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In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving th...
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... to the signal length, AUPLMD decomposes the signal into 13 PFs. Calculate the kurtosis value of the PF component according to the kurtosis criterion, as shown in Table 1. If the signal contains more fault components, the kurtosis value of the signal will be larger, so the PF component with a larger absolute value of kurtosis should be selected to reconstruct the signal, so as to analyze the signal of each state. ...Context 2
... to the signal length, AUPLMD decomposes the signal into 13 PFs. Calculate the kurtosis value of the PF component according to the kurtosis criterion, as shown in Table 1. If the signal contains more fault components, the kurtosis value of the signal will be larger, so the PF component with a larger absolute value of kurtosis should be selected to reconstruct the signal, so as to analyze the signal of each state. ...Similar publications
Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue...
Citations
... By optimizing the coarse-graining process in multi-scale permutation entropy, TSMWPE is less affected by the length of the signal. As a result, the entropy values obtained from feature extraction using TSMWPE are more stable, regardless of the length of the time series (Bandt and Pompe 2002;Dong et al. 2019;Song et al. 2022). The calculations involve defining y k,β for the original time series X = {x 1 , x 2 , . . . ...
Accurate and timely runoff prediction is essential for effective water resource management and controlling floods and droughts. However, the stochasticity of runoff due to environmental changes and human activities poses a significant challenge in achieving reliable predictions. This paper presents a multi-scale two-phase processing strategy to develop a hybrid model for runoff prediction. In the first phase of model design, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is utilised to identify significant frequencies in the non-stationary target data series. The inputs to the model are decomposed into intrinsic modal functions during this stage. In the second phase, the swarm decomposition (SWD) is used to decompose high-frequency components with consistently high values of time-shift multi-scale weighted permutation entropy (TSMWPE) into sub-sequences. This permits further identification and establishment of data attributes that are incorporated into the extreme learning machine (ELM) algorithm. The ELM then simulates the series of component data, creating a comprehensive tool for runoff prediction. The hybrid model demonstrates exceptional accuracy, achieving a Nash-Sutcliffe efficiency greater than 0.95 and a qualification rate exceeding 0.93. This model can be utilised in decision-making systems as an efficient and accurate solution for generating reliable predictions, particularly for hydrological challenges characterized by non-stationary data.
... The results revealed that LMD is more suitable and performs better than EMD for the incipient fault detection. Song et al. 38 proposed a fault feature extraction method that combines adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE) to recognize different categories and severities of reciprocating compressor valve faults. Yang and Zhou 39 utilized LMD and wavelet packet transform (WPT) to extract fault features of a diaphragm pump check valve. ...
The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy‐concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)‐based and empirical mode decomposition (EMD)‐based de‐noising methods is no longer applicable. To address this issue, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection, and waveform smoothing (OD‐WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well‐known methods like WT‐based and EMD‐based de‐noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals.
