Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

This paper presents a methodology to calculate the Remaining Useful Life (RUL) of machinery equipment by utilising physics-based simulation models and Digital Twin concept, in order to enable predi...

https://www.tandfonline.com/doi/pdf/10.1080/0951192X.2019.1686173

The use of Digital Twin for predictive maintenance in manufacturing

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality cont...

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Availability and maintenance modelling for systems subject to multiple failure modes

An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools

Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence

The use of multi-sensor information fusion techniques is essential for condition monitoring and prediction in large complex systems. In this paper, a new distributed model fusion method is proposed to predict the remaining useful life (RUL) of a nonlinear Wiener process. First, the state–space model of the nonlinear Wiener process is established, based on multi-sensor monitoring, and the distributed Kalman filtering algorithm is used to filter and fuse the measurement data received from multiple sensors. Next, the parameters and degradation states of the state–space model are estimated and updated online in real time using the expectation maximum and smoothing filter algorithms. Moreover, the distribution of the system’s RUL is obtained according to the estimated state–space model considering the random failure threshold factor. Finally, numerical experiments are conducted to elucidate the accuracy of the adopted distributed fusion method, and the adaptability and effectiveness of the proposed method are verified using the FD001 data of the C-MPASS dataset as an example.

Multi-sensor information fusion-based prediction of remaining useful life of nonlinear Wiener process

Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence

Remaining Useful Life Prediction for Multiple Degradation Indicators Systems Considering Stochastic Correlation

Since the degradation process of fan slewing bearings is easily influenced by the external environment, it is difficult to estimate its remaining useful life (RUL) accurately. A nonlinear Wiener degradation model considering the influence of random covariate is established for the prediction of the RUL of fan slewing bearings in this study. Firstly, considering the nonlinear and nonmonotonic properties of the operation process of fan slewing bearings, the degradation model of the nonlinear Wiener process of fan slewing bearings is established. Secondly, the combination of random covariate models and the nonlinear Wiener degradation process is researched. The stress effect which is used as a random covariate is introduced into the nonlinear Wiener degradation model in the form of the additive hazard model. Moreover, the closed expression for the RUL probability density function(PDF) is derived for the random variation of drift coefficients, the individual differences and the random variation of covariates. Thirdly, the maximum likelihood estimation algorithm is used to estimate the RUL parameters depending on the historical degradation data. Finally, the vibration data of fan slewing bearings monitored by sensors are used to verify the effectiveness of the proposed method. The results show that the proposed method can be used to improve the fitting degree of the model and the accuracy of RUL estimation.

Hui Shi

Papers

Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence

Multi-sensor information fusion-based prediction of remaining useful life of nonlinear Wiener process

Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence

Remaining Useful Life Prediction for Multiple Degradation Indicators Systems Considering Stochastic Correlation

Remaining Useful Life Estimation of Fan Slewing Bearings in Nonlinear Wiener Process with Random Covariate Effect