A predictive method that allows a condition-based maintenance implementation based on failure statistics and partial knowledge of failure mechanisms

Dissertation (MEng)--University of Pretoria, 2018.Over the past decades, the progression from a reactive maintenance approach, to a time/use-based preventative approach, to a predictive approach, or Condition-Based Maintenance (CBM), for components subjected to ageing failure mechanisms such as fatigue, corrosion and wear, has led to signi cant savings on downtime and expenditures. In this study, a spectrum of the level of insight and information available when embarking on this progression, is considered. On the one side of the spectrum is the case where a quantitative physical failure model is not available and/or the measurement of condition parameters is not feasible, but statistical failure data is available. This enables the use of Reliability Theory (RT) to implement a time/use-based Preventative Maintenance (PM) approach. On the other side is the ideal case for CBM, which entails feasible implementation of Condition Monitoring (CM) and where a physical failure model with all its parameters is known and the measured condition parameter enables the accurate calculation of the Remaining Useful Life (RUL). A bridge between CBM and time/usebased PM is represented by the Proportional Hazard Model (PHM) technique, which does not take a physical failure model into account, but where CM is feasible and relies on the fact that historic condition and failure data is available. The main research question that is addressed during this study is the lack of an approach to implement CBM on equipment when historic condition monitoring data is not available, which may often be the case. On the spectrum, this would be placed between the ideal CBM case and the PHM technique. A new methodology is therefore developed that combines partial insight into the physical failure model with some form of measurable condition, as well as failure statistics, in order to develop degradation functions, or PF curves, to resemble component condition which may be used for CBM decisions. The newly developed method enables the implementation of CBM, which is initially based only on failure statistics and assumptions regarding the physical failure models, without the need for historic CM data. When the newly developed method is implemented, CM data is assembled, and this data may be used to continuously update the failure model assumptions, to progressively develop a full, economic CBM implementation. The development of the new method is based on a numerical experiment, simulating components prone to fatigue failure, with various chosen initial conditions and operating conditions, to produce failure statistics. It is then assumed that, in practice, only these failure statistics would be known, as well as the form of the failure mechanism. The new method, to establish PF curves for a component with any given life based on this information, then entails arbitrarily choosing initial conditions, or defect sizes, and then calculating the operating condition parameter in the crack growth equation, to yield the required life. Using these arbitrarily chosen and calculated parameters, estimated PF curves may be derived, which would be used to base RUL and CBM decisions on. With the \true" PF curve known from the numerically generated data, the accuracy of such decisions can be evaluated. This is done in the form of a sensitivity study, where the sensitivity of the accuracy of RUL decisions as a function of the arbitrary choice of initial conditions, can be tested for a wide range of component types. This sensitivity study yields promising results, as the error for all component types are low. The practical application of the new method is also demonstrated for bearings, where a fatigue related ageing mechanism is assumed and vibration CM provides indirect measurement of the condition. It is shown that the method provides su ciently accurate predictions of RUL to enable implementation of CBM, without initial availability of historic CM data. A further bene t of the new method is showcased, through its enablement of numerical simulation of the outcomes of the application of di erent maintenance tactics on a complex system. The simulated illustrative system consists of four component types, with ten of each component type with randomised initial and operating conditions. A timebased simulation is made possible, since the estimated PF curves for each component is known, using the newly developed method. The model simulates a period of ten years and replacements are made according to the applied maintenance tactic. CBM, which forms part of a predictive approach and would be enabled by the method developed in this study, is compared to a reactive approach and a preventative approach. Compared to a reactive approach, the predictive approach resulted in 78% less downtime and 67% less expenditure. Compared to a preventative approach, the predictive approach resulted in 56% less downtime and 57% less expenditure. These promising results would assist in making a business case for the implementation of CBM in practical applications.Mechanical and Aeronautical EngineeringMEngUnrestricte