Measurement of machine performance degradation using a neural network model
Computers in Industry - Special issue: computer integrated manufacturing (ICCIM '95)
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Novel Ant Clustering Algorithm Based on Cellular Automata
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Adapting k-means for supervised clustering
Applied Intelligence
Wavelet-based signal de-noising via simple singularities approximation
Signal Processing
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
A Quick Ant Clustering Algorithm
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Finding groups in data: Cluster analysis with ants
Applied Soft Computing
A simple and fast algorithm for K-medoids clustering
Expert Systems with Applications: An International Journal
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Performance prediction is crucial to increasing drill availability and reliability as well as facilitating further proactive maintenance. This paper presents a novel method for pattern recognition based performance prediction. The features of vibration signals are extracted by wavelet packet transform after de-noising, and then band energy based feature measures are identified by ant colony clustering analysis to form an input vector for the performance assessment model. Prediction of performance degradation trend of drill is carried out using feature mapping based on the established performance assessment model. Experimental results have shown that the proposed method can analyze drill degradation quantitatively, and predict problems before they occur. Furthermore, ant colony clustering algorithm is improved to adjust comparison probability dynamically and detect outliers. Compared with other ant colony clustering algorithms, the algorithm has higher convergence speed to meet requirements of real-time analysis as well as further improvement of accuracy. Finally, effectiveness and feasibility of the proposed method are verified using vibration signals acquired from a drill test bed.