A Two-Stage Learning Method For Fault Detection of Machines Using Mechanical Big Data - Jamadagni, MS; Nagarkar, KA; Badame, VS; Chindwin, CR; Lomte, VM (International Journal of Computer Sciences and Engineering, 2025)
Intelligent fault diagnosis is a promising instrument to deal with mechanical big data because of its capacity in quickly and proficiently handling gathered signals and giving exact diagnosis outcomes. Feature extraction is done manually in most of the traditional techniques which required previous knowledge along with diagnostic expertise. Such procedures take favourable position of human inventiveness is tedious and work escalated. The main possibility of unsupervised component discovering that utilization of intelligence systems to learn raw data, a two-stage learning technique is proposed for intelligent analysis of machines. In the first stage vibration signal is utilized to get a grasp on features from mechanical vibration signals. In the next stage, softmax regression is used to classify the health conditions depends on the studied features. The approach is verified by a motor bearing dataset and a locomotive bearing dataset. It can be seen that using this method high diagnosis accuracy can be obtained. Also, the proposed method reduces the need of human labour making it preferable than the existing methods.