Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Patterning of tailored polycarbonate based non-chemically amplified resists using extreme ultraviolet lithography

A series of high-performance polycarbonates have been prepared with glass-transition temperatures and decomposition temperatures that are tunable by varying the repeat-unit chemical structure. Patterning of the polymers with extreme UV lithography has been achieved by taking advantage of direct photoinduced chain scission of the polymer chains, which results in a molecular-weight based solubility switch. After selective development of the irradiated regions of the polymers, feature sizes as small as 28.6 nm have been printed and the importance of resist-developer interactions for maximizing image quality has been demonstrated.