Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

Abstract

Featured Application: The proposed method could potentially be applied in condition-based maintenance and prognosis of rotating machineries to predict the incipient and final failure.Abstract: In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods. 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 ...

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