Analysis of Magnetic Field Waveforms of Different Launching Stages of Rail Gun Based on Wavelet Transform

Railway foreign body vibration signal detection based on wavelet analysis

“…Under the theory of wavelet analysis, sampling and wavelet frequency operation are the main tasks of the whole detection, because the vibration signal of each rotor in a steam turbine is different, the signal frequencies generated by different vibration signals are calculated in cycles and data collection is realized, during the operation of the steam turbine, the sampled data needs to be set periodically, the set period is the sampling frequency, generally, the data of the sampling setting is the number of whole cycles in order to sample and analyze and get the specific frequency range, at the same time, it can be known according to the vibration characteristics of the internal rotor of the steam turbine and the vibration signal, when performing operations after sampling, multi-scale decomposition of the signal source is required, and confirm the frequency range, starting from the signal of the wavelet classification at the top level, it is calculated by the number of cycles step by step, by analogy, the signal frequency range of each level can be obtained [25,26]. In addition, the fault signals generated during counting and pulse statistics are also obstacles to detection that wavelet analysis often encounters, the fault signal characteristics of the machine need to be extracted in advance and loaded with a computer, the fault identification methods used in different modes are different, therefore, it is very important to accurately locate the signal frequency band where the fault is located before performing wavelet analysis, otherwise, the signal source strength will be reduced due to improper selection of frequency bands or uneven sampling in the later stage, the weak fault information is hidden in the frequency band, which increases the complexity and difficulty of the operation [27,28].…”