Deep learning-based intelligent fault diagnosis and prognosis of rotating machinery in an automated food production line**

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The Canadian company Vancouver Freeze Dry Ltd. manufactures various types of freeze dry food items including fruits, vegetables, dairy products, and nutraceutical ingredients. The products are available in most supermarkets and retail stores. They are also being exported after meeting the Canadian demand. To meet the stiff global competition and economic challenges, the company plans to increase the productivity while maintaining high standards of product quality and operation safety. The unexpected failures of the rotating components in the automated production line have decreased the productivity and caused safety concerns. The proposed research will develop and implement mechatronic technologies that will solve this problem. Specifically, the project will develop an improved machine condition monitoring system with suitable sensing, signal processing, fault classification and prediction technologies. The underlying challenges include the various machinery faults and operation conditions; the complexity and even infeasibility of analytical modeling of the machine degradation processes; data preprocessing with sensing errors and noise; deep neural network model construction and training with small data sets; and real-time detection and prediction in online monitoring. Planned research activities will overcome these challenges. In particular, intelligent end-to-end learning of deep neural networks will address modeling problems; intelligent sensor fusion will address precision and reliability of sensory information, with improved robustness; convolutional neural networks, recurrent neural network structures, and transfer learning with shared model parameters will solve the problems of training with small data sets; and graphical processing unit (GPU)-based processing will overcome the computing speed problem for real-time application. The project outcomes will include a sensing system including multiple sensors and data acquisition devices, improved fault diagnosis and prognosis models, experimental verification, and technology transfer. The resulting economic advantage for the Canadian industry will be significant.