Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis  
[PERSON]  
1 Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam  
2 School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; [EMAIL_ADDRESS]  
3 School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, Australia; [EMAIL_ADDRESS]  
4 School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; [EMAIL_ADDRESS]  
5 Department of Automatic Control and Robotics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel. : +673-73456235  
timely basis from the impeding damage to final failure using either event data or condition monitoring (CM) data. Steel mill industries rely on a number of rotating parts, i.e., slew bearings. These bearings support highly loaded rotation and operate at a very low speed. When unforeseen failure occurs, the steel mill industry may suffer from significant production loss. In order to predict unforeseen failure, a condition monitoring and prognosis method is required. This requirement is becoming difficult to fulfil without online real-time predictive analytics capable of delivering a reliable prediction. The prediction method for self-updating the model must be able to keep pace with non-stationary processes in typical steel mill industries due to the production target. Most processes are also subjected to a number of changing external variables. This trait cannot be handled by a static model, where its structure is fully determined in its initial design. A model is supposed to be flexible for new concepts which normally lead to the expansion of its initial structure. An over-complex structure adversely affects the model's generalization because of overfitting. These research issues have led to algorithmic development of the so-called evolving intelligent systems (EISs) [2; 3], which have attracted significant research interest over the past decade [4; 5; 6; 7]. EISs have been successfully deployed in several predictive maintenance tasks [8; 9; 10]. This paper presents time-series feature prediction using a seminal work, namely the parsimonious network based on a fuzzy inference system (PANFIS) [11]. PANFIS is a fully open structure whose network structure can self-evolve during the process from data streams. PANFIS utilises the theory of statistical contribution [7; 12] to prune its fuzzy rules. A unique feature of PANFIS is rgw generalized Takagi Sugeno Kang (TSK) fuzzy system, where it scatters multivariate Gaussian functions as rule premise by using the first-order TSK rule consequent. It is well-known that such rules obscure rule semantics since the atomic rules of classic fuzzy rules vanish. PANFIS is equipped with a transformation strategy which extracts a fuzzy set representation of a high-dimensional ellipsoidal cluster. The fuzzy set merging strategy is incorporated because the projection of ellipsoids to one-dimensional space normally results in overlapping fuzzy sets. The parameter learning strategy is based on the extended recursive least square method [13], which appends a binary function to enhance the convergence and stability of the tuning process. The objective of the proposed method is to predict the future state of the slew bearing condition based on the vibration features on a timely basis. The Fuzzy-based method is selected as a prognosis method due to its capability to capture the nonlinear time-varying nature of the process [14]. Some recent literature has also proved that the fuzzy-based method is a potential method for prediction [15; 16; 17].