Fans are the important equipment in the manufacturing industry, which are easy to cause faults because of the complex working conditions and high speed. Various fault detection algorithms based on electrical, vibration and video have been proposed, but these algorithms are difficult to meet the detection requirements of enterprises. In this paper, a novel fan fault detection algorithm is presented based on sound signal detection technology. According to the different characteristics of the fan sound signal in the time and frequency domains, we combined the convolutional neural networks (CNN, which is sensitive to spatial information) and the gated recurrent unit (GRU, which is sensitive to timing information), and proposed a fan sound fault detection algorithm. Firstly, according to the variation characteristics of the acoustic signal in different states, the spectrogram of sound signal containing spatial and temporal characteristics is extracted, and the histogram equalization is used to enhance the spectrogram. On this basis, CNN and GRU are combined to train the Log-Fbank spectrogram respectively, so that it can better model the different characteristics of time domain and frequency domain information, so as to obtain a fan state model with high confidence and realize the sound fault detection of the fan. Experiments on MIMII dataset show that the algorithm has high fault recognition accuracy and fast convergence speed, which has good application value.
Whale optimization algorithm (WOA) is characterized by fewer parameters, simple structure, and stronger optimization seeking ability compared with traditional optimization algorithms, but in practical applications there are problems such as sluggish convergence speed and easily falling into local optimal solutions. This work proposes MAWOA, a whale optimization algorithm based on hybrid adaptive strategy, introducing a method to adaptively adjust the weights with the iterative situation of the population to accelerate the convergence of the algorithm; designing an adaptive adjustment threshold, and individuals select a random search method according to the value of the threshold to enhance the global search ability of the population and circumventing local values; introducing an adaptive nonlinear convergence factor to strengthen the algorithm in initial exploration breadth and later local development process. Twelve different morphological benchmark functions and a MAWOA-BP wine quality classification model were used to optimize the experiment. The results shows that MAWOA has stronger performance in terms of convergence speed and optimization-seeking accuracy, and the classification results are significantly improved compared with traditional classification models such as KNN, decision trees and BP neural networks.
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