Reliability Evaluation and Prediction Method with Small Samples

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities

A Data-Driven Reliability Estimation Approach for Phased-Mission Systems

We attempt to address the issues associated with reliability estimation for phased-mission systems (PMS) and present a novel data-driven approach to achieve reliability estimation for PMS using the condition monitoring information and degradation data of such system under dynamic operating scenario. In this sense, this paper differs from the existing methods only considering the static scenario without using the real-time information, which aims to estimate the reliability for a population but not for an individual. In the presented approach, to establish a linkage between the historical data and real-time information of the individual PMS, we adopt a stochastic filtering model to model the phase duration and obtain the updated estimation of the mission time by Bayesian law at each phase. At the meanwhile, the lifetime of PMS is estimated from degradation data, which are modeled by an adaptive Brownian motion. As such, the mission reliability can be real time obtained through the estimated distribution of the mission time in conjunction with the estimated lifetime distribution. We demonstrate the usefulness of the developed approach via a numerical example

A fast reliability analysis for an unmanned aerial vehicle performing a phased mission

It is becoming more common for Unmanned Aerial Vehicle (UAV) to perform phased mission where the phase's causes of failure may be different. The reliabilities of the phases are required throughout the mission in order to make future decisions for the mission. However, previous research of phased mission analysis has shown it to be very complex and take significantly long amounts of time. Also the analysis cannot be performed before the mission because information that is only available; when the mission is active is required for the analysis. The aim of this research is develop new methods for a phased mission analysis which can obtain the phases reliabilities on a real structure UAV mission, where all the components are non-repairable, in the fastest time as possible. The present methods are explored and the outcome is that the methods based on Binary Decision Diagram (BOO) analysis are the most efficient. Therefore the BOO analysis is use as a starting point for the new method. The phase mission BOO based methods are improved by altering the procedure of the analysis. Also modules that can appear in many phases can be taken out to simplify the analysis. Search methods that lookup computations that have already been done before are investigated to determine how much impact it has on the speed of the analysis. A method that restructures the phase's mission fault trees to optimize the number of modules that can be taken out is developed. It is tested on a real UAV mission and it is shown to significantly simplify the analysis. This method is extended by situation where a mission is being reconfigured several times throughout a mission and the analysis also has to be done several times. Additional changes are made by using part of the analysis of the original mission for the new one to speed up the analysis. A method is developed which identifies parts of the analysis referred to as groups which can treated as a mini phase missions. Each group can be performed on separate processor in parallel that reduces the online analysis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Systems reliability modelling for phased missions with maintenance-free operating periods

In 1996, a concept was proposed by the UK Ministry of Defence with the intention of making the field of reliability more useful to the end user, particularly within the field of military aerospace. This idea was the Maintenance Free Operating Period (MFOP), a duration of time in which the overall system can complete all of its required missions without the need to undergo emergency repairs or maintenance, with a defined probability of success. The system can encounter component or subsystem failures, but these must be carried with no effect to the overall mission, until such time as repair takes place. It is thought that advanced technologies such as redundant systems, prognostics and diagnostics will play a major role in the successful use of MFOP in practical applications. Many types of system operate missions that are made up of several sequential phases. For a mission to be successful, the system must satisfactorily complete each of the objectives in each of the phases. If the system fails or cannot complete its goals in any one phase, the mission has failed. Each phase will require the system to use different items, and so the failure logic changes from phase to phase. Mission unreliability is defined as the probability that the system fails to function successfully during at least one phase of the mission. An important problem is the efficient calculation of the value of mission unreliability. This thesis investigates the creation of a modelling method to consider as many features of systems undergoing both MFOPs and phased missions as possible. This uses Petri nets, a type of digraph allowing storage and transit of tokens which represent system states. A simple model is presented, following which, a more complex model is developed and explained, encompassing those ideas which are believed to be important in delivering a long MFOP with a high degree of confidence. A demonstration of the process by which the modelling method could be used to improve the reliability performance of a large system is then shown. The complex model is employed in the form of a Monte-Carlo simulation program, which is applied to a life-size system such as may be encountered in the real world. Improvements are suggested and results from their implementation analysed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo