Girija Parthasarathy

Use of SCADA Data for Failure Detection in Wind Turbines

This paper discusses the use of existing wind turbine SCADA data for development of fault detection and diagnostic techniques for wind turbines.

Neural Network Models for Usage Based Remaining Life Computation

In engine structural life computations, it is common practice to assign a life of certain number of start-stop cycles based on a standard flight or mission. This is done during design through detailed calculations of stresses and temperatures for a standard flight, and the use of material property and failure models. The limitation of the design phase stress and temperature calculations is that they cannot take into account actual operating temperatures and stresses. This limitation results in either very conservative life estimates and subsequent wastage of good components or in catastrophic damage because of highly aggressive operational conditions, which were not accounted for in design. In order to improve significantly the accuracy of the life prediction, the component temperatures and stresses need to be computed for actual operating conditions. However, thermal and stress models are very detailed and complex, and it could take on the order of a few hours to complete a stress and temperature simulation of critical components for a flight. The objective of this work is to develop dynamic neural network models that would enable us to compute the stresses and temperatures at critical locations, in orders of magnitude less computation time than required by more detailed thermal and stress models. The current paper describes the development of a neural network model and the temperature results achieved in comparison with the original models for Honeywell turbine and compressor components. Given certain inputs such as engine speed and gas temperatures for the flight, the models compute the component critical location temperatures for the same flight in a very small fraction of time it would take the original thermal model to compute.

Reduced Models for Rotating Component Lifing

The most common failure mode for engine rotating components is material fatigue. Low Cycle Fatigue or LCF is caused by stresses and temperatures resulting from start-stop cycles. One current common practice is to assign an LCF life of certain number of start-stop cycles based on a standard flight or mission. This is done during design through detailed calculations of stresses and temperatures for a standard flight, and the use of material property and failure models. The limitation of the design phase stress and temperature calculations is that they cannot take into account actual operating temperatures and stresses. In order to improve significantly the accuracy of the LCF lifing prediction, the component temperatures and stresses need to be computed for the actual operating conditions. However, stress and thermal models are very detailed and complex, and it could take on the order of a few hours to complete a stress and temperature simulation for a flight. The objective of this work is to develop reduced models, that would enable us to compute the stresses and temperatures at critical locations, without the detailed computationally intensive models. This paper describes the development of the reduced model and the results achieved in comparison with the original models for components of Honeywell propulsion engines. Given certain inputs such as engine speed and ambient temperature for the duration of the flight, the reduced models computes the component critical location temperature and thermal stress for the same flight in a very small fraction of time it would take the original finite element model to compute.Copyright

Architectures and Algorithms for Building Automation—An Industry View

Most of the content of this volume highlights new research developments in building automation, with an emphasis on heating, ventilation, and air conditioning (HVAC) control. This research is motivated by important industry and societal imperatives for improving the energy efficiency, carbon footprint, occupant comfort, and economics of building operation. Much needs to be done and control scientists and engineers have an important role to play. In this chapter, we hope to “ground” the research. The chapter consists of two primary parts. We first discuss the systems aspect of building automation systems (BASs). State-of-the-art BASs are large, complex, distributed systems. They connect to sensors, actuators, and low-level controllers; provide interfaces for operational, engineering, and management staff; and, increasingly, interconnect with other computer systems for enterprise-level applications such as facility management, energy management, and computerized maintenance management systems. A trend we highlight is the move toward “connected” buildings—in today’s Internet-of-Things (IoT) age the BAS extends to the cloud. Interoperability is another driving force in building automation; solutions that enable building owners and operators to combine equipment and applications from different suppliers—to avoid vendor lock-in—are also discussed. The content in this part of the chapter is relevant across the buildings sector. For illustration purposes, we discuss Honeywell systems that we are familiar with, especially the Enterprise Building Integrator (EBI) platform and the Tridium Niagara framework. The penetration of modern BASs is still largely limited to medium- and large-scale commercial buildings. Few light commercial buildings have systems of this sophistication (and cost). We also outline typical building control systems in use for light commercial. This building sector represents a major opportunity for new innovation—advanced control promises huge impact on energy efficiency, for example, provided that the technology can be delivered at low cost and is easy to deploy and operate by non-control-experts. The second part of the chapter discusses recent research projects in advanced control and related technologies that we have been involved with, most of which have now matured to the point of being deployed in buildings. Results from operational installations are described where available. We believe the controls research community can benefit from being better informed about the state of the art in building automation and control, with regard to the system platform as it exists and how it is evolving as well as to the algorithmic innovations that are being explored and applied in industry. We hope that, within the limitations of our experience and understanding, the content of this chapter will serve this purpose.

Computational Fluid Dynamic Modeling for Engine Diagnosis

This paper presents the results of a demonstration problem where computational fluid dynamics modeling (CFD) is used for engine diagnosis. As computational resources become faster and cheaper and detailed numerical models of heat transfer, fluid dynamics and chemical kinetics become more accurate, these numerical models can become viable alternatives for seeded fault tests. The work done here is one of the ways this could be done; that is, by using the results of a CFD model to map the effects of certain faults to a model parameter computed by a less detailed lumped parameter model.Copyright