Sean Reed

A model for predicting and monitoring industrial system availability

This paper describes the integration of a sensor data stream monitoring system into a proposed functional product model capable of predicting functional availability. Such monitoring systems enable ...

Improved Efficiency in the Analysis of Phased Mission Systems With Multiple Failure Mode Components

Systems often operate in phased missions where their reliability structure varies over a set of consecutive time periods, known as phases. The reliability of a phased mission is defined as the probability that all phases in the mission are completed without failure. While the Binary Decision Diagram (BDD) method has been shown to be the most efficient solution for measuring the reliability of phased missions with non-repairable components with mutually exclusive failure modes, the existing BDD based methods are still unable to analyze large systems without considerable computational expense. This paper introduces a new BDD based method that is shown to provide improved efficiency and accuracy in the repeat analysis of this type of phased mission.

A reliability analysis method using binary decision diagrams in phased mission planning

The use of autonomous systems is becoming increasingly common in many fields. A significant example of this is the ambition to deploy unmanned aerial vehicles (UAVs) for both civil and military applications. In order for autonomous systems such as these to operate effectively, they must be capable of making decisions regarding the appropriate future course of their mission responding to changes in circumstance in as short a time as possible. The systems will typically perform phased missions and, owing to the uncertain nature of the environments in which the systems operate, the mission objectives may be subject to change at short notice. The ability to evaluate the different possible mission configurations is crucial in making the right decision about the mission tasks that should be performed in order to give the highest possible probability of mission success. Because binary decision diagrams (BDDs) may be quickly and accurately quantified to give measures of the system reliability it is anticipated that they are the most appropriate analysis tools to form the basis of a reliability-based prognostics methodology. The current paper presents a new BDD-based approach for phased mission analysis, which seeks to take advantage of the proven fast analysis characteristics of the BDD and enhance it in ways that are suited to the demands of a decision-making capability for autonomous systems. The BDD approach presented allows BDDs representing the failure causes in the different phases of a mission to be constructed quickly by treating component failures in different phases of the mission as separate variables. This allows flexibility when building mission phase failure BDDs because a global variable ordering scheme is not required. An alternative representation of component states in time intervals allows the dependencies to be efficiently dealt with during the quantification process. Nodes in the BDD can represent components with any number of failure modes or factors external to the system that could affect its behaviour, such as the weather. Path simplification rules and quantification rules are developed that allow the calculation of phase failure probabilities for this new BDD approach. The proposed method provides a phased mission analysis technique that allows the rapid construction of reliability models for phased missions and, with the use of BDDs, rapid quantification.

Modelling service support system reliability

Functional Products, where the customer pays for the function and availability of a product instead of the product itself, are increasingly popular in capital intensive industries such as aerospace. Such products are integrated systems involving the combination of hardware and service support systems. The reliability prediction and optimisation of the service system that supports the hardware availability is essential to the feasibility of the product. These systems consist of maintenance procedures and resource provisions. Simulation based techniques are presented in this paper to analyse the reliability of support systems and their application is demonstrated through a simple example.

Evaluating availability of functional products through simulation

Abstract A functional product is an integrated package consisting of hardware, software and a service support system that provides a customer with a certain function and is sold under a performance-based contract that includes a functional availability guarantee. For the availability performance, prediction, optimisation and management of risk are therefore important concerns during product development. This paper describes a software tool that can generate an integrated model of a functional product from its design details and analyse it through simulation to provide availability performance information. The model’s application to the analysis of a real industrial system is demonstrated. Such tools are important for the development and widespread adoption of functional products. The resulting analysis gave an indication of a suitable guaranteed functional availability level for the product and could be used to compare the performance of different design options.

Prediction of service support costs for functional products

Abstract In the functional product business model, a customer is provided with functionality at a guaranteed level of availability under a pre-agreed pricing structure whilst the provider retains ownership of the hardware and provides a service support system to deliver services such as maintenance. The ability to accurately predict the costs of supporting functional product contracts is crucial to the provider in correctly pricing those contracts and for the development and implementation of an efficient service support system. Since the costs incurred are performance based and accumulated over a long duration, they are difficult to predict without effective modelling and decision support tools. This paper discusses a decision support tool that has been developed to provide detailed analysis of the predicted long-term costs of supporting functional product contracts. The tool features a web based user interface to allow collaborative use of the tool by multiple users. The cost predictions are obtained through a discrete event simulation model that emulates the performance of the hardware and service support system used by the provider to fulfil the functional requirements specified in the contracts. The simulations are executed on automatically provisioned remote web based servers, meaning that the computing resources utilised are not limited by the user’s client device. The tool is demonstrated via application to an industrial test case.

Modelling and simulation of functional product system availability and support costs

Functional Products (FP), total offers or product service systems that comprise of both Hardware (HW) and Support Services (SS) sold as an integrated offering under an availability guarantee are becoming increasing popular in industry. This paper addresses, through modelling and simulation the challenge faced by suppliers in developing an integrated HW and SS design to produce an FP which meets contracted availability. A recently published framework specified how an integrated model hardware and service support system model could be used to obtain functional availability predictions and perform simulation driven functional product development. This paper presents the first example of an integrated functional product model. It uses fault tree, Petri net and discrete event simulation techniques to enable the prediction of functional product availability and support costs. Such predictions are used here to evaluate and compare different service support system designs.

An efficient algorithm for exact computation of system and survival signatures using binary decision diagrams

System and survival signatures are important and popular tools for studying and analysing the reliability of systems. However, it is difficult to compute these signatures for systems with complex reliability structure functions and large numbers of components. This paper presents a new algorithm that is able to compute exact signatures for systems that are far more complex than is feasible using existing approaches. This is based on the use of reduced order binary decision diagrams (ROBDDs), multidimensional arrays and the dynamic programming paradigm. Results comparing the computational efficiency of deriving signatures for some example systems (including complex benchmark systems from the literature) using the new algorithm and a comparison enumerative algorithm are presented and demonstrate a significant reduction in computation time and improvement in scalability with increasing system complexity.

Simulation driven design of functional products: a tool for evaluation of hardware reliability and maintenance

Functional products, consisting of hardware and support services, may be leased through performance-based contracts that provide a guarantee of functional availability at fixed costs. This transfers risk from the product supplier to the customer compared to hardware only sales. During functional product development, the supplier must make design decisions that influence both hardware reliability and support service performance. A criterion for comparing design choices that accounts for supplier risk aversion is described, along with a simulation tool that can predict the performance of a product in development. Together these can be used to drive the product design. They are applied to analyse an example functional product and determine optimal design choice for the implementation of measures to improve the reliability of maintenance tasks. The analysis demonstrates the use of the criterion and simulation tool for evaluating design choices during product development. It also shows the possible influence of supplier risk aversion on the optimal design.

Maintenance processes modelling and optimisation

A Maintenance Procedure is conducted in order to prevent the failure of a system or to restore the functionality of a failed system. Such a procedure consists of a series of tasks, each of which has a distribution of times to complete and a probability of being performed incorrectly. The inclusion of tests can be used to identify any maintenance errors which have occurred. When an error is identified it can be addressed through a corresponding correction sequence which will have associated costs and add to the maintenance process completion time. A modified FMEA approach has been used to identify the possible tests. By incorporating any selection of tests into the maintenance process it can then analysed using a discrete-event simulation to predict the expected completion time distribution. The choice of tests to perform and when to do them is then made to successfully complete the maintenance objective in the shortest possible time using a genetic algorithm. The methodology is demonstrated by applying it to the repair process for a car braking system. The developed method is suitable for application in abroad range of industries.