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American Society for Testing and Materials

Rotating machinery prognostics: State of the art, challenges and opportunities

An overview of time-based and condition-based maintenance in industrial application

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation...

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1472&context=lkcsb_research

Residual-life distributions from component degradation signals: A Bayesian approach

A review on condition-based maintenance optimization models for stochastically deteriorating system

The purpose of this paper is to describe an approach to real-time reliability prediction, applicable to an individual product unit, operating under dynamic conditions. The concept of conditional reliability estimation is extended to real-time applications using time-series analysis techniques to bridge the gap between physical measurement and reliability prediction. The model is based on empirical measurements, self-generating, and applicable to online applications. This approach has been demonstrated to the prototype level. Physical performance is measured and forecast across time to estimate reliability. Time-series analysis is adapted to forecast performance. Exponential smoothing with a linear level and trend adaptation is applied. This procedure is computationally recursive and provides short-term, real-time performance forecasts which are linked directly to conditional reliability estimates. Failure clues must be present in the physical signals, and failure must be defined in terms of physical measures to accomplish this linkage. On-line, real-time applications of performance reliability prediction are useful in operation control as well as predictive maintenance.

Real-time performance reliability prediction

Multivariate performance reliability prediction in real-time

This paper addresses a predictive condition-based maintenance approach based on monitoring, modeling, and predicting a system's deterioration. The system's deterioration is considered as a stochastic dynamic process with continuous degrading. Structural time series, coupled with state-space modeling and Kalman filtering methods, is adopted for recursively modeling and forecasting the deterioration state at a future time. The probability of a failure is then predicted based on the forecasted deterioration state and a threshold of a failure. Finally, maintenance decisions are made according to the predicted failure probabilities, associated preventive and corrective maintenance cost, and the profit loss due to system performance deterioration. The approach can be applied on-line to provide economic and preventive maintenance solutions in order to maximize the profit of the ownership of a system. Copyright © 2007 John Wiley & Sons, Ltd.

Predictive condition-based maintenance for continuously deteriorating systems

Optimization of virotherapy for cancer.

The inherent problems associated with laser cladding remanufacturing process using NiCrBSi alloy powder are the formation of crack and porosity, which can be detrimental to the mechanical properties of the deposited material. In this paper, a comprehensive investigation of crack and porosity is carried out using Ni60A alloy. It is shown that the linear energy density can be used to identify a threshold for the elimination of large porosity. And statistical analysis is used to optimize process parameters leading to minimum porosity area, and the result shows that powder feed rate in the coating process is the most significant parameter considering the porosity area. The crack can be effectively eliminated by the combination of preheating up to 300 °C and placing insulated plank under the substrate. In this situation, one large-area crack-free and low porosity rate (0.027%) coating is obtained successfully and its average microhardness is about 2.7 times as much as that of the substrate. The other large-area crack-free coating with low porosity rate (0.01%) is prepared successfully without preheating, but its microhardness descends intensely because of high dilution of the coating.

Huitian Lu

Papers

Real-time performance reliability prediction

Multivariate performance reliability prediction in real-time

Predictive condition-based maintenance for continuously deteriorating systems

Optimization of virotherapy for cancer.

Process optimization of laser cladding Ni60A alloy coating in remanufacturing