In the era of big data, the understanding of the complex and uncertain nature of reliability growth data has deepened. Beyond the well-known characteristic that failure data are random variables following specific probability distributions, expert judgments expressed linguistically constitute fuzzy data. Allowable values for critical parameters are often confined to specific ranges, representing typical grey data. Moreover, knowledge regarding specific components, materials and processes frequently manifests as rough data. Effectively utilizing reliability growth data characterized by multiple uncertainties – randomness, fuzziness, greyness and roughness – is therefore key to solving the modeling challenges for reliability growth of high-end intelligent equipment. This paper proposes a novel model and associated new concepts for uncertainty representation and integration to address this gap.
Guided by the core principles of big data – which emphasize utilizing all available data beyond random sampling, eliminating confounding factors to discern general trends and prioritizing correlation over strict causality – this research adopts a “full data utilization” perspective. It begins with the collection, identification, and analysis of reliability growth data. Through an in-depth examination of the characteristics and commonalities of data embodying various uncertainties (random, fuzzy, grey and rough), the concept of a standard uncertainty number is defined. The representation of SUNs, conversion rules for transforming diverse uncertainty data into SUNs and a comprehensive operational framework for SUNs are developed. Subsequently, analytical and data mining models based on SUNs are established. These models facilitate multi-dimensional, multi-stage and multi-level exploration of key factors influencing the reliability growth of high-end intelligent equipment, leading to the construction of a reliability growth evaluation index system. To overcome existing modeling bottlenecks, holographic reliability growth evaluation and prediction models are constructed by integrating big data technologies, complex uncertainty data analysis methods, sequence operators, spectrum analysis and intelligent algorithms.
The proposed novel concepts and framework demonstrate the feasibility of integrating diverse uncertainties to achieve high reliability for complex equipment.
The limitation of this research is its coverage of various uncertainties. It is not possible to cover fully all uncertainties due to their unknown status, and the proposed model provides only a method rather than a completed solution to the challenge.
Manufacturers employ reliability growth tests to iteratively enhance equipment reliability and performance through cycles of “exposing defects – analyzing causes – implementing improvements.” However, when data fail to meet the assumptions of traditional reliability growth models, practitioners often resort to ad hoc measures – such as using simulated data or borrowing data from similar equipment – which may compromise reliability. Traditional models constrained by random sampling are evidently inadequate for the development of complex equipment, necessitating new approaches. The concepts and models proposed in this paper have the potential to significantly improve the quality and reliability of complex products in smart manufacturing.
While numerous uncertainty models exist, effective frameworks for their integration remain scarce. The definitions, operational systems of SUNs, the various SUN-based data mining models and the holographic reliability growth evaluation and prediction models presented here are original contributions of the authors.
