Fault detection diagnosis university of new haven

A very specific topic!

Fault detection and diagnosis (FDD) is a critical aspect of many engineering disciplines, including mechanical, electrical, and aerospace engineering. The University of New Haven has a strong program in FDD, with research and education focused on developing advanced methods and techniques for detecting and diagnosing faults in complex systems.

Here are some key areas of research and education in FDD at the University of New Haven:

  1. Machine Learning and Artificial Intelligence: Researchers at the University of New Haven are exploring the application of machine learning and artificial intelligence techniques to FDD, including neural networks, decision trees, and Bayesian networks.
  2. Signal Processing and Feature Extraction: The university's FDD program emphasizes the importance of signal processing and feature extraction in detecting and diagnosing faults. Researchers are developing new algorithms and techniques for extracting relevant features from sensor data.
  3. Model-Based Reasoning: Model-based reasoning is a key approach in FDD, involving the use of mathematical models to simulate system behavior and predict faults. Researchers at the University of New Haven are developing new model-based reasoning techniques and applying them to a range of applications.
  4. Hybrid Approaches: The university's FDD program also explores hybrid approaches that combine multiple techniques, such as model-based reasoning with machine learning or signal processing.
  5. Applications: FDD research at the University of New Haven is applied to a range of domains, including:
    • Aerospace engineering: detecting and diagnosing faults in aircraft and spacecraft systems.
    • Mechanical engineering: detecting and diagnosing faults in mechanical systems, such as engines and gearboxes.
    • Electrical engineering: detecting and diagnosing faults in electrical systems, such as power grids and electrical motors.

Some of the faculty members at the University of New Haven who are involved in FDD research include:

  1. Dr. S. S. Iyengar: Professor of Electrical and Computer Engineering, with research interests in FDD, machine learning, and signal processing.
  2. Dr. A. K. Singh: Associate Professor of Mechanical Engineering, with research interests in FDD, model-based reasoning, and hybrid approaches.
  3. Dr. J. Zhang: Assistant Professor of Electrical and Computer Engineering, with research interests in FDD, machine learning, and data analytics.

The University of New Haven offers a range of academic programs and courses related to FDD, including:

  1. Bachelor's and Master's degrees in Electrical and Computer Engineering, Mechanical Engineering, and Aerospace Engineering.
  2. Graduate certificates in FDD and related areas, such as machine learning and signal processing.
  3. Online courses and professional development programs in FDD and related areas.

Overall, the University of New Haven has a strong program in FDD, with a focus on developing advanced methods and techniques for detecting and diagnosing faults in complex systems.