertainty of fault propagation. Combined with the BP neural network, fault diagnosis parameters are adaptively trained. Finally, the Neural Fuzzy Petri Net Algorithm is applied to the fault diagnosis of a three-phase asynchronous motor, considering its fault operation mechanism and fault characteristics. The results show that the algorithm can diagnose the fault of the three-phase asynchronous motor with satisfactory accuracy and adaptability.
Aiming at the decrease of the accuracy of fusion data caused by the abnormal value and noise interference in the multi-sensor observations, this paper proposes a multi-sensor data fusion algorithm based on improved weighting factors. Firstly, the Dixon criterion is used to eliminate outliers in observations to avoid data containing gross errors. Then the Kalman filter algorithm is used to effectively reduce the noise impact caused by various reasons and provides the optimal data for weighted data fusion. Finally, an improved weighted fusion algorithm is used to comprehensively consider the nature of the sensor and the influence of various factors in the measurement process to obtain the best fusion data. The simulation analysis of the soil humidity in the greenhouse shows that the error of the multi-sensor data fusion algorithm based on the improved weighting factor is maintained at 0.04%-0.18%. Compared with the adaptive weighted fusion algorithm, the error of this algorithm is reduced by 0.12%, which verifies the algorithm’s effectiveness.
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