This article addresses a critical gap in the field of fault diagnosis for complex systems, focusing on the development and application of an ontology-based approach to capture and utilize expert knowledge. The key objective is to enhance fault diagnosis precision and effectiveness, specifically in challenging No-Fault-Found (NFF) scenarios, by harnessing the extensive, often implicit, understanding of seasoned professionals. The study uses a comprehensive methodology that includes creating a specialized ontology called DIAGONT, which captures the expert reasoning in fault diagnosis. Field experts contribute to the development of this ontology, ensuring its relevance and applicability. Real-world case studies and controlled experiments are used to rigorously validate the ontology. The goal of these experiments is to evaluate how effective the ontology is in enhancing fault diagnosis procedures when compared to traditional methods. Our case studies focused on two complex engineering assets, a loading arm and a helicopter mission system, due to their complexity and the frequency of non-functional failure scenarios. The analysis shows that using the DIAGONT ontology leads to improved accuracy and efficiency in fault diagnosis. A structured format allowed experts to successfully capture and reuse diagnostic knowledge, resulting in a noticeable reduction in NFF scenarios. The application of ontology-based approach exhibited potential in enhancing knowledge transfer between experts and less experienced technicians, potentially resulting in long-lasting improvements in maintenance practices. The results highlight how ontology-based systems can improve fault diagnosis in complex engineering systems.

Contracting for Availability (CfA) is an increasingly adopted commercial process which consists of a partnership between customer and solution provider(s) towards supporting system availability for long periods with the target of better value for money. In a competitive bidding process, contractors commit an effective bid price to win contracts, which the process heavily depends on existing data from similar projects. Within the CfA context, based on industrial engagement and literature review, it has been realised that one of the biggest challenges is to deal with a lack of available data to aid the cost and asset availability estimation process. In order to fill this gap, this paper presents an innovative simulation model for Cost and Availability Trade-Off and Estimation in CfA Bids (CATECAB), which uses multiple regression analysis and a mixed Monte-Carlo and bootstrapping re-sampling technique. Its main innovation is the ability to produce estimates based on a comprehensive analysis across the different attributes that impact system availability, and in scenarios where data availability is limited. A case study is presented with four CfA scenarios, provided by a major defence contractor in the UK, which showed that additional investment could improve availability. Experienced cost engineers validated the results and acknowledged that it is a valuable contribution to improve cost and availability estimation during bidding.

Contracting for Availability (CfA) is an increasingly adopted commercial process which consists of a partnership between customer and solution provider(s) towards supporting system availability for long periods with the target of better value for money. In a competitive bidding process, contractors commit an effective bid price to win contracts, which the process heavily depends on existing data from similar projects. Within the CfA context, based on industrial engagement and literature review, it has been realised that one of the biggest challenges is to deal with a lack of available data to aid the cost and asset availability estimation process. In order to fill this gap, this paper presents an innovative simulation model for Cost and Availability Trade-Off and Estimation in CfA Bids (CATECAB), which uses multiple regression analysis and a mixed Monte-Carlo and bootstrapping re-sampling technique. Its main innovation is the ability to produce estimates based on a comprehensive analysis across the different attributes that impact system availability, and in scenarios where data availability is limited. A case study is presented with four CfA scenarios, provided by a major defence contractor in the UK, which showed that additional investment could improve availability. Experienced cost engineers validated the results and acknowledged that it is a valuable contribution to improve cost and availability estimation during bidding.

The article is concerned with competitive cost and availability estimates of complex engineering systems at the bidding stage of availability type support services contracts (CfA). Such estimates are important to calculate the best price that will win the contract in a competitive bidding scenario while ensuring that the contract can be carried out profitably. An enhanced genetic algorithm is applied to multivariable functions of cost, availability and time, which is implemented in a Cost Availability and Time Optimisation simulation model (CATION) that builds automated and fast estimates. The model considers a multi-attribute performance impact scenario and calculates the optimal investment in each attribute to achieve total contract cost and system availability targets, for the certain contract duration. Twelve CfA are investigated, in the aerospace and marine domains, under cost and availability targets, for validating the results. Conclusions from these case studies indicate that different monetary allocations can have an impact on the performance of the system, and optimisation of this allocation can assure that targets are met at adequate costs.

With the growth of the product-service system (PSS) in recent years, how to better manage the service data to improve the informed decision-making capability has become an on-going aim among the Through-life Engineering Services (TES) firms. This scenario has led managers, more and more, to turn their attention to the quality of data and information created, gathered and used within the company. Encouraged by this background, a service data quality framework has been developed aiming to provide companies with a set of methods and tools to prioritise relevant service data and assess its quality levels. The process involves four main steps that go through: (1) Mapping out important data for Through-life Support available within the company and its internal and external flows; (2) Application of a multiple criteria decision-making technique to prioritise the relevant data set considering its costs for being collected and maintained, business impact, frequency of use and ease of obtainability; (3) Quality assessment of the prioritised dataset, based on a capability maturity model; (4) Defining strategies to address data quality issues. Validation on an industrial case study demonstrates potential benefits of the process and further work opportunities.

This research investigates through a systems approach, “Additive Manufacturing” (AM) applications in “Defence Support Services” (DS2). AM technology is gaining increasing interest by DS2 providers, given its ability of rapid, delocalised and flexible manufacturing. From a literature review and interviews with industrial and academic experts, it is apparent that there is a lack of research on AM applications in DS2. This paper’s contribution is represented by the following which has been validated extensively by industrial and academic experts: (1) DS2 current practices conceptual models, (2) a framework for AM implementation and (3) preliminary results of a next generation DS2 based on AM. To carry out the research, a Soft System Methodology was adopted. Results from the research increased the confidence of the disruptive potential of AM within the DS2 context. The main benefits outlined are (1) an increased support to the availability given a reduced response time, (2) reduced supply chain complexity given only supplies of raw materials such as powder and wire, (3) reduced platform inventory levels, providing more space and (4) reduced delivery time of the component as the AM can be located near to the point of use. Nevertheless, more research has to be carried out to quantify the benefits outlined. This requirement provides the basis for the future research work which consists in developing a software tool (based on the framework) for experimentation purpose which is able to dynamically simulate different scenarios and outline data on availability, cost and time of service delivered.

The defence context more recently has been experiencing a significant shift towards servitization. As competition has increased, commercial strategies are increasingly moving towards providing through-life solutions for complex engineering products such as submarines. Within such a context value for money is an essential driver in a life cycle sense for selecting a bid. The defence sector has largely been affected by this change in the business environment. Industrial Product Service System (IPS2) is a model of providing services that satisfy industrial customers and aims to reduce lifecycle impacts of products and services through product servicing, remanufacturing and recycling. This approach has proved to be an effective solution to enhance the services support in military projects. IPS2 offers client value by responding more efficiently to the client demands with reduced prices; it is delivered in the form of contracting approaches between Ministry of Defence (MoD) and industry; these contracts can differ in several aspects as risk sharing, application level, ownership policy and supportability specifications vary. This research focuses on Contracting for Availability (CfA), which is a particular approach of IPS2.The paper aims to present the review of literature in designing support strategies for CfA, identifying the good practices and challenges, and to propose a systematic approach to fill the industrial and academic gap towards an optimization of the current modelling process. This work starts by presenting a literature review in IPS2; it then moves into the optimization processes, describing how contractors currently design a long term service support contract in the military context with better value for money and high level of system readiness. The key cost and performance drivers are identified and a framework is presented to enhance the design process of CfA. The methodology of the paper relies on literature. This research aims to extend the work of several authors in predicting the cost of services in the military contracts.

Designing military support is challenging and current practices need to be reviewed and improved. This paper gives an overview of the Industry current practices in designing military support under Ministry of Defence/Industry agreements (in particular for Contracting for Availability (CfA)), and identifies challenges and opportunities for improvement. E.g. training delivery was identified as an important opportunity for improving the CfA in-service phase. Thus, an innovative conceptual framework is presented to assess the impact of training on the equipment availability and cost. Additionally, guidelines for improving the current training delivery strategies are presented, which can also be applied to other Industry contexts.