Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis

Mao, Qianjun; Fang, Xi; Hu, Yunpeng; Li, Guannan

doi:10.1016/j.applthermaleng.2018.07.104

Cited by 37 publications

(18 citation statements)

References 29 publications

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“…We therefore employ the Ensemble Empirical Mode Decomposition (EEMD) [ 56 , 57 ] as a self-adaptive filtering for echo intensity denoising, which could adaptively decompose the nonlinear and nonstationary waveform into the sum of components, the Intrinsic Mode Functions (IMFs) and one residual component, and then distinguish and remove IMF with noise as its main component, eliminating the mode mixing problem by adding finite white noise to the investigated signal. The brief process of EEMD denoising [ 58 , 59 ] is expressed as follows: Add a white noise with the given amplitude to the original signal in echo intensity; Perform EMD [ 60 ] to the signal in echo intensity with the added white noise to obtain

Intrinsic Mode Function (IMF) components and one residual component, whereas the definition and acquisition process of IMF are relegated to Appendix A ; Repeat with the given number of trials. In every trial, the number of IMF

, is a constant.…”

Section: Sand Wave Detection and Morphological Geometrical Topological Characterization With Echo Intensitymentioning

confidence: 99%

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Nian

Zang

Geng

et al. 2021

Sensors

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Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

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, is a constant.…”

Section: Sand Wave Detection and Morphological Geometrical Topological Characterization With Echo Intensitymentioning

confidence: 99%

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Nian

Zang

Geng

et al. 2021

Sensors

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“…and RVF j * and E½IMF 1 j * , E½IMF 2 j * , E½IMF 3 j * , and E ½IMF 4 j * . Equation ( 5) is used to obtain the normalized matrix Z org * of the original feature parameter matrix Z org of the vibration signal of R2R processing roll for flexible material is expressed as equation (6).…”

Section: Algorithm Derivation Of Pca Extraction Model Of Processing Roller's Performance Degradation Featurementioning

confidence: 99%

“…Previous studies have shown that principal component analysis (PCA), based on the idea of spatial transformation, achieves the purpose of optimal variance without reducing the information content contained in the original data and describes the high-dimensional data information with less principal component information, which has incomparable advantages over other algorithms [4][5][6]. In reference [4] (2018), spearman grade correlation coefficient and PCA were used for feature fusion to obtain the health index representing the declining state of rolling bearing performance.…”

Section: Introductionmentioning

confidence: 99%

“…This method is effective for fault detection and diagnosis of most of the unsupervised classification experimental data sets. In reference [6] (2018) proposed a sensor fault detection method of water chiller sensor based on empirical mode decomposition (EMD) threshold denoising and principal component analysis (EMD-TD-PCA). PCA model is established by EMD threshold denoising data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

Deng

Zhou

Yao

et al. 2020

International Journal of Rotating Machinery

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Performance feature extraction is the primary problem in equipment performance degradation assessment. To handle the problem of high-dimensional performance characterization and complexity of calculating the performance indicators in flexible material roll-to-roll processing, this paper proposes a PCA method for extracting the degradation characteristic of roll shaft. Based on the analysis of the performance influencing factors of flexible material roll-to-roll processing roller, a principal component analysis extraction model was constructed. The original feature parameter matrix composed of 10-dimensional feature parameters such as time domain, frequency domain, and time-frequency domain vibration signal of the roll shaft was established; then, we obtained a new feature parameter matrix Z org ∗ by normalizing the original feature parameter matrix. The correlation measure between every two parameters in the matrix Z org ∗ was used as the eigenvalue to establish the covariance matrix of the performance degradation feature parameters. The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. Finally, using the eigenvalue cumulative contribution rate as the screening rule, we linearly weighted and fused the eigenvectors and derived the feature principal component matrix F of the processing roller vibration signal. Experiments showed that the initially obtained, 10-dimensional features of the processing rollers’ vibration signals, such as average, root mean square, kurtosis index, centroid frequency, root mean square of frequency, standard deviation of frequency, and energy of the intrinsic mode function component, can be expressed by 3-dimensional principal components F 1 , F 2 , and F 3 . The vibration signal features reduction dimension was realized, and F 1 , F 2 , and F 3 contain 98.9% of the original vibration signal data, further illustrating that the method has high precision in feature parameters’ extraction and the advantage of eliminating the correlation between feature parameters and reducing the workload selecting feature parameters.

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“…ere are two types of denoising strategies for the threshold method of EMD. One is removing the noisedominated IMFs directly [6], and the other is denoising IMFs by wavelet thresholding denoising (WTD) [7]. In order to distinguish the noise-dominated IMFs and signaldominated IMFs, many methods have been proposed including the energy entropies of the IMFs [8], the correlation coefficients of the original signal and IMFs [9], and maximum variances of IMFs [10], among others.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

Zhang

Tian

Banerjee

et al. 2019

Mathematical Problems in Engineering

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Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

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Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis

Cited by 37 publications

References 29 publications

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD‐ICA‐WTD

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