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Engineering systems are growing in complexity, requiring increasingly intelligent and flexible methods to account for and predict uncertainties in service. This paper presents a framework for dynamic uncertainty prediction under limited data (UPLD). Spatial geometry is incorporated with LSTM networks to enable real-time multistep prediction of quantitative and qualitative uncertainty over time. Validation is achieved through two case studies. Results demonstrate robust prediction of trends in limited and dynamic uncertainty data with parallel determination of geometric symmetry at each time unit. Future work is recommended to explore alternative network architectures suited to limited data scenarios. Engineering and Physical Sciences Research Council (EPSRC): 1944319

Lack of methods on standardising the prioritisation of technologies within the context of Through-life Engineering Services (TES) has been identified. Inspired by the TES value streams and support activity assets, existing within a common TES framework, a new design framework for technologies prioritisation is proposed. A dynamic toolkit to identify the most suitable technology is also developed, using a Quality Function Deployment method, Analytic Hierarchy Process and ROI analysis. A real case study from the defence sector is employed to validate the developed design framework and toolkit; the results show a well-structured guide that can effectively simplify the decision-making process.

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study.\ud \ud The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle.

Engineering systems must function effectively whilst maintaining reliability in service. Predicting maintenance costs and asset availability raises varying degrees of uncertainty from multiple sources. Previous reviews in this domain have assessed cost uncertainty and estimation for the entire life cycle. This paper presents a systematic review to investigate existing methodologies and challenges in uncertainty quantification, aggregation and forecasting for modern engineering systems through their in-service life. Approaches to forecast uncertainty here are hindered chiefly by data quality of available data, experience and knowledge. A total of 107 papers were analysed to answer three research questions based on the scope, through which two core research gaps were identified. An integrated combination of identified approaches will enhance rigour in uncertainty assessment and forecasting. This review contributes a systematic identification and assessment of current practices in uncertainty quantification and scientific methodologies to quantify, aggregate and forecast quantitative and qualitative uncertainties to better understand their impact on cost and availability to aid decision making throughout the in-service phase.

Maintenance procedures for complex engineering systems are increasingly determined by predictive algorithms based on historic data, experience and knowledge. Such data and knowledge is accompanied by varying degrees of uncertainty which impact equipment availability, turnaround time and unforeseen costs throughout the system life cycle. Once quantified, these uncertainties call for robust forecasting to facilitate dependable maintenance costing and ensure equipment availability. This paper builds on the theory of spatial geometry as a methodology to forecast uncertainty where available data is insufficient for the application of traditional statistical analysis. To ensure continuous forecast accuracy, a conceptual dynamic multistep prediction model is presented applying spatial geometry with long-short term memory (LSTM) neural networks. Based in MATLAB, this deep learning model predicts uncertainty for the in-service life of a given system. The further into the future the model predicts, the lower the confidence in the uncertainty prediction. Forecasts are therefore also made for a single time step ahead. When this single step is reached in real time, the next step is forecast and used to update the long range prediction. The uncertainty here is contributed by an aggregation of quantitative data and qualitative, subjective expert opinions and additional traits such as environmental conditions. It is therefore beneficial to indicate which of these factors prompts the greatest impact on the aggregated uncertainty for each forecast point. Future work will include the option to simulate and interpolate input data to enhance the accuracy of the LSTM and explore suitable approaches to mitigate, tolerate or exploit uncertainty through deep learning.

The exponential increase in technological complexity of modern engineering systems necessitates rigorous and accurate maintenance planning to determine optimum equipment availability and turnaround time whilst allowing for overruns and unforeseen costs. Quality and availability of quantitative data, as well as qualitative expert opinion and experience expose uncertainties that can result in under or over estimation of the above factors. Uncertainty quantification in complex engineering systems should consider inter-connected components and associated processes from a combination of quantitative and qualitative (compound) perspectives. This paper presents a framework to quantify and aggregate compound uncertainties and to be assessed against a predetermined acceptable level of uncertainty. This will provide maintenance planners with a confident, comprehensive view of parameters surrounding the above factors to improve decision making capabilities. The framework was validated by assessing individual and compound uncertainties in a bespoke heat exchanger test rig comprised of subsystem modules interact in a non-linear manner, as well as subjective opinions and actions of operators. The results demonstrate the framework’s ability to effectively quantify these factors with an indication of their impact on the system. Future work will include further validation with more complex case studies and development of methods to forecast the quantified uncertainty through the in-service phase of an asset’s life cycle.

Information asymmetry (IA) in terms of contextual variety and importance is one of the most challenging aspects of through-life costing in product-service systems (PSS). IA is an imbalance in the information, data and knowledge shared among the parties involved in a contractual agreement. In manufacturing systems under PSS, interaction and effective communication among several parties who are involved in a contractual agreement, rely on the continuity and accuracy of information and context. In such systems, contextual variety exhibits complexity and uncertainty in through-life costing and subsequently in PSS cost assessment. Although the economic aspect of PSS has been studied previously, the impact of IA on through-life cost and for different PSS solutions has not been detailed. Considering manufacturing value chains, this paper introduces a new concept of PSS-hierarchy to perform through-life costing in the presence of IA for various PSS solutions. Moreover, this paper proposes a generic life-cycle model for different PSS solutions to assess the total cost of ownership (TCO). The proposed model has been developed to support decisions on contract design in manufacturing systems. This study considers the manufacturer, service provider and customer perspectives to develop the TCO model using a machine tool manufacturing case study.

Complex engineering systems present a wealth of uncertainties concerning aspects ranging from performance measurements to maintainability and through-life characteristics. A quantifiable understanding of these uncertainties is vital to system optimisation and plays a key role in decision-making processes for manufacturing organisations worldwide; impacting profit, product availability and manufacturing efficiency. The aim of this paper is to examine challenges and complications that arise when quantifying uncertainties in complex engineering systems that rely on expert opinion. A thermographic inspection system is utilised as a use case. Contractor-client and supervisor-maintainer relationships are examined. Key challenges highlighted involve accurate depiction of error margins and corresponding uncertainties of components where data is only heuristically obtainable, as well as the influence of environmental conditions and skill of the maintainer.