Concentración de macro y micro minerales en tejidos

Unlike diagnostics, which has started to become an established practise, prognostics is still a large area of research and is subject to changing standards. Within literature, different definitions of prognostics include; “prognostics is, or should be, performed at

56

the component or sub-component level”; “prognostics involves predicting the time progression of a specific failure mode from its incipience to the time of component failure”; “an appreciation of future component operation is required”; “prognostics is related to, but not the same as, diagnostics.” (Sikorska et al., 2011).

A definition by the ISO 13381-1 standard gives the definition of prognostics as ‘an estimation of time to failure and risk for one or more existing and future failure modes’, (ISO 13381-1, 2004). Certainly as the technologies mature, the definitions will become more standardised, however, at present the majority of work done within the field is of a theoretical nature, with few published examples of prognostic models being applied in real scenarios, under a normal range of operating conditions and how this may impact on business models.

Without doubt, the potential advantages of prognostics is great, compared with just using diagnostics as part of a CBM system; as only the downtime due to the actual maintenance action becomes relevant. The ability to plan and schedule maintenance whilst the system is up and running offers significant savings in cost, logistics, maintenance downtime and life cycle costs. System design and development, reliability, safety can also be added to this list (Sun, Bo, et al., 2010). Example of these potential cost savings are given by (Hecht, 2006) for aviation assets, where maintenance and re-test can be pre-planned prior, yielding a saving of maintainer time and significant reduction in its variability.

In published literature that relates to prognostics, it can be seen that there is a strong correlation between and the high reliance on diagnostics. However the boundary between the two is somewhat blurred. Certainly the damage that has occurred needs to be identified and quantified as a starting point for applying prognostics, this is a retrospective activity, while prognostics is concerned with trying to predict the damage that is yet to occur. Although diagnostics may provide useful business outputs on its own, prognostics relies on diagnostic outputs (e.g. fault indicators, degradation rates etc.) and therefore cannot be done in isolation. (Sikorska et al., 2011). Detection of failure progression is more valuable compared to the detection of failure once it has reached a severe point. Furthermore, it is a prerequisite for prognostics (Xiong et al., 2008).

57

The process of prognostics involves two main stages, firstly the current health status of the system/component must be determined and this stage may considered under the heading of diagnostics. Other terms used in literature to describe this stage are; degradation identification and health assessment. Various methods may be used to fulfil this task, but as discussed above in section 3.4 the process tends to be carried out automatously, mainly by Bayesian methods and AI, utilising such techniques as pattern recognition by clustering and classification. Both of these techniques have advantages and disadvantage associated with them. The former generally requiring a model of the degradation, this involves an understanding of the physical process that accounts for the degradation and can be very difficult, if not almost impossible to determine. Where in the latter techniques, to infer an accurate degradation pattern model from an A.I based approach can utilise a large amount of failure data. This data may be difficult to obtain in large quantities, as in general, systems are not run to failure and in some systems this may take months/years to evolve.

The second stage of the prognostic is the estimation of the RUL by prediction of the degradation trend. Prognostics implies forecasting of the systems/components future health level by propagating the current health level until a failure threshold. Terms used for describing this phase are extrapolation, propagation of fault, trending, tracking and time series analysis.

The range of possible prognostic approaches and their applicability for different system in terms of increasing accuracy and cost is shown in figure 3.1 below (Vachtsevanos et al., 2006). It can be seen that experienced based prognostics is the cheapest option and its range of system applicability is diverse, consisting of statistical (e.g. Weibull) and knowledge based models (e.g. fault log data), however in terms of accuracy, it falls short of Physics of Failure (PoF) and Data Driven techniques which form the middle section of the pyramid. Model-based prognostics, at the top of the pyramid, are the most accurate but difficult and costly to develop, but are expected to take prognostics into the future.

58

Figure 3.1. Possible prognostic approaches and their applicability for different system in terms of increasing accuracy and cost (Vachtsevanos et al., 2006)

Prognostic technologies typically use measured or inferred features, in combination with data-driven and/or physics-based models, to predict the condition of the system at some future time. Prognostic techniques combining data-driven and physics-based models are sometimes referred to as ‘hybrid’ prognostics.

Figure 3.2. Main categorisation of model for RUL prediction (Sikorska et al., 2011), RUL PREDICTION KNOWLEDGE BASED MODELS LIFE EXPECTANCY MODELS ARTIFICIAL NEURAL NETWORKS PHYSICAL MODELS Increasing Complexity Fixed Rules Fuzzy Rules

Stochastic Statistical RUL Forecasting Parameter Estimation (Hybrid) Application specific Aggregate reliability functions Conditional Probability Methods Trend Extrapolation ARMA Variants PHM Other time estimation methods RUL PDF _Static Bayesian Networks Dynamic Bayesian Networks Markov Models Hidden Markov Models Kalman Filters Particle Filters

59

In the figure below, the main categories of prognostics techniques for remaining useful life prediction used in literature are presented. The next section will look at the majority of these techniques in the form of literature review to enable the best solution for the problem to be proposed.