89 results about "Mode mixing" patented technology

The invention discloses a mechanical fault diagnosis method and system based on TJM transfer learning. According to the method, CEEMDAN decomposition is introduced. The algorithm calculation amount isreduced while the mode mixing problem is solved, and meanwhile the problems that according to a traditional machine learning method, when training and testing data distribution has a certain degree of difference, the established classification model is poor in popularization capacity, and even sometimes the classification model cannot be universally used are solved through a transfer learning method. And meanwhile, the problem of low fault diagnosis efficiency caused by data difference between different working conditions of the rotary machine is solved, and the problems that the fault stateis incomplete and the fault diagnosis cannot be correctly and completely carried out due to insufficient data acquisition quantity of the rotary machine in some working states are also solved. According to the method, the characteristic that cross-domain feature matching and instance reweighting are jointly executed in the TJM transfer learning method is utilized, the problem that the recognitionand diagnosis rate is not high due to the fact that the data difference between the source domain and the target domain is large is solved to the maximum degree, and the fault diagnosis precision is greatly improved.

The invention discloses a denoising method based on hybrid EMD (Empirical Mode Decomposition), and belongs to the technical field of digital signal processing. According to the denoising method disclosed by the invention, EEMD (Ensemble Empirical Mode Decomposition) and MEMD (Multivariate Empirical Mode Decomposition) algorithms are combined, and aiming at a masking signal frequency used in the MEMD algorithm, parameter optimization is carried out, so that mode mixing can be more effectively eliminated, and a denoising effect is better; and according to the denoising method, targeted optimization is further carried on an additive noise amplitude in the EEMD, filtering thresholds of noise related IMF (Spurious modes) and filtering thresholds of noise IMF, so that an integral denoising effect is further improved. Compared with the prior art, the method disclosed by the invention can more effectively eliminate mode mixing, and has a better denoising effect.