Modeling of system level diagnosis processes using methods of theory-making under uncertainty
Systems and methods of decision-making
Topics:
1. (t,k)- diagnosis for state matching composition networks
(t,k)- diagnosis is a generalization of sequential diagnosis, requires at least k faulty processors identified and repaired in each iteration provided there are at most t faulty processors. The multiprocessor systems that are considered are regular (which include many well-known interconnection networks, such as hypercubes, crossed cubes, twisted cubes), or irregular systems. The main design strategy of the (t,k)- diagnosis algorithm is to identify the neighborhood of a sufficiently large fault-free aggregate. A fault-free (faulty) aggregate is a vertex subset such that all vertices in it are fault-free (faulty) and all its neighboring vertices are faulty (fault-free). By the aid of the syndrome, system the vertex set first is partitioned into some vertex subsets.
2. System-level diagnosability for regular structures
An approach aimed at evaluating the diagnosability of regular systems under the PMC model are introduced. The diagnosability is defined as the ability to provide a correct diagnosis, although possibly incomplete.
A lower bound to diagnosability t is determined by lower bounding the minimum of a “syndrome-dependent” bound over the set of all the admissible syndromes. An approach, which applies to any regular system, relies on connected components of units declaring each other non- faulty. This approach has been used to derive tight lower bounds to the diagnosability of the regular systems (toroidal grids and hypercubes), which improve the existing bounds for the same structures.
3. A fast pessimistic diagnosis algorithms for generalized multicomputer systems
This theme presents a system-level diagnosis algorithm for a generalized hypercube which is an attractive variance of a hypercube. The algorithms are considered based on the PMC model and can isolate faulty nodes to within a set that contains at most one fault-free node.
Some problems for a fast pessimistic diagnosis algorithms are considered which can diagnose not only a hypercube but also a generalized hypercube.
4. Distributed diagnosis of permanent and intermittent faults in large-scale wireless networks
A distributed fault diagnosis algorithms for wireless sensors networks (WSN) are considered, in order to handle sensor nodes having permanent fault sensor or intermittently faulty processing units. Faults appears in WSNs because of physical defects caused due to environmental hazards, imperfection in hardware and/or software. If faults are not detected and handled properly the consequences may be inexorable in case of safety critical applications. A distributed fault diagnosis algorithms to handle both permanent and intermittent faults in WSNs are considered.
5. Fault diagnosis algorithms for distributed systems
Distributed systems are becoming very popular day-by-day due to their applications in various fields such as electronic automotives, remote environment control like underwater sensor network, etc. Faults may affect the nodes of the system at any time. So diagnosing the faulty nodes in the distributed system is an worst necessity to make the system more reliable and efficient.
Some types of faults, system and fault models, those are already in literature, are described. A genetic based fault diagnosis algorithm are considered which provides better result than other fault diagnosis algorithms.
A fault diagnosis framework is considered that includes all kinds of faults for distributed network
6. Probabilistic fault diagnosis in large heterogeneous distributed systems
Probabilistic diagnosis aims at making the system-level fault diagnostic problem both easier to solve and the resulting algorithms more generally applicable. The price to pay for these advantages is that the diagnostic result is no longer guaranteed to be correct and complete in every fault situation.
The algorithms based on the local information diagnosis concept are investigating. These algorithms differ mainly in the inference propagation and fault classification phases, representing a trade-off between performance and diagnostic accuracy. The quality of the heuristic rules employed in the fault classification phase significantly affects the accuracy of diagnosis. Some heuristic methods of fault classification are defined, and the diagnostic performance provided by these heuristics are compared.