Detection of faults in a high speed packaging machine using a multilayer perceptron (MLP)

In high speed manufacturing systems, continuous operation is desirable, with minimal disruption for repairs and service. An intelligent diagnostic monitoring system, designed to detect developing faults before catastrophic failure, or prior to undesirable reduction in output quality, is a good means of achieving this. Artificial neural networks have already been found to be of value in fault diagnosis of machinery. The aim here is to provide a system capable of detecting a number of faults, in order that maintenance can be scheduled in advance of sudden failure, and to reduce the necessity to replace parts at intervals based on mean time between failures. Instead, parts will need to be replaced only when necessary. Analysis of control information in the form of position error data from two servomotors is described.

Assisted remote viewing of a teleoperation work cell

Intelligent viewing systems are required if efficient and productive teleoperation is to be applied to dynamic manufacturing environments. These systems must automatically provide remote views to an operator which assist in the completion of the task. This assistance increases the productivity of the teleoperation task if the robot controller is responsive to the unpredictable dynamic evolution of the workcell. Behavioral controllers can be utilized to give reactive 'intelligence.' The inherent complex structure of current systems, however, places considerable time overheads on any redesign of the emergent behavior. In industry, where the remote environment and task frequently change, this continual redesign process becomes inefficient. We introduce a novel behavioral controller, based on an 'ego-behavior' architecture, to command an active camera (a camera mounted on a robot) within a remote workcell. Using this ego-behavioral architecture the responses from individual behaviors are rapidly combined to produce an 'intelligent' responsive viewing system. The architecture is single-layered, each behavior being autonomous with no explicit knowledge of the number, description or activity of other behaviors present (if any). This lack of imposed structure decreases the development time as it allows each behavior to be designed and tested independently before insertion into the architecture. The fusion mechanism for the behaviors provides the ability for each behavior to compete and/or co-operate with other behaviors for full or partial control of the viewing active camera. Each behavior continually reassesses this degree of competition or co-operation by measuring its own success in controlling the active camera against pre-defined constraints. The ego-behavioral architecture is demonstrated through simulation and experimentation.

A Newton method for pose refinement of 3D models

Different optimization methods can be employed to optimize a numerical estimate for the match between an instantiated object model and an image. In order to take advantage of gradient-based optimization methods, perspective inversion must be used in this context. We show that convergence can be very fast by extrapolating to maximum goodness-of-fit with Newton's method. This approach is related to methods which either maximize a similar goodness-of-fit measure without use of gradient information, or else minimize distances between projected model lines and image features. Newton's method combines the accuracy of the former approach with the speed of convergence of the latter.

Artificial ants for active routing

Ant colonies in nature provide a good model for a distributed, robust and adaptive routing algorithm. This paper proposes the adoption of the same strategy for the routing of packets in an Active Network. Traditional store-and-forward routers are replaced by active intermediate systems, which are able to perform computations on transient packets, in a way that results very helpful for developing and dynamically deploying new protocols. The adoption of the Active Networks paradigm associated with a cooperative learning environment produces a robust, decentralized routing algorithm capable of adapting to network traffic conditions.

Robust identification for linear-in-the-parameters models

In this paper new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness, including three algorithms using combined A- or D-optimality or PRESS statistic (Predicted REsidual Sum of Squares) with regularised orthogonal least squares algorithm respectively. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalisation scheme in orthogonal least squares or regularised orthogonal least squares has been extended such that the new algorithms are computationally efficient. A numerical example is included to demonstrate effectiveness of the algorithms. Copyright (C) 2003 IFAC.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Planning autonomous vehicles in the absence of speed lanes using an elastic strip

Planning of autonomous vehicles in the absence of speed lanes is a less-researched problem. However, it is an important step toward extending the possibility of autonomous vehicles to countries where speed lanes are not followed. The advantages of having nonlane-oriented traffic include larger traffic bandwidth and more overtaking, which are features that are highlighted when vehicles vary in terms of speed and size. In the most general case, the road would be filled with a complex grid of static obstacles and vehicles of varying speeds. The optimal travel plan consists of a set of maneuvers that enables a vehicle to avoid obstacles and to overtake vehicles in an optimal manner and, in turn, enable other vehicles to overtake. The desired characteristics of this planning scenario include near completeness and near optimality in real time with an unstructured environment, with vehicles essentially displaying a high degree of cooperation and enabling every possible(safe) overtaking procedure to be completed as soon as possible. Challenges addressed in this paper include a (fast) method for initial path generation using an elastic strip, (re-)defining the notion of completeness specific to the problem, and inducing the notion of cooperation in the elastic strip. Using this approach, vehicular behaviors of overtaking, cooperation, vehicle following,obstacle avoidance, etc., are demonstrated.

Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.