Engineering topics

Roychoudhury, Indranil

Publications and source records attributed to Roychoudhury, Indranil.

At least 37 records · Page 2

NASA NTRS2015

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

structural model decomposition↗

NASA NTRS2014

Qualitative Event-Based Fault Isolation under Uncertain Observations

For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.

Daigle, Matthew↗

NASA NTRS2014

Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform

Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.

Prognosis↗

NASA NTRS2014

Integrated Diagnostics and Prognostics for the Electrical Power System of a Planetary Rover

For electric vehicles, technology for monitoring, diagnosis, and prognosis of the electrical power system (EPS) becomes essential for safe and efficient operation. To this end, we develop a general system-level integrated diagnosis and prognosis framework, which detects, isolates, and identifies EPS faults, and predicts when the EPS will fail to deliver sufficient power. The approach takes advantage of recent work in structural model decomposition in order to distribute the global diagnosis and prognosis problems into local subproblems that can be solved in parallel, thus enabling implementation on distributed computational platforms. The framework is applied to the EPS of a planetary rover testbed, and is demonstrated using data from field experiments.

Daigle, Matthew↗

NASA NTRS2014

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.

Daigle, Matthew↗

NASA NTRS2013

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.

Structural Decomposition↗

NASA NTRS2013

Distilling the Verification Process for Prognostics Algorithms

The goal of prognostics and health management (PHM) systems is to ensure system safety, and reduce downtime and maintenance costs. It is important that a PHM system is verified and validated before it can be successfully deployed. Prognostics algorithms are integral parts of PHM systems. This paper investigates a systematic process of verification of such prognostics algorithms. To this end, first, this paper distinguishes between technology maturation and product development. Then, the paper describes the verification process for a prognostics algorithm as it moves up to higher maturity levels. This process is shown to be an iterative process where verification activities are interleaved with validation activities at each maturation level. In this work, we adopt the concept of technology readiness levels (TRLs) to represent the different maturity levels of a prognostics algorithm. It is shown that at each TRL, the verification of a prognostics algorithm depends on verifying the different components of the algorithm according to the requirements laid out by the PHM system that adopts this prognostics algorithm. Finally, using simplified examples, the systematic process for verifying a prognostics algorithm is demonstrated as the prognostics algorithm moves up TRLs.

verification↗

NASA NTRS2013

Developing IVHM Requirements for Aerospace Systems

The term Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable sustainable and safe operation of components and subsystems within aerospace platforms. However, very little guidance exists for the systems engineering aspects of design with IVHM in mind. It is probably because of this that designers have to use knowledge picked up exclusively by experience rather than by established process. This motivated a group of leading IVHM practitioners within the aerospace industry under the aegis of SAE's HM-1 technical committee to author a document that hopes to give working engineers and program managers clear guidance on all the elements of IVHM that they need to consider before designing a system. This proposed recommended practice (ARP6883 [1]) will describe all the steps of requirements generation and management as it applies to IVHM systems, and demonstrate these with a "real-world" example related to designing a landing gear system. The team hopes that this paper and presentation will help start a dialog with the larger aerospace community and that the feedback can be used to improve the ARP and subsequently the practice of IVHM from a systems engineering point-of-view.

IVHM↗

NASA NTRS2013

Towards Requirements in Systems Engineering for Aerospace IVHM Design

Health management (HM) technologies have been employed for safety critical system for decades, but a coherent systematic process to integrate HM into the system design is not yet clear. Consequently, in most cases, health management resorts to be an after-thought or 'band-aid' solution. Moreover, limited guidance exists for carrying out systems engineering (SE) on the subject of writing requirements for designs with integrated vehicle health management (IVHM). It is well accepted that requirements are key to developing a successful IVHM system right from the concept stage to development, verification, utilization, and support. However, writing requirements for systems with IVHM capability have unique challenges that require the designers to look beyond their own domains and consider the constraints and specifications of other interlinked systems. In this paper we look at various stages in the SE process and identify activities specific to IVHM design and development. More importantly, several relevant questions are posed that system engineers must address at various design and development stages. Addressing these questions should provide some guidance to systems engineers towards writing IVHM related requirements to ensure that appropriate IVHM functions are built into the system design.

Requirements↗

NASA NTRS2013

Model-Based Diagnosis and Prognosis of a Water Recycling System

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Roychoudhury, Indranil↗

NASA NTRS2013

A Structural Model Decomposition Framework for Systems Health Management

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

Roychoudhury, Indranil↗

NASA NTRS2012

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Bregon, Anibal↗

NASA NTRS2012

A Distributed Approach to System-Level Prognostics

Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative Jack of scalability and efficiency of typical prognostics approaches. In order to address these is ues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resul ting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

Daigle, Matthew J.↗

NASA NTRS2012

Requirements Flowdown for Prognostics and Health Management

Prognostics and Health Management (PHM) principles have considerable promise to change the game of lifecycle cost of engineering systems at high safety levels by providing a reliable estimate of future system states. This estimate is a key for planning and decision making in an operational setting. While technology solutions have made considerable advances, the tie-in into the systems engineering process is lagging behind, which delays fielding of PHM-enabled systems. The derivation of specifications from high level requirements for algorithm performance to ensure quality predictions is not well developed. From an engineering perspective some key parameters driving the requirements for prognostics performance include: (1) maximum allowable Probability of Failure (PoF) of the prognostic system to bound the risk of losing an asset, (2) tolerable limits on proactive maintenance to minimize missed opportunity of asset usage, (3) lead time to specify the amount of advanced warning needed for actionable decisions, and (4) required confidence to specify when prognosis is sufficiently good to be used. This paper takes a systems engineering view towards the requirements specification process and presents a method for the flowdown process. A case study based on an electric Unmanned Aerial Vehicle (e-UAV) scenario demonstrates how top level requirements for performance, cost, and safety flow down to the health management level and specify quantitative requirements for prognostic algorithm performance.

video acuity↗

NASA NTRS2011

Improving Distributed Diagnosis Through Structural Model Decomposition

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

Bregon, Anibal↗

NASA NTRS2011

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

Daigle, Matthew↗

NASA NTRS2011

Investigating the Effect of Damage Progression Model Choice on Prognostics Performance

The success of model-based approaches to systems health management depends largely on the quality of the underlying models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are generally assumed in the literature, but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop different damage progression models. In simulation, we investigate how model changes influence prognostics performance. Results demonstrate that, in some cases, simple damage progression models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.

Daigle, Matthew↗

NASA NTRS2010

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

Daigle, Matthew↗