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.

Highlights

  • Prognosis approaches are typically applied to predict the lifetime of rotating components, which can generally be divided into two stages

  • The direct mode prediction aims to study the correlation among input features, where eight features are used as the input attributes to predict the trend of input attributes

  • Anumber numberofofliterature literature reviews on prognosis methods have conducted been conducted in few the decades last few and each and review paper has paper presented own terminology of prognosisofapproaches

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Summary

Introduction

Prognosis approaches are typically applied to predict the lifetime of rotating components, which can generally be divided into two stages. 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 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 proposed method can be potentially applied to other time-series predictions, such as stocks prediction, climate change prediction, and tool life prediction These examples of applications usually have a huge number of historical datasets, which are difficult to model by typical regression methods and time-series prediction methods. Less historical datasets will reduce the prediction accuracy because the model is constructed based on minimum information from the historical datasets

Classification of Prognosis Approaches
Method or Algorithm
Slew Bearing Test-Rig and Data Acquisition
Vibration
Feature
Feature Extraction
It can behistogram seen fromlower
Rule Growing Mechanism of PANFIS
Rule Pruning Scenario of PANFIS
Fuzzy Set Extraction and Merging Scenario
Adaptation of Rule Consequent
Time-Series Feature Prediction
Direct Mode Prediction
Time-Series Mode Prediction
60 Days 80
Conclusions
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