Narrow Results

The traditional fault detection methods for turntable bearings mainly rely on manual inspection and simple vibration signal analysis. Although these methods can detect faults to a certain extent, they have limitations such as low efficiency, low accuracy, and susceptibility to human factors. To overcome the challenges and limitations of traditional methods, we propose a fault detection method for engineering crane turntable bearings based on the adaptive fireworks algorithm (AFA). Fault detection of turntable bearing of engineering lifting machinery based on an AFA is an innovative method using the fireworks algorithm (FWA) for fault detection. FWA is a kind of optimization algorithm with global search and local search ability, which can effectively solve complex engineering problems. In the fault detection of turntable bearing of engineering lifting machinery, the FWA adaptively adjusts the radius and number of fireworks explosions, so that the algorithm can search in the global scope and detect the fault more accurately. At the same time, the FWA also has a local search ability, which can carry out fine search of the fault area and improve the accuracy of fault detection. By applying the FWA to the fault detection of turntable bearing of engineering lifting machinery, the efficiency and accuracy of fault detection can be effectively improved, the cost of fault detection can be reduced, and the safe operation of engineering lifting machinery can be guaranteed. The fault detection method of turntable bearing of engineering lifting machinery based on an AFA is an innovative method with broad application prospects and can provide an effective solution for the fault detection of engineering lifting machinery.

Due to the influence of random wind shear in the atmospheric phenomenon, the random vibration of the main shaft of the wind turbine generator is generated. This vibration signal will be mixed with the misalignment signal of the high-speed shaft, which will cause interference to the fault diagnosis. Based on the analysis of the phenomenon of wind shear and the fault, the independent component analysis was carried out on the high-speed shaft mixed vibration signals on the basis of using rapid fixed point algorithm based on kurtosis, and the weak fault information is extracted successfully. At the same time, this method was compared with the weak information extraction method based on wavelet denoising, which proved the superiority of the proposed method. The experimental results show that the method has good field applicability and has a good application prospect in the field of weak information extraction for rotating machinery of wind power generation.

In a typical pressurized water reactor nuclear power plant, the condensate pumps contribute to the majority of the feedwater flow to the steam generators, which is converted into steam that spins the turbine generator to produce electricity. Adequate monitoring of the condensate pump feedwater flow is essential not only for the pump’s reliability but also for efficient plant operations overall. This study applies the functional principal component analysis in characterizing the vibration signals generated under six different levels of flow rates of the pump, an environmental factor. The functional design of experiments is then applied to obtain an optimal flow rate according to the target vibration curve. The obtained flow rate is found comparable to the theoretical pump curve’s best efficiency point and is recommended to be used for optimal pump performance and reliability.

This study proposes a structural damage recognition method based on the filtered feature selection (FFS) and the convolutional neural network (CNN). The FFS usually provides a better sample feature input for a CNN, avoiding the problem that the CNN is prone to over-fitting for the data containing a large amount of invalid feature information. To demonstrate the efficiency and accuracy of the method, a steel frame structure is investigated. The acceleration signals under three different measures (Chi-square test, F test and Mutual information method) of the FFS are studied, along with their influence on the CNN recognition accuracy, network training time and feature dimension. Studies have shown that the Chi-square test has the best effect over the other two measures in terms of efficiency and accuracy. The results of numerical simulations and vibration experiments show that the method has achieved good results in terms of recognition accuracy and training time, and it can significantly reduce the feature dimension while ensuring the accuracy of the CNN recognition.

In view of the frequency spectrum characteristics of vibration signal of rotating machinery, the versatile model of pattern recognition and fault diagnosis of rotating machinery based on wavelet packet-neural network is presented. The abrupt change information of vibration signal can be obtained and the features related to the fault can be extracted by employing the multi-dimension and multi-resolution characteristics of wavelet to decompose and reconstruct the vibration signal. Energy of special frequency ranges is selected as feature vector and is put into ART2 neural network, then the trained neural network is able to perform real-time diagnosis of rotating machinery fault. The effectiveness of this method is proved by emulating rotating machinery failures.

This paper proposes a new fault diagnosis scheme based on continuous density Hidden Markov Model (HMM) for vibration signals. Features extracted from vibration signals of rotor-gear-bearing transmission system are used to train HMMs to represent various running conditions. The feature vectors based on the node energies of wavelet packet decomposition are extracted from the vibration signals. Faults can be identified by selecting the HMM with the highest probability. The proposed method was tested by measuring the data of rotor-gear-bearing transmission system and has been demonstrated to be accurate and feasible.

In order to improve the mechanical structure of the type of fault resolution precision high voltage circuit breaker spring mechanism, the paper analyzes the characteristics of the circuit breaker and the combination of mechanical vibration signal, PSO algorithm (PSO), SVM parameter optimization method, proposed collaborative dynamic acceleration constant inertia weight particle swarm optimization (WCPSO) optimization, support vector machine (SVM) analysis breaker fault classification parameters and kernel function parameters. The vibration signal circuit breaker empirical mode decomposition, the total intrinsic mode components through energy analysis to obtain the required fault feature vectors and support vector machine as input, the use of dynamic acceleration constant synergy inertia weight PSO support vector machines penalty factor C and radial basis kernel function parameters optimize the fault feature vector signal input test samples after SVM training sample trained optimized for fault status classification. The experimental analysis of this method can effectively improve the resolution of the breaker failure signal type accuracy.

The deformation failure and mechanical vibration is closely related to power transformer windings. The problem of a transformer is poor accuracy in the diagnosis of mechanical deformation and difficult to accurately judge the winding fault type. In this paper, as the research object of the S11-M-500/35 type distribution transformer, to obtain the vibration information through the obstructing of transformer winding insulation, low voltage winding compression and loose winding deformation failure. Two signal processing methods, wavelet packet energy spectrum entropy and the short-time Fourier transform are adopted respectively to extract the feature of different deformation information. Using the method of fuzzy c-means clustering analysis to compare the classification of the two methods of feature extraction to determine the one with more effectiveness. The results demonstrate that the wavelet packet energy spectrum entropy feature extraction method can achieve the best classification results for the winding failure deformation type. Its membership degree is above 0.94, providing the basis for the later mechanical failure diagnosis of winding type.