Improving fault detection and reliability in FDDI networks

FDDI (Fibre Distributed Data Interface) is a 100 Mbit/s token ring network with two counter rotating optical rings. In this paper various possible faults (like lost token, link failures, etc.) are considered, and fault detection and the ring recovery process in case of a failure and the reliability mechanisms provided are studied. We suggest a new method to improve the fault detection and ring recovery process. The performance improvement in terms of station queue length and the average delay is compared with the performance of the existing fault detection and ring recovery process through simulation. We also suggest a modification for the physical configuration of the FDDI networks within the guidelines set by the standard to make the network more reliable. It is shown that, unlike the existing FDDI network, full connectivity is maintained among the stations even when multiple single link failures occur. A distributed algorithm is proposed for link reconfiguration of the modified FDDI network when many successive as well as simultaneous link failures occur. The performance of the modified FDDI network under link failures is studied through simulation and compared with that of the existing FDDI network.

Robust Observer Design with Application to Fault Detection

A new scheme for robust estimation of the partial state of linear time-invariant multivariable systems is presented, and it is shown how this may be used for the detection of sensor faults in such systems. We consider an observer to be robust if it generates a faithful estimate of the plant state in the face of modelling uncertainty or plant perturbations. Using the Stable Factorization approach we formulate the problem of optimal robust observer design by minimizing an appropriate norm on the estimation error. A logical candidate is the 2-norm, corresponding to an H�¿ optimization problem, for which solutions are readily available. In the special case of a stable plant, the optimal fault diagnosis scheme reduces to an internal model control architecture.

On Solving Non-Standard H-/H2/H-Infinity Fault Detection Problems

We discuss solvability issues of H_-/H_2/infinity optimal fault detection problems in the most general setting. A solution approach is presented which successively reduces the initial problem to simpler ones. The last computational step generally may involve the solution of a non-standard H_-/H_2/infinity optimization problem for which we discuss possible solution approaches. Using an appropriate definition of the H- index, we provide a complete solution of this problem in the case of H2-norm. Furthermore, we discuss the solvability issues in the case of H-infinity-norm.

On solving non-standard ℍ -/ℍ 2/∞ fault detection problems

We discuss solvability issues of ℍ -/ℍ 2/∞ optimal fault detection problems in the most general setting. A solution approach is presented which successively reduces the initial problem to simpler ones. The last computational step generally may involve the solution of a non-standard ℍ -/ ℍ 2/∞ optimization problem for which we discuss possible solution approaches. Using an appropriate definition of the ℍ -- index, we provide a complete solution of this problem in the case of ℍ 2-norm. Furthermore, we discuss the solvability issues in the case of ℍ ∞-norm. © 2011 IEEE.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.