Multi-source fault diagnosis of chiller plant sensors based on an improved ensemble empirical mode decomposition Gaussian mixture model

Zhang, Boyan; Yan, Xiao; Li, Guangyu; Fan, Kaixing

doi:10.1016/j.egyr.2022.01.179

Cited by 13 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In EMD, a given signal

is decomposed into a set of single-component signals (i.e., IMF) and residuals, i.e.,

where

represents the i -th IMF obtained by the decomposition algorithm, and

represents the residual term obtained by the signal decomposition. The screening process of EMD consists of two steps [ 31 ]: (1) estimating the “local mean”, and (2) extracting the local mean from the given signal until the resulting signal meets the IMF standard.…”

Section: Methodology and Theorymentioning

confidence: 99%

Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA

Zhou

Xiong

Bai

et al. 2022

Sensors

View full text Add to dashboard Cite

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis.

show abstract

“…In EMD, a given signal

is decomposed into a set of single-component signals (i.e., IMF) and residuals, i.e.,

where

represents the i -th IMF obtained by the decomposition algorithm, and

Section: Methodology and Theorymentioning

confidence: 99%