The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. Current algorithms in this area rely on loopy belief propagation for active query selection. These algorithms have an exponential time complexity, making them slow and even intractable in large networks. We propose a rank-based greedy algorithm that sequentially chooses queries such that the area under the ROC curve of the rank-based output is maximized. The AUC criterion allows us to make a simplifying assumption that significantly reduces the complexity of active query selection (from exponential to near quadratic), with little or no compromise on the performance quality.

We present a conflict-based approach to diagnosing Discrete Event Systems (DES) which generalises Reiter's Diagnose algorithm to a much broader class of problems. This approach obviates the need to explicitly reconstruct the system's behaviors that are consistent with the observation, as is typical of existing DES diagnosis algorithms. Instead, our algorithm explores the space of diagnosis hypotheses, testing hypotheses for consistency, and generating conflicts which rule out successors and other portions of the search space. Under relatively mild assumptions, our algorithm correctly computes the set of preferred diagnosis candidates. We investigate efficient symbolic representations of the hypotheses space and provide a SAT-based implementation of this framework which is used to address a real-world problem in processing alarms for a power transmission system.

We propose a theory of abstraction of discrete-event systems (DES) formulated at the semantic level, i.e., as a function that maps event traces at the original (ground) level to traces at the abstract level. We study how diagnosis of DES can be performed using an abstract model, and under which conditions this process leads to a correct solution (i.e., a set of alternative diagnoses that include the real status of the system). Finally, we study how the use of an abstract model can affect the precision of diagnosis, i.e., the presence of spurious system states in the solution. To this end, we introduce the notion of diagnosability with abstract models, which ensures the precision of abstract diagnoses, and we discuss a practical way to test it.

Moreover, all of the of a system and, after detection, to determine the location faults that occurred within the (possibly extended) time interval and/or the type of system faults that caused the abnormal during which the system has been observed must be behaviour. A diagnosis hypothesis indicates which fault(s) accounted for in the diagnosis. Considering again the diagnosis occurred in the system, and the diagnosis is the set of alternative of a car, for each component we could be interested hypotheses that explain (i.e., are compatible) with in knowing whether a fault has occurred to it during the last the observed system behaviour. In this paper, we focus on week; in such a case, it is difficult to perform a drastic abstraction Model-Based Diagnosis (MBD) of Discrete-Event Systems of the model without losing any precision in the (DESs, see (Cassandras and Lafortune 1999)), where the diagnosis discrimination among different hypotheses. is computed by comparing a complete DES model In this article, we study a novel approach to reduce the of the system behaviour with a (partial) observation of the complexity of DES diagnosis, based on a reformulation of actual system behaviour (Sampath et al. 1995).

Malfunctioning HVAC equipment in commercial buildings wastes between 15% and 30% of energy. Many diagnosis approaches tackle this problem, but they either suffer from a lack of detailed fault information or a lack of adaptability to different buildings and equipment. Clearly, especially in the light of an ever increasing amount of sensor data that is available in heavily metered smart buildings, easily adaptable self learning in-depth diagnosis approaches are needed. This paper addresses the challenges of developing such approaches and describes the contribution artificial intelligence techniques like transfer learning, ontologies, knowledge representation or diagnosis can make in overcoming these challenges.

Model-based diagnosis (MBD) uses an abstraction of system to diagnose possible faulty functions of an underlying system. To improve the solution efficiency for multi-fault diagnosis problems, especially for large scale systems, this paper proposes a method to induce reasonable diagnosis solutions, under coarse diagnosis, by using the relationships between system outputs and components. Compared to existing diagnosis methods, the proposed framework only needs to consider associations between outputs and components by using an assumption-based truth maintenance system (ATMS) [de Kleer 1986] to obtain correlation components for every output node. As a result, our method significantly reduces the number of variables required for model diagnosis, which makes it suitable for large scale circuit systems.

We present a spectrum-based, sequential software debugging approach coined Sequoia, that greedily selects tests out of a suite of tests to narrow down the set of diagnostic candidates with a minimum number of tests. Sequoia handles multiple faults, that can be intermittent, at polynomial time and space complexity, due to a novel, approximate diagnostic entropy estimation approach, which considers the subset of diagnoses that cover almost all Bayesian posterior probability mass. Synthetic experiments show that Sequoia achieves much better diagnostic uncertainty reduction compared to random test sequencing.Real programs, taken from the Software Infrastructure Repository, confirm Sequoia's better performance, with a test reduction up to 80% compared to random test sequences.

Since the seminal work of Sampath et al. in 1996, despite the subsequent flourishing of techniques on diagnosis of discrete-event systems (DESs), the basic notions of fault and diagnosis have been remaining conceptually unchanged. Faults are defined at component level and diagnoses incorporate the occurrences of component faults within system evolutions: diagnosis is context-free. As this approach may be unsatisfactory for a complex DES, whose topology is organized in a hierarchy of abstractions, we propose to define different diagnosis rules for different subsystems in the hierarchy. Relevant fault patterns are specified as regular expressions on patterns of lower-level subsystems. Separation of concerns is achieved and the expressive power of diagnosis is enhanced: each subsystem has its proper set of diagnosis rules, which may or may not depend on the rules of other subsystems. Diagnosis is no longer anchored to components: it becomes context-sensitive. The approach yields seemingly contradictory but nonetheless possible scenarios: a subsystem can be normal despite the faulty behavior of a number of its components (positive paradox); also, it can be faulty despite the normal behavior of all its components (negative paradox).

In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.