Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

A gravel-sand bifurcation:a simple model and the stability of the equilibrium states

A river bifurcation, can be found in, for instance, a river delta, in braided or anabranching reaches, and in manmade side channels in restored river reaches. Depending on the partitioning of water and sediment over the bifurcating branches, the bifurcation develops toward (a) a stable state with two downstream branches or (b) a state in which the water discharge in one of the branches continues to increase at the expense of the other branch (Wang et al., 1995). This may lead to excessive deposition in the latter branch that eventually silts up. For navigation, flood safety, and river restoration purposes, it is important to assess and develop tools to predict such long-term behavior of the bifurcation. A first and highly schematized one-dimensional model describing (the development towards) the equilibrium states of two bifurcating branches was developed by Wang et al (1995). The use of a one-dimensional model implies the need for a nodal point relation that describes the partitioning of sediment over the bifurcating branches. Wang et al (1995) introduce a nodal point relation as a function of the partitioning of the water discharge. They simplify their nodal point relation to the following form: s*=q*k , where s* denotes the ratio of the sediment discharges per unit width in the bifurcating branches, q* denotes the ratio of the water discharges per unit width in the bifurcating branches, and k is a constant. The Wang et al. (1995) model is limited to conditions with unisize sediment and application of the Engelund & Hansen (1967) sediment transport relation. They assume the same constant base level for the two bifurcating branches, and constant water and sediment discharges in the upstream channel. A mathematical stability analysis is conducted to predict the stability of the equilibrium states. Depending on the exponent k they find a stable equilibrium state with two downstream branches or a stable state with one branch only (i.e. the other branch has silted up). Here we extend the Wang et al. (1995) model to conditions with gravel and sand and study the stability of the equilibrium states