mohammadali saniee monfared

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.

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting process. This paper generalizes the SES method into a new adaptive method called revised simple exponential smoothing (RSES), as an alternative method to recognize non-stationary level shifts in the time series. We show that the new method improves the accuracy of the forecasting process. This is done by controlling the number of observations and the smoothing parameter in an adaptive approach, and in accordance with the laws of statistical control limits and the Bayes rule of conditioning. We use a numerical example to show how the new RSES method outperforms its traditional counterpart, SES.

As governmental subsidies to universities are declining in recent years, sustaining excellence in academic performance and more efficient use of resources have become important issues for university stakeholders. To assess the academic performances and the utilization of the resources, two important issues need to be addressed, i.e., a capable methodology and a set of good performance indicators as we consider in this paper. In this paper, we propose a set of performance indicators to enable efficiency analysis of academic activities and apply a novel network DEA structure to account for subfunctional efficiencies such as teaching quality, research productivity, as well as the overall efficiency. We tested our approach on the efficiency analysis of academic colleges at Alzahra University in Iran.