Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents a novel inte- grated 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 automati- cally 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.

Systems health monitoring is essential in guar- anteeing the safe, efficient, and correct opera- tion of complex engineered systems. Diagnosis, which consists of detection, isolation and identi- fication of faults; and prognosis, which consists of prediction of the remaining useful life of com- ponents, subsystems, or systems; constitute sys- tems health monitoring. This paper presents an integrated model-based diagnostic and prognos- tic framework, where we make use of a com- mon modeling paradigm to model both the nom- inal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach us- ing a simulated propellant loading system that in- cludes tanks, valves, and pumps.

Complex engineering systems require efficient fault diagnosis methodologies, but centralized ap- proaches 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 decompo- sition 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 that gen- erate globally correct local diagnosis results. The proposed approach is applied to a multi-tank sys- tem, and results demonstrate an improvement in the design of local diagnosers. Since local diag- nosers 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 dis- tributed approaches.

Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.

Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key tech- nology 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 life- times that are important, but, rather, the lifetimes of the sys- tems 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 rela- tive lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics prob- lem, based on the concept of structural model decomposi- tion. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting 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 re- sults show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.

Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete behaviors because mode changes make diagnosability hard to achieve. Including additional sensors can improve diagnosability, but that is not always feasible. An alternative strategy is active diagnosis, where we improve the diagnosis result by executing or blocking controllable events. We present a qualitative, event-based approach to active diagnosis of hybrid systems, where we automatically synthesize event-based diagnosers for hybrid systems that can determine if the system is diagnosable through passive or active diagnosis. We apply our active diagnosis scheme to a real-world electrical power distribution system.

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX-09), that ProDiagnose can produce results with over 96% accuracy and < 1 second mean diagnostic time. **Reference:** B. W. Ricks, and O. J. Mengshoel. "The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems." Proc. of the 20th International workshop on Principles of Diagnosis (DX-09) Stockholm, Sweden, 2009 **BibTex Reference:** @inproceedings{ricks09diagnostic, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems}, booktitle = {Proc. of the 20th International Workshop on Principles of Diagnosis (DX-09)}, address = {Stockholm, Sweden}, year = {2009} }

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

The lecture of the [Stanford-IVHM lecture series](https://dashlink.arc.nasa.gov/group/stanford-ivhm-lecture-series/) will give an overview of the approaches in building diagnostic solutions for networks and complex systems. The conventional rule-based approach and the top-down analysis will be compared with other innovative solutions based on information modeling and codebook correlation. One specific solution pioneered by research done in Columbia University and later implemented by SMARTS/EMC will be presented in more detail as an example of a consistent approach to diagnostics. Speaker: Yuri Rabover, Ph.D. VMTurbo Dr. Yuri Rabover, is a co-founder and Director of Product Strategy of VMTurbo, a startup in a stealth mode. Prior to VMTurbo Yuri spent 12 years working for SMARTS as director of engineering, product management and technology partnership. After EMC acquired SMARTS for $275M in 2005, Yuri was managing the Advanced Solution Group in the EMC Corporate CTO Office developing prototypes and proof of concepts of new innovative solutions. He is a seasoned technologist, strategist and researcher in the wide area of system, network and storage management with more than 20 years of industry and academia experience.

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.