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UBC Theses and Dissertations

Condition monitoring of industrial machines using wavelet packets and intelligent multisensor fusion Raman, Srinivas


Machine condition monitoring is an increasingly important area of research and plays an integral role in the economic competitiveness in many industries. Machine breakdown can lead to many adverse effects including increased operation and maintenance costs, reduced production output, decreased product quality and even human injury or death in the event of a catastrophic failure. As a way to overcome these problems, an automated machine diagnostics scheme may be implemented, which will continuously monitor machine health for the purpose of prediction, detection, and diagnosis of faults and malfunctions. In this work, a signal-based condition monitoring scheme is developed and tested on an industrial fish processing machine. A variety of faults are investigated including catastrophic on-off type failures, partial faults in gearbox components and sensor failures. The development of the condition monitoring scheme is divided into three distinct subtasks: signal acquisition and representation, feature reduction, and classifier design. For signal acquisition, the machine is instrumented with multiple sensors to accommodate sensor failure and increase the reliability of diagnosis. Vibration and sound signals are continuously acquired from four accelerometers and four microphones placed at strategic locations on the machine. The signals are efficiently represented using the wavelet packet transform and node energies are used to generate a feature vector. A measure for feature discriminant ability is chosen and the effect of choosing different analyzing wavelets is investigated. Since the dimensionality of the feature vector can become very large in multisensor applications, various means of feature reduction are investigated to reduce the computational cost and improve the classification accuracy. Local Discriminant Bases, a popular and complementary approach to wavelet-based feature selection is introduced and the drawbacks in the context of multisensor applications are highlighted. To address these issues, a genetic algorithm is proposed for feature selection in robust condition monitoring applications. The fitness function of the genetic algorithm consists of three criteria that are considered to be important in fault classification: feature set size, discriminant ability, and sensor diversity. A procedure to adjust the weights is presented. The feature selection scheme is validated using a data set consisting of one healthy machine condition and five faulty conditions. For classifier design, the theoretical foundations of two popular non-linear classifiers are presented. The performance of Support Vector Machines (SVM) and Radial Basis Function (RBF) networks are compared using features obtained from a filter selection scheme and a wrapper selection scheme. The classifier accuracy is determined under conditions of complete sensor data and corrupted sensor data. Different kernel functions are applied in the SVM to determine the effect of kernel variability on the classifier performance. Finally, key areas of improvement in instrumentation, signal processing, feature selection, and classifier design are highlighted and suggestions are made for future research directions.

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Attribution-NonCommercial-NoDerivatives 4.0 International