A novel scalable method for machine degradation assessment using deep convolutional neural network

Abstract Bandsaw machines are widely used in the rough machining stage to cut various materials into required dimensions. Deterioration on the blade, which is a critical component of the bandsaw machine, not only causes a waste of cutting material but also represents a major portion of the operation & maintenance cost for the machine user. Although non-high-end manufactures put as much emphasis on the accuracy of the cuts as high-end manufacturers, non-high-end bandsaw machine users are not as easily able to justify the high cost associated with the blade wear monitoring solution. Therefore, this paper proposes a methodology to develop a scalable blade degradation model that is suitable for massive deployment at an affordable cost. A 4-stage roadmap is proposed to provide step by step guidance in the development and deployment of the scalable blade degradation model. As the core issue of the roadmap, the degradation model development is solved by the proposed dual-phase modeling methodology. In phase I, a physics informed model (which relies on physical analysis to extract effective features) is established to generate a reliable health indicator (HI) to monitor the blade wear condition utilizing the critical vibration and acoustic signals. Phase II proposes to develop a deep convolutional neural network (DCNN) based surrogate model to replace the physics informed model. The DCNN based surrogate model will use only alternative low-cost sensor data. By eliminating the usage of the high-cost vibration and acoustic sensors, the developed surrogate model is expected to cost much less than the physics informed model. Finally, the effectiveness of the proposed methodology is validated using data from different bandsaw machines and blades, and the superior performance of the DCNN is observed as compared to traditional machine learning algorithms.