This paper presents an Artificial Neural Network (ANN) model for failure prediction of critical electronic systems in power plants. Reliability modeling of electronic circuits can be best performed by the stressor – susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor v...

High performance computing systems can have high failure rates as they feature a large number of servers and components with intensive workload. The availability of the system can be easily compromised if the failure of these subsystems is not handled correctly. To ensure an availability of the computing resources, there is a need for an effective fault management framework. This research propo...

This work proposes a method for prioritising critical water mains (CWMs) with high risk, in terms of both failure probability and consequence costs. The proposed method can improve the prioritisation of high-risk CWMs requiring condition assessment in the near future and will further optimise the operational maintenance of water utilities. Due to the improved prioritisation, water utilities can...

Failure prediction methods are becoming sine qua non conditions for effective availability enhancement in complex computer and communication systems. Therefore, there is a growing need for validation, benchmarking and assessment of such methods on real industrial data. Our thesis is that the effectiveness of such methods can be significantly enhanced when combined with fault injection. Then, no...

Resource virtualization has become one of the key super-power mobile computing architecture technologies. As mobile devices and multimedia traffic have increased dramatically, the load on mobile cloud computing systems has become heavier. Under such conditions, mobile cloud system reliability becomes a challenging task. In this paper, we propose a new model using a naive Bayes classifier for hy...

This paper reports a neural network-based methodology for failure probability prediction and quality enhancement of microengine MEMS using attribute data derived from actual measurements on microengines. A backpropagation neural network was employed for failure probability prediction. Microengine attributes constituted the inputs while time-tofailure statistics (mean, median and shape parameter...

Making predictions about future rewards is an important ability for primates, and its neurophysiological mechanisms have been studied extensively. One important approach is to identify neural systems that process errors related to reward prediction (i.e., areas that register the occurrence of unpredicted rewards and the failure of expected rewards). In monkeys that have learned to predict appet...

Tracking degradation of mechanical components is very critical for effective maintenance decision making. Remaining useful life (RUL) estimation is a widely used form of degradation prediction. RUL prediction methods when enough run-to-failure condition monitoring data can be used have been fully researched, but for some high reliability components, it is very difficult to collect run-to-failur...

Keywords: Definition of financial distress Sampling methods Featuring methods Review Financial distress prediction Corporate failure prediction Case-based reasoning Ensemble Group decision-making Support vector machine Hybrid modeling Neural network Decision tree Logistic regression Multiple discriminant analysis a b s t r a c t As a hot topic, financial distress prediction (FDP), or called as ...

1247 June 2013 C LINICAL research in Anesthesiology increasingly focuses on prediction of myriad perioperative complications. This research has been driven by development of databases that capture various measures of patient health, anesthetic care, surgical procedure, and outcomes. One of the strengths of this approach is that it allows analysis of outcomes that were previously understudied du...