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Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation–maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter.

Total care schemes are now a common feature in the sales of power generation and propulsion plant. To mitigate risk of financial penalties and maximise profit, many suppliers will rely on health usage and condition monitoring techniques. Intelligent condition monitoring is a relatively new concept in this field and introduces prognostic capability. One key obstacle in this approach is the implementation of some form of rule-base that encapsulates possible fault conditions. The difficulty here is that a given fault scenario will not necessarily manifest itself in the same manner twice and will require complex rule-sets to describe possible variations in the development of the fault. In addition, due to the robustness of current high-integrity plant, example fault conditions are very rare and hence difficult to model using data driven approaches. Seeding faults during development is one approach often used, however, this can never be entirely representative of an in-service failure in addition to being a costly exercise. This paper describes the practical implementation of novelty detection schemes that aim to overcome the limitations described above.

Date of Conference: 20-25 April 1997 Conference Name: International Conference on Robotics and Automation, IEEE 1997 In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robot's environment. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight data, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused by employing multiple logical sonars at different geographical sites. Feature data from multiple logical sensors are fused with Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Dempster-Shafer fusion results are contrasted with the results of combination of sensor beliefs through simple majority vote. The method is verified by experiments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.

This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor environments. The neural network can differentiate more targets, and achieves high differentiation and localization accuracy, improving on previously reported methods. It achieves this by exploiting the identifying features in the differential amplitude and time-of-flight characteristics of these targets. An important observation follows from the robustness tests, which indicate that the amplitude information is more crucial than time-of-flight for reliable operation. The study suggests wider use of neural networks and amplitude information in sonar-based mobile robotics.

This paper investigates the use of voting as a conflict-resolution technique for data analysis in robot programming. Voting represents an information-abstraction technique. It is argued that in some cases a voting approach is inherent in the nature of the data being analyzed: where multiple, independent sources of information must be reconciled to give a group decision that reflects a single outcome rather than a consensus average. This study considers an example of target classification using sonar sensors. Physical models of reflections from target primitives that are typical of the indoor environment of a mobile robot are used. Dispersed sensors take decisions on target type, which must then be fused to give the single group classification of the presence or absence and type of a target. Dempster-Shafer evidential reasoning is used to assign a level of belief to each sensor decision. The decisions are then fused by two means. Using Dempster’s rule of combination, conflicts are resolved through a group measure expressing dissonance in the sensor views. This evidential approach is contrasted with the resolution of sensor conflict through voting. It is demonstrated that abstraction of the level of belief through voting proves useful in resolving the straightforward conflicts that arise in the classification problem. Conflicts arise where the discriminant data value, an echo amplitude, is most sensitive to noise. Fusion helps to overcome this vulnerability: in Dempster-Shafer reasoning, through the modeling of nonparametric uncertainty and combination of belief values; and in voting, by emphasizing the majority view. The paper gives theoretical and experimental evidence for the use of voting for data abstraction and conflict resolution in areas such as classification, where a strong argument can be made for techniques that emphasize a single outcome rather than an estimated value. Methods for making the vote more strategic are also investigated. The paper addresses the reduction of dimension of sets of decision points or decision makers. Through a consideration of combination order, queuing criteria for more strategic fusion are identified.

This paper describes consequences of local information processing for decision making in decentralised systems of sensor nodes. In decentralised data fusion systems, nodes take decisions based on information acquired locally. The ability of such nodes to fuse or combine information is linked to network organisation. Earlier work demonstrates a problem of inconsistency which arises given cyclic information flow in decentralised systems where nodes combine global information. This work shows how this inconsistency limits the decision making capabilities of the sensor nodes. Consequences for real-world systems using decentralised processes for decision making in process monitoring, tracking and aviation are discussed.

Network management concerns the ability of a network to sustain continued correct operation. What constitutes correct operation of a network is dependent on the nature and application of the system. A decentralized data fusion system consists of a network of sensor nodes, each capable of local fusion of sensor data. The decentralized estimation algorithms described here are constrained to operate on fully connected and tree connected topologies. Neither topology is practical, both are vulnerable to the loss of a single link. Decentralized estimation in fully connected systems exploits the fact that direct communication is possible between every pair of nodes. The loss of any link changes the pattern of communication upon which the estimation algorithm is based. In a tree connected system, the loss of a single link can divide the network in two, stopping network-wide communication altogether. Management must maintain a continued flow of information throughout the network and along the routes which provide for the best overall network performance. Reliable routing depends on the existence of a range of routes and the ability to switch routes when failures occur. Decentralized data fusion management necessitates that the routing system operates within the constraints imposed on the sensing network. A management system which reconciles the requirement for redundant routes with the need for the maintenance of valid topologies is described. Thus, we provide for continued communication and estimation in a changing topological environment.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.