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

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

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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

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

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

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

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