Projects

 

Current sponsored research projects

 

Distributed Monitoring and Diagnosis of Embedded Systems Using Hierarchical Abstractions

Sponsor: NSF EHS program

The objective of this project is to develop systematic, scalable, robust, online model-based FDI schemes for distributed embedded systems. The novelty of the research centers on (i) hierarchical abstraction schemes for managing the complexity of the FDI task and enabling the design and development of online model-based FDI algorithms that are provably robust and reliable, (ii) a unified framework for diagnosis of multiple types of faults that occur in the physical and the computational parts of embedded systems as well as faults with different fault profiles (abrupt and incipient faults), and (iii) the development of a tool suite for distributed embedded systems for online FDI. Experimental test-beds are used to demonstrate and verify the effectiveness of the developed methods. The impact of the project lies on providing guarantees for reliable safe operation of complex, distributed safety-critical systems.

 

Aircraft Electrical Power System Diagnostics and Health Management

Sponsor: Navy STTR; in conjunction with Qualtech Systems, Inc.

The objective of this project is to improve the availability and reliability of aircraft power generator systems using health monitoring techniques that combine diagnostic and prognostic algorithms. We propose an innovative scheme for diagnosis and prognosis that combines the use of dynamical physical system models augmented with signal models for analyzing vibration signatures and physics of failure models for electrical, electronic, and mechanical generator components, such as rectifiers, transformers, batteries, converters, and bearings. These schemes estimate degrading device behavior as the system is involved in its regular operation. The fault diagnostic scheme uses innovative model-based approaches for root cause analysis, and the prognostic reasoning framework is based on simulation of the failing device (identified by diagnostic analysis) for relevant usage scenarios. Continued monitoring of system variables along with the degradation estimates will form the basis of algorithms that compute reliable estimates of the remaining life curve for the degrading components. The figure on the left illustrates the architecture of our diagnostic and prognostic system.

 

Online Statistical Methods for Robust State Estimation, Anomaly Detection, and Degradation Analysis in Complex, Embedded Systems 

Sponsor: NASA Aeronautics Program

Complex, safety-critical systems in aircraft, such as power generation systems have interacting subsystems that operate in multiple physical domains. A number of catastrophic accidents have demonstrated that these systems can degrade and fail in ways that are hard to predict at design time. The drive for increased safety, reliability, and autonomy imposes stringent requirements on system operation and performance, even in the presence of degradation and faults in components. Such requirements can be addressed only by accurate assessment of system health, and this has generated increased demands for onboard monitoring, analysis, and decision making schemes. Our proposed approach will combine model-based and statistical algorithms to provide robust schemes that manage modeling uncertainties, measurement noise, and the computational complexities associated with online tracking, estimation, detection, and analysis of nonlinear hybrid behaviors. The theoretical underpinnings for tracking and analysis of nominal and faulty system behavior will be centered on the use of approximate Dynamic Bayes Net (DBN) techniques. Anomaly detection methods that work in conjunction with the DBN tracker will focus on signal analysis and statistical techniques that include time-frequency representations, and maximum likelihood methods for accurate fault detection while keeping the false alarm rate low. Our detection and analysis algorithms will be tuned to analyze different fault types (sensor, actuator, and process) and different fault profiles (abrupt, incipient, and intermittent). Anomaly detection will trigger an innovative fault isolation scheme that combines qualitative reasoning and quantitative analysis of the fault dynamics to isolate and identify the root cause for the observed anomalies.

Advanced Diagnostics & Prognostics Techniques applied to NASA Spacecraft and Testbeds

Sponsor: NASA Ames

This project covers the following tasks: (1) Model building for the ADAPT Testbed subsystems. These models will form the basis for building a simulation testbed for offline experiments (VIRTUAL-ADAPT), as well as the basis for running online model-based monitoring, fault detection, and fault isolation studies, (2) Model-based Diagnosis ExperimentsA run time environment will be developed for monitoring of nominal behavior (observer-based schemes), fault detection (statistical techniques), fault isolation (TRANSCEND), and fault identification (TRANSCEND). This will involve the use of hybrid techniques because the test-bed systems combine continuous and discrete behaviors. Implement runtime infrastructure for FACT (Fault Adaptive Control Technology)1 on ADAPT in a way that we can plug in different observers, different fault detectors, and different fault isolation schemes for diagnosis. (3) Participate in the comparison of different diagnosis algorithms. (4) Formal analysis of Hybrid Diagnosis schemes and development of new approaches that address the development of hybrid diagnosis schemes for NASA applications.

 

Past sponsored research projects

 

Within the MACS laborarory we employ a three tank fluid system test-bed to evaluate tools and techniques on a real physical system.

You may also browse Completed Projects.

 

Fault Adaptive Control Technology (FACT)

Sponsor: DARPA Software Enabled Control (SEC) program

Dependability and safety of military systems is a common goal mandating increased component reliability and the use of physical redundancy in fault-tolerant architectures. In future military systems, affordability will preclude this approach. Dependability and mission readiness must be achieved by new robust control techniques that exploit on-line diagnostic capabilities combined with advanced control mechanisms rather than by ultra-reliable components and physical redundancy. In this project we develop techniques that will enable fault-adaptive real-time control of heterogeneous dynamic systems, such as those found in avionics applications.

Robust Hybrid Diagnosis and Reconfiguration

Sponsor: NASA Intelligent Systems (IS) program (2000-2003)

The essence of the proposed project is to develop autonomous, fault-adaptive, real-time control technology for complex dynamic systems. These control systems will be capable of autonomously detecting and isolating faults in system operation and reconfiguring the control paradigm in order to ensure stable and effective system operation. In this context, faults refer to drifts or failures in actuators, sensors and system components. The detection and isolation of these faults will be accomplished through dynamic system observers, which serve the dual purpose of estimating system states for feedback control. A key novelty of this project will be the development of fault observers and adaptive control systems for hierarchical hybrid systems, i.e., systems containing both continuous dynamics and discrete events. Hierarchical models at different levels of abstraction will be employed to efficiently deal with interacting subsystems that operate on widely differing time scales. The primary challenge in developing the overall scheme for fault-adaptive control will be the seamless integration of the various components of the system: (i) hybrid observers for tracking of continuous system behavior through discrete mode changes, (ii) fault detection schemes linked to the hybrid observers, (iii) multi-paradigm fault isolation schemes based on the hierarchical hybrid models, and (iv) controller selection and reconfiguration schemes to maintain system functionality, safety, and reliability. Model-based techniques will provide the overarching framework for developing the individual component technologies. The primary advantage of such systems will be the dramatic reduction of manually intensive monitoring and control. Another distinct advantage will be improved safety due to the continuous fault detection and adaptation capability.

Advanced Life Support System (ALS) technology

Sponsor(s): NASA Advance Human Support Technology Program (2003-2006)

The support of human life in the hostile environment of space critically depends on a set of complex technical systems that contain or interact with biological and chemical processes. The NASA Advanced Life Support Systems (ALS) program, itself a component of the larger Advanced Human Support Technology (AHST) Program, was created to explore new technologies required to support extended manned missions in space. Potential applications include a Lunar base, a manned mission to Mars, and the International Space Station (ISS). An ALS must exhibit a high level of autonomy, so as not to detract from the mission specific tasks of the crew. This requirement translates to a high level of availability of the individual components of the ALS. It also requires that the integrated system have the ability to adapt to changing mission objectives and crew configurations, mainly in response to unplanned events.