Intelligent fault diagnosis method for common rail injectors based on hierarchical weighted permutation entropy and pair-wise feature proximity feature selection

It is of great significance for intelligent manufacturing to study diagnosis methods to realize the diagnosis of mechanical equipment faults. Multiscale weighted permutation entropy is an effective...

[1]  Wenbo Lu,et al.  Dynamic response and control of middle rock sidewall under impact of blast loading , 2019, Journal of Vibration and Control.

[2]  José Antonio Lozano,et al.  Mutual information based feature subset selection in multivariate time series classification , 2020, Pattern Recognit..

[3]  Enzhe Song,et al.  Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy , 2019, Entropy.

[4]  S. Pincus Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[5]  Zhi Zhong,et al.  Adaptive graph learning and low-rank constraint for supervised spectral feature selection , 2019, Neural Computing and Applications.

[6]  An-bo Ming,et al.  Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine , 2017 .

[7]  Junsheng Cheng,et al.  Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosis , 2018 .

[8]  Pengjian Shang,et al.  Financial time series analysis using the relation between MPE and MWPE , 2020 .

[9]  Wang Zhenya,et al.  Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine , 2020 .

[10]  Weiting Chen,et al.  Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.

[11]  Jun Zhang,et al.  Composite multi-scale weighted permutation entropy and extreme learning machine based intelligent fault diagnosis for rolling bearing , 2019, Measurement.

[12]  Yuan Zhang,et al.  Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy , 2019, Entropy.

[13]  Zuhua Jiang,et al.  Start-of-injection-based software optimization for consistency between the cylinders in common-rail diesel engines , 2016 .

[14]  Rong Wang,et al.  Stable and orthogonal local discriminant embedding using trace ratio criterion for dimensionality reduction , 2018, Multimedia Tools and Applications.

[15]  Jianzhong Zhou,et al.  Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy , 2019, Entropy.

[16]  Burhan Ergen,et al.  Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models , 2020 .

[17]  Xianzhi Wang,et al.  An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery , 2019, Journal of Vibration and Control.

[18]  Yashvir Singh,et al.  Prediction of performance and emission parameters of Kusum biodiesel based diesel engine using neuro-fuzzy techniques combined with genetic algorithm , 2020 .

[19]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

[20]  Choong-Hoon Lee,et al.  An uncertainty analysis of the time-resolved fuel injection pressure wave based on BOSCH method for a common rail diesel injector with a varying current wave pattern , 2018, Journal of Mechanical Science and Technology.

[21]  Bartosz Krawczyk,et al.  Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble , 2017, Eng. Appl. Artif. Intell..

[22]  Sener Uysal,et al.  Improved SAR target recognition by selecting moment methods based on Fisher score , 2020, Signal Image Video Process..

[23]  Andrés Marino Álvarez-Meza,et al.  Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM , 2020 .