Tran et al., 2021 - Google Patents
Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosisTran et al., 2021
- Document ID
- 12316659786736020563
- Author
- Tran M
- Elsisi M
- Liu M
- Publication year
- Publication venue
- Measurement
External Links
Snippet
Feature selection represents the main challenge against the classification strategies for several applications of signal processing. Besides, the high computational speed and accuracy represent a critical requirement of chatter diagnosis. In this paper, a new fuzzy …
- 238000003745 diagnosis 0 title abstract description 23
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tran et al. | Effective feature selection with fuzzy entropy and similarity classifier for chatter vibration diagnosis | |
| Tran et al. | Effective multi-sensor data fusion for chatter detection in milling process | |
| Mohanraj et al. | Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms | |
| Ji et al. | Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation | |
| Madhusudana et al. | Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal | |
| Zhu et al. | Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results | |
| Niu et al. | Multisensory based tool wear monitoring for practical applications in milling of titanium alloy | |
| Zhang et al. | Classification of fault location and performance degradation of a roller bearing | |
| Zheng et al. | Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM | |
| Wang et al. | A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot | |
| Peng et al. | A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine | |
| Gharavian et al. | Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes | |
| Lin et al. | Sequential spindle current-based tool condition monitoring with support vector classifier for milling process | |
| Wang et al. | Investigation on an in-process chatter detection strategy for micro-milling titanium alloy thin-walled parts and its implementation perspectives | |
| Patange et al. | Review on tool condition classification in milling: A machine learning approach | |
| Zhou et al. | Tool condition monitoring in milling using a force singularity analysis approach | |
| Sun et al. | Online machining chatter forecast based on improved local mean decomposition | |
| Wei et al. | Tool wear state recognition based on feature selection method with whitening variational mode decomposition | |
| Chen et al. | Chatter detection in milling processes using frequency-domain Rényi entropy | |
| Zheng et al. | Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process | |
| Lu et al. | In-process complex machining condition monitoring based on deep forest and process information fusion | |
| Bonda et al. | Vibration signature based stability studies in internal turning with a wavelet denoising preprocessor | |
| Shuuji et al. | Low-speed bearing fault diagnosis based on improved statistical filtering and convolutional neural network | |
| Lai et al. | A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters | |
| Han et al. | Chatter detection in milling of thin-walled parts using multi-channel feature fusion and temporal attention-based network |