Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA-CAPF Framework

Zhiwei Sun ,Xiong Hu,Kai Dong

doi:10.1155/2021/9403401

Abstract

The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. The proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. The prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.

Highlights

As special equipment, quay crane (QC) is the most frequently used equipment in the area of port container transportation. e health condition of the hoist gearbox determines the working efficiency and production safety of QCs
On the basis of this, the method proposed in this paper divides the remaining useful life (RUL) prediction procedures of bearing into two parts: first, the load is predicted, and different operating conditions are classified by load discretization result
According to the commonly used “light-medium-heavy” classification pattern of port load, we set k 3. It can be seen from the smoothed vibration energy spectrum shown in Figure 7 that the bearing degradation can be divided into three stages and RUL prediction will start from the failure stage. e threshold is predetermined as the RMS value when the port maintenance personnel detect abnormal noises

Summary

Introduction

QC is the most frequently used equipment in the area of port container transportation. e health condition of the hoist gearbox determines the working efficiency and production safety of QCs. In order to study the impacts of dynamic conditions on RUL prediction, we try to establish the life prediction model under no-predetermined dynamic operating conditions. On the basis of this, the method proposed in this paper divides the RUL prediction procedures of bearing into two parts: first, the load is predicted, and different operating conditions are classified by load discretization result. The system state is updated, and the RUL of the bearing is predicted based on the prediction results of load and corresponding operating conditions. E particle filter algorithm improved by a condition activation vector is applied to predict the system state and update the degradation rates and signal jump coefficients so as to realize the RUL prediction. (1) A framework of RUL prediction based on the ARIMA model and CAPF method under off-design operating conditions is proposed.

Theory Background

RUL Prediction Method Based on ARIMA and CAPF

Case Study

RUL Prediction by ARIMA-CAPF Framework

25 First predicting point

Method