Recognition, Processing, and Detection of Sensor Fault Signal Based on Genetic Algorithm

Abstract

With the development of electronic information science and network transmission technology, signal processing technology is widely used in various fields. The processing of sensor coarse signal is the key of signal processing technology, in order to study the signal detection and transmission function of the sensor. A genetic algorithm-based sensor fault signal identification, processing, and detection are proposed, and three common signal analysis and processing methods are summarized. The methods of optimal arrangement of sensors are as follows: effective independent algorithm, genetic algorithm, simulated annealing algorithm, and ant colony algorithm principle are studied in detail; signal analysis methods are as follows: fast Fourier transform, wavelet transform, and HHT transform are studied in detail. In the experimental system of the sensor’s coarse signal processing mode, the optimal arrangement of the measurement points of the acceleration sensor in this experiment is directly related to the information collection effect of the monitoring system. Combined with numerical simulation and engineering cases, the soft computing (genetic algorithm, simulated annealing algorithm, and ant colony algorithm) is analyzed in detail, out of the MATLAB program for soft computing. Taking four typical functions as the numerical experimental platform, the three algorithms are used for comparative experimental analysis, and their optimized performance and application range are analyzed. Finally, the practical application performance of soft computing is tested by the practical application problem of optimal path optimization of measuring points. When there are only 10 measuring points, all three algorithms can quickly converge to the global optimal solution, but when there are 100 measuring points, only approximate solutions can be obtained.