This thesis presents an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the definition of a set of fault indicators, which are appropriate for monitoring purposes, the availability of real-time process measurements, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. The incorporation of particle-filtering (PF) techniques in the proposed scheme not only allows for the implementation of real time algorithms, but also provides a solid theoretical framework to handle the problem of fault detection and isolation (FDI), fault identification, and failure prognosis. Founded on the concept of sequential importance sampling (SIS) and Bayesian theory, PF approximates the conditional state probability distribution by a swarm of points called particles and a set of weights representing discrete probability masses. Particles can be easily generated and recursively updated in real time, given a nonlinear process dynamic model and a measurement model that relates the states of the system with the observed fault indicators. Two autonomous modules have been considered in this research. On one hand, the fault diagnosis module uses a hybrid state-space model of the plant and a particle-filtering algorithm to (1) calculate the probability of any given fault condition in real time, (2) estimate the probability density function (pdf) of the continuous-valued states in the monitored system, and (3) provide information about type I and type II detection errors, as well as other critical statistics. Among the advantages offered by this diagnosis approach is the fact that the pdf state estimate may be used as the initial condition in prognostic modules after a particular fault mode is isolated, hence allowing swift transitions between FDI and prognostic routines. The failure prognosis module, on the other hand, computes (in real time) the pdf of the remaining useful life (RUL) of the faulty subsystem using a particle-filtering-based algorithm. This algorithm consecutively updates the current state estimate for a nonlinear state-space model (with unknown time-varying parameters) and predicts the evolution in time of the fault indicator pdf. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, 95% confidence intervals, and other hypothesis tests for the failure condition under study. Finally, inner and outer correction loops (learning schemes) are used to periodically improve the parameters that characterize the performance of FDI and/or prognosis algorithms. Illustrative theoretical examples and data from a seeded fault test for a UH-60 planetary carrier plate are used to validate all proposed approaches. Contributions of this research include: (1) the establishment of a general methodology for real time FDI and failure prognosis in nonlinear processes with unknown model parameters, (2) the definition of appropriate procedures to generate dependable statistics about fault conditions, and (3) a description of specific ways to utilize information from real time measurements to improve the precision and accuracy of the predictions for the state probability density function (pdf).

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Generalmente, cuando una máquina falla y no es reparada en un corto tiempo, perjudica económicamente a la empresa, ya que el equipo deja de producir durante ese tiempo. Esta demora sucede porque la empresa encargada del servicio postventa no logra atender de forma inmediata, ya que no cuenta con los repuestos necesarios para lograr reparar el equipo. Es por ello, que el proveedor de las piezas de repuestos debe tener disponible los componentes para así atender a sus clientes en el menor tiempo posible y evitar de esta forma el pago de penalidades por incumplimiento del servicio. En ese sentido, con la finalidad de que estos proveedores puedan abastecer a sus clientes oportunamente, es importante que analicen el patrón de la demanda de los ítems, ya que estas presentan demanda intermitente. Si bien es cierto, existe una amplia literatura sobre modelos de pronósticos para piezas de repuestos; sin embargo, estas presentan deficiencias ya que se basan en la venta histórica y no utilizan como datos de entrada el ciclo de vida del equipo. De esta forma, en el presente trabajo de investigación se analizó en el primer capítulo la fundamentación teórica, en el cual se discute el estado de arte. En el segundo capítulo se presenta el diagnóstico realizado al caso de estudio. En el tercer capítulo se presenta la propuesta de solución a nivel macro y micro. Finalmente, en el cuarto capítulo se analizó la validación, así como la viabilidad económica de la propuesta. / Generally, when a machine fails and is not repaired in a short time, it hurts the company financially, since the equipment stops producing during that time. This delay happens because the company in charge of the after-sales service fails to attend immediately, since it doesn’t have the necessary spare parts to repair the equipment. That is why the supplier of spare parts must have the components available to serve their customers in the shortest possible time and thus avoid paying penalties for breach of service. In that sense, for these suppliers to supply their customers in a timely manner, it is important that they analyze the pattern of demand for the items, since they present intermittent demand. While it is true, there is extensive literature on forecast models for spare parts; However, these have deficiencies because they are based on historical sales and don’t the equipment life cycle as input data. In this way, in the present research work the theoretical foundation was analyzed in the first chapter, in which the state of art is discussed. In the second chapter the diagnosis made to the case study is presented. In the third chapter the proposal for a macro and micro level solution is presented. Finally, in the fourth chapter the validation was analyzed, as well as the economic viability of the proposal. / Trabajo de Suficiencia Profesional