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

Empirical convergence analysis of federated averaging for failure prognosis

Data driven prognosis involves machine learning algorithms to learn from previous failures and generate its prediction model. However, often a single asset does not fail so frequently to have enough training data in the form of historical failures. This problem can be addressed by learning from failures across a cluster of similar other assets, but often working in different environments. The algorithm therefore must learn from a distributed dataset which might be heterogenous but with underlying similarities. Federated Learning is an emerging technique that has recently also been proposed as a fitting solution for prognosis of industrial assets. However, even the most commonly used Federated Learning algorithms lack theoretical convergence guarantees, and therefore their convergence must be analysed empirically. This paper empirically analyses the convergence of the Federated Averaging (FedAvg) algorithm for a fleet of simulated turbofan engines. Results demonstrate that while FedAvg is applicable for prognosis, it cannot acknowledge the differences in asset failure mechanisms. As a result, the prognosis framework needs to be modified such that similar failures are clustered together before FedAvg can be implemented.This research was funded by the EPSRC and BT Prosperity Part- nership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/

Failure Prognosis of Embedded Systems Based on Temperature Drift Assessment

International audienceThe Systems-on-Chip provide a large capacity for calculation and monitoring, so they are increasingly integrated into risky processes such as aeronautical and power generation systems. However, embedded systems are subject to degradation caused by wear, that can be accelerated by the often hostile environment. This paper proposes a method of failure prognosis of embedded systems based on the estimation of the temperature drift under reference operating conditions, then the modelling of the drift trend using a support vector regression model. The remaining useful life is estimated using the integral of the probability density function of the time to failure. Experimental results, evaluated by performance analysis techniques, show the effectiveness of the proposed approach

Neglected hematological parameters in heart failure prognosis – Disclosures from the REFERENCE study

Aims: In heart failure patients, anemia and iron deficiency are predictors of poor outcome. We studied the association of anemia, iron deficiency and related hematological parameters with short-term rehospitalization, short-term all-cause mortality and end of follow-up all-cause mortality in heart failure patients. Material and Methods: Anemia, iron deficiency, red cell distribution width and erythropoietin were assessed in patients hospitalized with acute decompensated heart failure. Univariate Cox proportional hazard model was used to assess the relationship between variables and outcomes. Results: 65 patients were followed for a median of 13.7 (Q1-Q3 6.7-18.9) months. Mean age was 79.2 (SD 10.8) years. The mean left ventricular ejection fraction was 50.38 ± 19.07 %. Variables associated with an increased risk for short-term rehospitalization were red cell distribution width (HR 1.35; 95% CI 1.16- 1.58), anemia (HR 3.81; 95% CI 1.29-11.28) and anemia with iron deficiency (HR 3.50; 95% CI 1.30-9.38). Increased risk for short-term mortality was associated with red cell distribution width (HR 1.83; 95% CI 1.29-2.59), erythropoietin (HR 1.38; 95% CI 1.04-1.82), absolute iron deficiency (HR 7.22; 95% CI 1.50-34.81) and anemia with iron deficiency (HR 4.48; 95% CI 1.26-15.88). Variables associated with increased risk for end of follow-up mortality were red cell distribution width (HR 1.31; 95% CI 1.12-1.54) and erythropoietin (HR 1.29; 95% CI 1.11-1.49). Conclusions: Conclusions: Anemia and red cell distribution width correlated with higher risk for short-term rehospitalization. Absolute iron deficiency, red cell distribution width and erythropoietin were associated with higher risk for short-term mortality. Red cell distribution width and erythropoietin were associated with higher risk for end of follow-up mortality.info:eu-repo/semantics/publishedVersio

Secondary analysis of data on comorbidity/multimorbidity: a call for papers

Despite the high proportion and growing number of people with comorbidity/multimorbidity, clinical trials often exclude this group, leading to a limited evidence base to guide policy and practice for these individuals [1–5]. This evidence gap can potentially be addressed by secondary analysis of studies that were not originally designed to specifically examine comorbidity/multimorbidity, but have collected information from participants on co-occurring conditions. For example, secondary data analysis from randomized controlled trials may shed light on whether there is a differential impact of interventions on people with comorbidity/multimorbidity. Furthermore, data regarding comorbidity/multimorbidity can often be obtained from registration networks or administrative data sets

The importance of hematological parameters in heart failure prognosis - evidence from the REFERENCE Study

info:eu-repo/semantics/publishedVersio