A mechanical data analysis using kurtogram and extreme learning machine

Today’s industry demands precise functioning and zero failure of rotating machinery (RM) to avoid disastrous accidents as well as financial losses. Rolling element bearings (REBs) are the heart of RM. Therefore, as early as possible to provide the significant time for maintenance planning, an intelligent diagnosis of REB fault is a critical and challenging task. Thus, this paper presents an efficient method for fault diagnosis. The proposed method mainly consists of two consecutive units: (1) generation of kurtogram of raw vibration signal and (2) training of extreme learning machine (ELM) classifier using kurtogram. Kurtogram has a distinct capability to represent the hidden non-stationary components of a raw signal. Therefore, it is considered as a unique feature vector for fault classification. ELM is a well-organized fast learning method proposed by Huang et al. and showed that it is better than traditional learning algorithms. However, one of the open issues of ELM is to design compact-size ELM architecture by preserving the accuracy of the solution. Thus, improved random increment ELM is proposed in this paper. Initially, it randomly adds the nodes to network architecture to rapidly reduce the residual error up to the predefined threshold and then sequentially adds the nodes to the network architecture for further reducing the residual error. Performance of the proposed routine is evaluated by REB vibration data: artificially generated vibration data and Case Western Reserve University bearing data. The experimental study reveals the classification accuracy of the proposed approach with both the datasets for various faults and also compared with existing methods.