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.

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.

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi- tion is used to perform the diagnosis and prog- nosis tasks in a distributed way. We show how different submodels can be automatically con- structed to solve the local diagnosis and prog- nosis problems. We illustrate our approach us- ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison. In this paper we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as benchmarks in the model-based diagnosis community. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.

This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system bond graph (BG) into a set of structurally observable bond graph fac- tors (BG-Fs). Each BG-F is systematically translated into a corresponding DBN Factor (DBN-F), which is then used in its corresponding local diagnoser for quantitative fault detec- tion, isolation, and identification. By construction, the ran- dom variables in each DBN-F are conditionally independent of the random variables in all other DBN-Fs, given a subset of communicated measurements considered as system inputs. Each DBN-F and BG-F pair is used to derive a local diag- noser that generates globally correct diagnosis results by lo- cal analysis. Together, the local diagnosers diagnose all single faults of interest in the system. We demonstrate on an electri- cal system how our distributed diagnosis scheme is compu- tationally more efficient than its centralized counterpart, but without compromising the accuracy of the diagnosis results.

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.

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

Model-based prognostics approaches capture system knowl- edge in the form of physics-based models of components that include 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 decom- position approach adapted from the diagnosis community, called possible conflicts, in order to both improve the com- putational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state esti- mate 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.

In performance maintenance in large, complex systems, sensor information from sub-components tends to be readily available, and can be used to make predictions about the system's health and diagnose possible anomalies. However, existing methods can only use predictions of individual component anomalies to guess at systemic problems, not accurately estimate the magnitude of the problem, nor prescribe good solutions. Since physical complex systems usually have well-defined semantics of operation, we here propose using anomaly detection techniques drawn from data mining in conjunction with an automated theorem prover working on a domain-specific knowledge base to perform systemic anomalydetection on complex systems. For clarity of presentation, the remaining content of this submission is presented compactly in Fig 1.

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.