PROGNOSTICS AND SECURE HEALTH MANAGEMENT OF ANALOG CIRCUITS

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dc.contributor.advisorPecht, Michael Gen_US
dc.contributor.advisorAzarian, Michael Hen_US
dc.contributor.authorKhemani, Varunen_US
dc.contributor.departmentReliability Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-09-16T05:35:09Z
dc.date.available2022-09-16T05:35:09Z
dc.date.issued2022en_US
dc.description.abstractAnalog circuits are a critical part of industrial circuits and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit Prognosis and Health Management (PHM) is critical to the health of industrial circuits. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with number of circuit components. Therefore, this research presents an advanced Design Of Experiments-based (DOE) approach to account for components that degrade in an individual and interacting fashion, to narrow down the number of possible fault classes under consideration. A wavelet-based deep-learning approach is developed that can localize the circuit component that is the source of degradation and predict the exact value of the degraded component. This degraded value is used in conjunction with degradation models to predict when the circuit will fail based on the source of degradation. Increasing outsourcing in the fabrication of electronic circuits has made them susceptible to the insertion of hardware trojans by untrusted foundries. In many cases, hardware trojans are more destructive than software trojans as they cannot be remedied by a software patch and are impossible to repair. Process reliability trojans are a new class of hardware trojans that are inserted through modification of fabrication parameters and accelerate the aging of circuit components. They are challenging to detect through traditional trojan detection methods as they have zero area footprint i.e., require no insertion of additional circuitry. The PHM approach is modified to detect these hardware trojans in order to incorporate circuit security, resulting in the Prognosis and Secure Health Management (PSHM) framework. Deep neural networks achieve state-of-the-art performance on classification and regression applications but are a black-box approach, which is a concern for implementation. Wavelets are approximations of cells found in the human visual cortex and cochlea. They were used to develop wavelet scattering networks (WSNs), which were intended to be an interpretable alternative to deep neural networks. WSNs achieve state-of-the-art performance on low to moderately complex datasets but are inferior to deep neural networks for extremely complex datasets. Improvements are made to WSNs to overcome their shortcomings in terms of performance and learnability. Further applications of the research are highlighted for rotating machinery vibration analytics, functional safety online estimation etc.en_US
dc.identifierhttps://doi.org/10.13016/ogmy-8edj
dc.identifier.urihttp://hdl.handle.net/1903/29154
dc.language.isoenen_US
dc.subject.pqcontrolledMechanical engineeringen_US
dc.subject.pqcontrolledElectrical engineeringen_US
dc.subject.pquncontrolledAnalog Circuitsen_US
dc.subject.pquncontrolledDesign of Experimentsen_US
dc.subject.pquncontrolledDiagnosisen_US
dc.subject.pquncontrolledHardware Trojanen_US
dc.subject.pquncontrolledPrognosisen_US
dc.subject.pquncontrolledWavelet Scattering Networksen_US
dc.titlePROGNOSTICS AND SECURE HEALTH MANAGEMENT OF ANALOG CIRCUITSen_US
dc.typeDissertationen_US

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