Method of Converting a Resource into a Product

The present invention is concerned in particular with the scheduling of maintenance actions such as washing events for a compressor of a gas turbine. An objective function including fuel/power price forecasts is evaluated/optimised in order to determine the advisability of a washing event. The cost function depends on a state vector comprising both Integer/Boolean and continuous state variables which are interconnected via a set of rules or constraints. Mixed Integer Programming (MIP) is a used for implementing the inventive procedure

State Transition Recognition in Robotic Assembly Using Hidden Markov Models

A process monitor for robotic assembly based on Hidden Markov Models (HMMs) is presented. The measurements are the force/torque signals arising from interaction between the workpiece and the environment for a planar assembly task. The HMMs represent a stochastic knowledge-based system where the models are trained off-line with the Baum-Welch re-estimation algorithm. After the HMMs have been trained, we use them on-line in a robotic system to recognise events as they occur. Process monitoring with an accuracy of 98% was accomplished in 0.5-0.6s. 1 Introduction Process plants must deal with changing states, multiple faults, unexpected situations and unreliable measurements. To handle these problems real-time process monitoring is essential. Process monitoring is widely used as a component in many industrial processes. In robotic assembly, however, there is an increasing need for efficient process monitoring methods to account for existing uncertainties of workpieces and the environment..

Hidden Markov Models as a Process Monitor in Robotic Assembly

A process monitor for robotic assembly based on Hidden Markov Models (HMMs) is presented. The HMM process monitor is based on the dynamic force/torque signals arising from interaction between the workpiece and the environment. The HMMs represent a stochastic, knowledge-based system where the models are trained off-line with the Baum-Welch re-estimation algorithm. The assembly task is modeled as a discrete event dynamic system, where a discrete event is defined as a change in contact state between the workpiece and the environment. Our method 1) allows for dynamic motions of the workpiece, 2) accounts for sensor noise and friction and 3) exploits the fact that the amount of force information is large when there is a sudden change of discrete state in robotic assembly. After the HMMs have been trained, we use them on-line in a 2D experimental setup to recognise discrete events as they occur. Successful event recognition with an accuracy as high as 97% was achieved in 0.5-0.6 seconds with..

A Hidden Markov Approach to the Monitoring of Robotic Assembly

A process monitor for robotic assembly based on Hidden Markov Models (HMMs) is presented. The measurements are the force/torque signals arising from interaction between the workpiece and the environment. After the HMMs have been trained, we use them on-line in a robotic system to recognise events as they occur. Process monitoring with an accuracy of 98% was accomplished in 0.5-0.6s. 1 Introduction Process plants must deal with changing states, multiple faults, unexpected situations and unreliable measurements. To handle these problems real-time process monitoring is essential. Process monitoring is widely used as a component in many industrial processes. In robotic assembly, however, there is an increasing need for efficient process monitoring methods to account for existing uncertainties of workpieces and the environment. One example of process monitoring in robotic assembly is presented by Donald [1], where a theory of planning multi-step error detection and recovery strategies for..

Combining Force and Position Measurements for the Monitoring of Robotic Assembly

A method for combining dynamic force and static position measurements for the monitoring of assembly is presented. A multilayer perceptron (MLP) network is used as a classifier where the individual network outputs correspond to contact state transitions occuring during the assembly process. When a contact state transition occurs, the MLP output with the largest value is chosen. The recognised contact state is sent to a discrete event controller which guides the workpiece through a series of contact states to the final desired configuration. The MLP has been successfully implemented on a Motorola 68040 based VxWorks board with successful recognition rates of 94.4% and 92.0% on a training set and an independent test set, respectively. 1 Introduction Reliable monitoring of robotic assembly allows for error detection and recovery from unwanted and unexpected situations. In this paper we present a method for combining data from two common information sources; dynamic force and static pos..

Pathcorrection for an industrial robot

A method in an industrial robot for increasing the accuracy in the movements of the robot, where a tool supported by the robot is brought to adopt a plurality of positions generated by the control system, which are each determined by a measuring system, whereby a deviation between the generated position and the position determined by the measuring system is introduced as a correction in the control system for adjusting the movement. The generated positions and the positions determined by the measuring system, respectively, are adapted to form a first path an a second path, respectively, whereby the correction is determined by a path deviation between geometrically calculated positions in the respective path