Physics based methodology for wind turbine failure detection, diagnostics & prognostics

The prediction of the time to failure for components within a wind turbine is becoming more important as a consequence of enlargement of the wind turbines and placing them offshore. These developments bring higher replacement and downtime costs with it in case of failure. Current failure prediction models are data driven or based on statistics, however both approaches are not sufficient to predict the failure accurately. This paper focuses on the actual loads acting on the system by taking into account how the component will fail or in other words the physics of failure. A generic physics of failure based methodology has been proposed that gives a step-by-step plan in which forces and operational data are taken into account. The methodology is divided into three parts: detection, diagnostics and prognostics. In order to validate the physics based methodology, a case study has been set up for one component and failure. SCADA and CMS data from three operating wind turbines are used to complete the case study. In this way both SCADA and CMS data are used in one method, where usually either SCADA or CMS is used. The degradation pattern and prediction of the time to failure are obtained. The case study has been proven that the methodology is useful in practice and shows the high potential of using this approach

Physics-Based Modeling Strategies for Diagnostic and Prognostic Application in Aerospace Systems

This paper presents physics-based models as a key component of prognostic and diagnostic algorithms of health monitoring systems. While traditionally overlooked in condition-based maintenance strategies, these models potentially offer a robust alternative to experimental or other stochastic modeling data. Such a strategy is particularly useful in aerospace applications, presented in this paper in the context of a helicopter transmission model. A lumped parameter, finite element model of a widely used helicopter transmission is presented as well as methods of fault seeding and detection. Fault detection through diagnostic vibration parameters is illustrated through the simulation of a degraded rolling-element bearing supporting the transmission’s input shaft. Detection in the time domain and frequency domain is discussed. The simulation shows such modeling techniques to be useful tools in health monitoring analysis, particularly as sources of information for algorithms to compare with real-time or near real-time sensor data.</p

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.Ph.D