Vibration-Based Signal Analysis for Shearer Cutting Status Recognition Based on Local Mean Decomposition and Fuzzy C-Means Clustering
Source abstract
Abstract
In order to accurately acquire shearer cutting status, this paper proposed a pattern recognition method, based on the local mean decomposition (LMD), time-frequency statistical analysis, improved Laplacian score (LS), and fuzzy C-means (FCM) clustering algorithm. The LMD was employed to preprocess the vibration signals of shear cutting coal seam, and several product functions (PFs) were obtained. Following this, 14 time-frequency statistical parameters of the original signal and optimal PF were extracted. Additionally, the improved LS algorithm was designed to ensure the accurate estimation of features, and a new feature vector could be selected. Subsequently, the obtained eigenvector matrix was fed into a FCM to be clustered, for optimal clustering performance. The experimental examples were provided to verify the effectiveness of the methodology and the results indicated that the proposed algorithm could be applied to recognize the different categories of shearer cutting status.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Machine Fault Diagnosis Techniques · Gear and Bearing Dynamics Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China
School of Information and Electrical Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China
Funding
National Natural Science Foundation of China · National Key Basic Research Program of China · Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions
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