mahdiyeh kalaei

Remaining useful life (RUL) prediction is crucial in prognostics and health management (PHM) systems. The primary objective is to forecast the time to failure (TTF) or anticipate the RUL of a system. In real industrial cases, systems typically consist of multiple components that can affect each other, and ignoring these dependencies when modeling PHM systems can lead to erroneous RUL predictions and ineffective maintenance planning. Recognizing this, the focus of this paper is on the prognostics of multi-component systems, where the degradation processes of the system are influenced by both internal factors specific to the components and external factors related to the environment.

Today, the electricity power system is the most complicated engineering system has ever been made. The integrated power generating stations with power transmission lines has created a network, called complex power network. The reliability estimation of such complex power networks is a very challenging problem, as one cannot find any immediate solution methods in current literature. In this paper, we advanced a new method for estimating the reliability of such networks, which is based on 1) decomposition of the whole network into sub-networks called islands, 2) estimating each island’s reliability in exact form using the network reliability theory, and 3) assembling the islands back together to estimate the whole network reliability, again in exact form. We applied the new method on Iran’s power network with 105 generation stations and 16460 kilometres of transmission lines.

Today, the electricity power system is the most complicated engineering system has ever been made. The integrated power generating stations with power transmission lines has created a network, called complex power network. The reliability estimation of such complex power networks is a very challenging problem, as one cannot find any immediate solution methods in current literature. In this paper, we advanced a new method for estimating the reliability of such networks, which is based on 1) decomposition of the whole network into sub-networks called islands, 2) estimating each island’s reliability in exact form using the network reliability theory, and 3) assembling the islands back together to estimate the whole network reliability, again in exact form. We applied the new method on Iran’s power network with 105 generation stations and 16460 kilometres of transmission lines.

In most modern manufacturing systems, products are often the output of some multistage processes. In these processes, the stages are dependent on each other, where the output quality of each stage depends also on the output quality of the previous stages. This property is called the cascade property. Although there are many studies in multistage process monitoring, there are fewer works on profile monitoring in multistage processes, especially on the variability monitoring of a multistage profile in Phase-I for which no research is found in the literature. In this paper, a new methodology is proposed to monitor the standard deviation involved in a simple linear profile designed in Phase I to monitor multistage processes with the cascade property. To this aim, an autoregressive correlation model between the stages is considered first. Then, the effect of the cascade property on the performances of three types of T 2 control charts in Phase I with shifts in standard deviation is investigated. As we show that this effect is significant, a U statistic is next used to remove the cascade effect, based on which the investigated control charts are modified. Simulation studies reveal good performances of the modified control charts.