In academia, the definition of “Remaining Useful Life” is well established and standardised through the International Standard Organisation – Remaining Useful Life (RUL) is "remaining time before system health falls below a defined
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failure threshold" (International Standard Organisation, 2015). However, the term remaining useful life comes in various forms such as “remnant life”, “excess life”, “remaining life”, “mean residual life” and “lifetime remaining”. In literature, RUL results have been presented as a probability density function, regression, value, confidence limit or a proportion of the likelihood of failure. In this Thesis, the presented results will have the form of a value based on a 95% confidence limit. While a well-established approach in remaining useful life prediction can be found in the aerospace avionic/electronic domain, research shows this discipline is still developing in the maintenance context. However, the maintenance prognosis is gradually gaining ground. The research gap relates to predicting the remaining useful life multi-component in an assembly.
Nguyen et al (2015) propose a novel predictive maintenance policy with multi- level decision approach. Their work focused on multi-component systems with complex structure by using a system level and a component level for decision- making. A Monte Carlo simulation technique is used for evaluating maintenance costs. They argue that the approach is robust, but computing time can increase when the number of components is high.
Rodrigues (2017) estimates remaining useful life prediction of multiple- component systems based on a system-level performance indicator. In his work, a system-level performance indicator is calculated based on the performance of each component and the system-level RUL predicted. The focus is on hydraulic system containing multiple pumps with an air conditioning system for aircraft containing various components. The method used is Particle Filter known as Sequential Monte Carlo.
Lee and Pan (2017) present a predictive maintenance of complex system with multi-level reliability structure, where data generated from on-board sensors are utilised. A discrete time Markov Chain model for modelling multiple degradation processes of components and a Bayesian network model for predicting system reliability is applied. A probabilistic inference is conducted at the system level.
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Hafsa et al (2015) emphasise the essence of interactions between complex system components RUL by conducting a prognostics of health status of multi- component systems. Their work focused on a Lorry system and the Weibull model is applied to estimate the remaining useful life of the system.
Bian and Gebraeel (2014) present stochastic modelling and real-time prognostics for multi-component systems with degradation rate interactions. In their work, the behaviours of condition-degradation-based sensor signal relating to each component are modelled. The model estimates the residual lifetime distribution of each component using a Bayesian model.
Furthermore, the prediction methodology described in literature for estimating the remaining useful life of residential appliances incorporates Weibull analysis (Welch and Rogers, 2010). This methodology actually solves fraction of units remaining using a β factor to reasonably estimate the RUL of a residential appliance based on a particular Expected Useful Life (EUL) and years in service instead of remaining useful life. The systems under investigation are air conditioning units and the methodology can be applied to other appliances such as refrigerator, freezers clothes washers and dryers (Welch and Rogers, 2010). Louen et al (2013) propose a two-step RUL framework which requires acceptable and unacceptable performance data for training. A support vector machine (SVM) method detects faults and monitors health of an equipment in operating mode, while the performance degradation is Weibull distributed. The Weibull distribution produces a trajectory for the performance degradation, which is the distance to the SVM’s separating hyperplane. The trajectory performance degradation is the performance indicator. The RUL is described as the difference between the end of life and the current time focusing on a single asset.
In (Bechhoefer et al, 2015), the RUL is estimated using actuarial methods. The failure rate is initially used to estimate the parameters for the Weibull distribution. The conditional expectation of the truncated survival function of the Weibull is used to estimate the time-to-failure: given that the equipment has survived to time
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is calculated as the difference in the expected time of failure from current time. The RUL compares the simulated and the experimented time of failure to give the RUL. An opportunity cost is linked with lost productivity due to a failure in order to calculate the associated cost. This cost is based on the safety margin on the equipment.
The researchers above developed a framework to predict the RUL of a single component and appliance using a Weibull function and data-driven methodology. In their research, the single component or appliance is run and assessed until failure. However, in this research, RUL prediction of a component is conducted by assessing the same components in an assembly – multi-component using the Weibull reliability function, statistical technique and data-driven methodology. This research is conducted in the TES centre to identify the qualitative and quantitative factors from the historical information, which is likely to affect maintenance prognosis of mechanical component degradation.
In academia, prognostics focus on the performance of a system, subsystem, or component to estimate its remaining useful life, while this Thesis aims to assess the mechanical multi-component degradation to predict their remaining useful life. Research in academia provided only the framework structure and data analysis of remaining useful life. However, in literature, no evidence relative to the RUL prediction of multi-component in an assembly, which has led to undertaking this research.