Establishing a Real-Time Multi-Step Ahead Forecasting Model of Unbalance Fault in a Rotor-Bearing System

Recently, prognostics and health management (PHM) has garnered a lot of attention in the industrial sector for its cost-effective maintenance and safe operation of the system. In this regard, vibration-based predictive maintenance using sensors plays a significant role in the diagnosis and prognosis of various faults. The need of the hour is to know when and which part must be replaced in advance for efficient and reliable operation. Unbalance is one major fault acting on any rotary system leading to excessive vibration and causing various other faults developing early failure in components directly or indirectly. In this paper, we show how a prognostic model can be built for the identification of future unbalance trend of a rotor-bearing system with the aid of a mathematical model of the system and statistical/machine learning methods. The prognostic model developed is used to forecast the unbalance time-series data of an industrial turbine rotor in real-time which forecasts the month ahead unbalance values. The proposed model is verified for prognostic analysis using datasets from a local plastic company. After careful examination of the results, it is concluded that the proposed model can aid in precisely detecting future system unbalance.

Automated summary

Recently, prognostics and health management (PHM) has gained attention in industry for cost-effective maintenance and safe operation. Vibration-based predictive maintenance is crucial for diagnosing and predicting faults. This study presents a prognostic model using a mathematical model and statistical/machine learning methods to forecast future unbalance trend in a rotor-bearing system. The model is used to predict real-time unbalance values of an industrial turbine rotor for the next month. The proposed model is validated using data from a local plastic company and proves to accurately detect future system unbalance.