Projects (2015-2020)

• 50% of rotating machinery failures related to bearings failure

• Why are they hard to diagnose? Nonlinearity

• The objective is to develop an algorithm that can monitor bearings health and generalize it for various operating conditions ( load and speed)

• Bearings with various conditions and various severities were investigated

• A comparison with Envelope Analysis, wavelet and FFT was performed

• 99% accuracy achieved in classifying the bearing fault then determining the fault severity

Publications: CSNDD | JVA | JVC | CSNDD | ND (book) | TN (book)

• A physics-informed approach was developed (defect-free model was built)

• Physics-Informed features were extracted using residual and cross-sample-entropy analysis

• Random forest model integrated with SHAP algorithm was implemented to rank the extracted features

• A project in collaboration with UTRC

• The objective is to build an algorithm that can identify faults in one of the gear-boxes using noisey vibration signals

• Configuration: healthy, root crack on 1 tooth, root crack on 5 teeth, missing tooth

• 6 DOF model was built and various root tooth crack cases were modeled

• 99% accuracy achieved in identifying the defect in the gear-train

Publications: PHM | JSEA

• What is the best way to integrate data from various sources?

• A integrative deep learning approach to fuse data from different sensors

• The algorithms is based on two parts:

  • • • CNN are used to extract features from different time serieses

      • LSTM are used to rank and select the extracted features from different sources

• How can we measure a system's dimensionality?

• A novel method was developed during my doctoral work

• Based on the information in the embedding dimension of a given signal

• Capable of distinguishing various nonlinear responses within the system (chaos, multi-periodic, etc.)

• Applied to gear-train setup to identify cracks

• 7 DOF manipulator

• This project is divided into three parts: 1) Kinematic and dynamic modeling 2) Trajectory planning 3) Control

• A sliding controller is used to achieve the desired motion

• A project in collaboration with University of Uberlandia, Brazil

• Two cracks with different severities were introduced using crack propagator

• Mutual information was used to rank features

• Only three features were required to distinguish between the faults

• 100% accuracy was achieved

Publications: IFTOMM (book)

• Develop a transfer learning approach to update fault identifier when the system changes

• Structural change in the system

  • • • • A large amount of data available before the structural change

        • Limited data after the structural change

• Change in operation scenario

  • • • • A large amount of data for previous operation scenario

        • Limited data for new operation scenario

• A project in collaboration with Western Michigan University

• Detect inter-turn short circuit (ITSC)

• Early diagnosis is critical (1 second data)

• ITSC fault causes overheating result in catastrophic failure

• Noise in sensor reading

• 100% accuracy was achieved

• Worked with Villanova team lead by Dr. Nataraj collaborating with local companies and hospitals to develop Novavent, a low-cost ventilator for the treatment of COVID-19 patients

News: Forbes| kyw | MediaRoom | KeystoneEdge

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• Industrial machinery applications reported nonlinear phenomena such multi-periodic, quasi periodic and chaotic that were originating from defects or due to their nonlinear nature.

• How can we characterize system response and get insight using nonlinear dynamics?

• A novel family of methods was developed during the my doctoral work

• Based on extracting information from the phase space domain

• Generalized on various range on real mechanical and electrical systems

• A patent was filled

• Publications: VETOMAC| JVET | Grant

• Based on integrating mutual information and recurrence quantification analysis

• Recurrence plots reveals all the times when a dynamic system visits roughly the same area in the phase space (repeating different kinds of behavior)

Publications: JSEA

• 11^th order dynamical model of a two-stage servo actuator system

• Two parametric faults were studied

  • • Increased friction between spool and sleeve

    • Degradation of the armature permanent magnet

• Single and simultaneous faults

• High noise

Publications: PHM