Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Solid phase velocity measurements utilising electrostatic sensors and cross correlation signal processing

Gas-solids two phase systems are widely employed within process plant in the form of pneumatic conveyors, dust extraction systems and solid fuel injection systems. The measurement of solids phase velocity therefore has wide potential application in flow monitoring and, in conjunction with density measurement instrumentation, solids mass flow rate measurement. Historically, a number of authors have detailed possible measurement techniques, and some have published limited test results. It is, however, apparent that none of these technologies have found wide application in industry. Solids phase velocity measurements were undertaken using real time cross correlation of signals from two electrostatic sensors spaced axially along a pipeline conveying pulverised coal (PF). Details of the measurement equipment, the pilot scale test rig and the test results are presented.

Modelling and prototyping the conceptual design of 3D CMM micro-probe

This paper details the prototyping of a novel three axial micro probe based on utilisation of piezoelectric sensors and actuators for true three dimensional metrology and measurements at micro- and nanometre scale. Computational mechanics is used first to model and simulate the performance of the conceptual design of the micro-probe. Piezoelectric analysis is conducted to understand performance of three different materials - silicon, glassy carbon, and nickel - and the effect of load parameters (amplitude, frequency, phase angle) on the magnitude of vibrations. Simulations are also used to compare several design options for layout of the lead zirconium titanate (PZT) sensors and to identify the most feasible from fabrication point of view design. The material options for the realisation of the device have been also tested. Direct laser machining was selected as the primary means of production. It is found that a Yb MOPA based fiber laser was capable of providing the necessary precision on glassy carbon (GC), although machining trials on Si and Ni were less successful due to residual thermal effects.To provide the active and sensing elements on the flexures of the probe, PZT thick films are developed and deposited at low temperatures (Lt720 degC) allowing a high quality functional ceramic to be directly integrated with selected materials. Characterisation of the materials has shown that the film has a homogenous and small pore microstructure.

Numerical and experimental studies of a substructural identification strategy

For structural health monitoring it is impractical to identify a large structure with complete measurement due to limited number of sensors and difficulty in field instrumentation. Furthermore, it is not desirable to identify a large number of unknown parameters in a full system because of numerical difficulty in convergence. A novel substructural strategy was presented for identification of stiffness matrices and damage assessment with incomplete measurement. The substructural approach was employed to identify large systems in a divide-and-conquer manner. In addition, the concept of model condensation was invoked to avoid the need for complete measurement, and the recovery process to obtain the full set of parameters was formulated. The efficiency of the proposed method is demonstrated numerically through multi-storey shear buildings subjected to random force. A fairly large structural system with 50 DOFs was identified with good results, taking into consideration the effects of noisy signals and the limited number of sensors. Two variations of the method were applied, depending on whether the sensor could be repositioned. The proposed strategy was further substantiated experimentally using an eight-storey steel plane frame model subjected to shaker and impulse hammer excitations. Both numerical and experimental results have shown that the proposed substructural strategy gave reasonably accurate identification in terms of locating and quantifying structural damage.