Condition-based early stage diagnosis robust ant colony clustering algorithm and feature mapping
Performance evaluation is crucial to increasing equipment availability and reliability as well as facilitating further proactive maintenance. This paper presents a novel condition-based early stage diagnosis method using robust ant colony clustering algorithm and feature mapping. Vibration signals acquired from equipments are decomposed by wavelet packet transform after wavelet denoising, and then feature measures based on frequency bands are extracted by ant colony clustering algorithm to form an input vector for training performance evaluation model. Operating conditions of equipment are predicted using feature mapping. Furthermore, a robust ant colony clustering algorithm is proposed to adjust comparison probability dynamically. Finally, effectiveness and feasibility of the proposed method are verified by vibration signals acquired from a drilling test bed.