Algorithms for Sensor Validation and Multisensor Fusion

Doctor of Philosophy ALGORITHMS FOR SENSOR VALIDATION AND MULTISENSOR FUSION by Sean James Wellington Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single ‘best sensor’. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate. Acknowledgements I am very grateful to Professor Gurvinder Virk from the Department of Electrical and Electronic Engineering, University of Portsmouth, for his support; without his encouragement I probably would not have even started this research project. Thanks are due also to Professor Graham King of Southampton Institute for his advice and Dr Jonathan Vincent, also of Southampton Institute, for help with proof reading. Many colleagues have been generous with their time. I am particularly grateful to Dr John Atkinson of the Thick Film Unit, Department of Engineering Sciences, University of Southampton and Dr Russ Sion of C-Cubed Limited. John has provided much useful information about thick film sensors for chemical analytes. The thick film sensor data used to illustrate the operation of the various sensor validation and fusion schemes proposed in this thesis were originated as part of the EU CRAFT project SpHINX – sensors for measuring pH in inks (BRST-CT98-5521), and kindly supplied by Dr Russ Sion. Genetic Algorithms were implemented using a software toolbox written by Dr Jonathan Vincent. Finally, I am hugely grateful to my family, and in particular my wife, Nikki, for her boundless patience, love and support.