Graph-based systems are models wherein the nodes represent the components and the edges represent the fault propagation between the components. For critical systems, some components are equipped with smart sensors for on-board system health management. When an abnormal situation occurs, alarms will be triggered from these sensors. This paper considers the problem of identifying the set of potential failure sources from the set of ringing alarms in graph-based systems. However, the computational complexity of solving the optimal multiple fault diagnosis (MFD) problem is exponential. Based on Lagrangian relaxation and subgradient optimization, we present a heuristic algorithm to find approximately the most likely candidate fault set. A computationally cheaper heuristic algorithm - primal heuristic - has also been applied to the problem so that real-time MFD in systems with several thousand failure sources becomes feasible in a fraction of a second. This paper also considers systems with asymmetric and multivalued alarms (tests).

In this paper the classical Westland set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Multisignal modeling methodology is a simple and efficient knowledge representation scheme that captures the basic attributes of a system (structure, specifications, etc.) that are obtainable from design data and product specifications. QSI's TEAMS toolset employs multisignal modeling for testability analysis, test program set development, onboard monitoring and ground support systems. We outline the modeling methodology, its use in related areas of reliability analysis and failure modes, effects and criticality analysis (FMECA), and our efforts in building a reusable test and model library.

Real time monitoring of complex systems requires a smart and efficient inference engine. TEAMS-RT is capable of monitoring up to 1000 tests in real-time. Even so, for larger systems, a centralized solution will be computationally infeasible. Here, we present a lattice architecture of multiple collaborative TEAMS-RTs that can be embedded in the different subsystems of an interconnected system. A signal processing toolkit has been developed to facilitate data acquisition, filtering, feature extraction and test decisions.

Presents a comprehensive methodology for validating automatically generated software. The process consists of developing a fault-effect dependency model for the software, designing input stimuli to make the bugs manifest, designing tests to detect the incompatibilities from the intended behavior, and identifying the faulty code segment. A key feature of the methodology is the application of functional dependency modeling concepts, that have been proven to be robust in the testability analysis and fault diagnosis of large, complex hardware systems, to software verification and validation.

This paper is concerned with the parameter optimization in closed product-form queueing networks. Our approach is to combine the techniques of the calculus of variations with the mean value analysis (MVA) recursion of closed queueing networks. We view the MVA recursion as nonlinear difference equations describing a multi-stage system, wherein a stage corresponds to the network population, and the response times at each node constitute the state variables of the multi-stage system. This viewpoint leads to a two-point boundary value problem , in which the forward system corresponds to the MVA recursion and the backward system corresponds to an MVA-like adjoint recursion. The method allows for a very general class of objective functions, and the adjoint equations provide the necessary information to compute the gradient of the cost function. The optimization problem can then be solved by any of the gradient-based methods. For the special case when the objective function is the network delay function, the gradient vector is shown to be related to the moments of the queue lengths. In addition, the adjoint vector offers the potential for the on-line adaptive control of queueing networks based on the state information (e.g., actual degree of multi-programming, response times at the devices.) The theory is illustrated via application to the problem of determining the optimal disk routing probabilities in a large scale, modern I/O (Input/Output) subsystem. A subsequent paper will deal with extensions of the theory to multi-class networks.