Amin Amini
ABSTRACT
In recent years, digital twin (DT) has been an efficient and reliable representation tool for Industry 4.0 in general and fault diagnosis and monitoring, especially in machinery consisting of several components working in tandem that introduce a large amount of data to be processed. Having a cloud-based and platform-independent system that can interpret Big Data in real-time and represent the processed information in a user-friendly manner offers the opportunity not only for improving fault diagnosis and troubleshooting automation, but also offering a better representation of data for operators, technicians and engineers. This not only decreases the repair costs by enabling constant components' state of health awareness, but also increases the efficacy of the apparatus by lowering the downtimes due to faults and issues.
In this paper (WindTwin), we propose a real-time, interactive and dynamic 3-D neural-network model that provides important information of a small-scale wind turbine (WT) machinery setup including the state of health of different components (bearings, shafts) based on monitoring sensors (vibrations and temperature) and operations conditions (rotation speed). The system features real-time data processing of six sensors with eight channels including two single axis-accelerometer, one tri-axial accelerometer, one acoustics (microphone), one temperature and one light sensor (for rotational speed) with sampling data collection rate of 51.2 kHz achieved through an edge developed machine learning algorithm based on Artificial Neural Network (ANN) algorithm using single hidden-layer feed forward neural network (SLFN) and gradient-based backpropagation (BP) training algorithm. The system also provides platform-independent (desktop and mobile companion app) user interfaces thanks to its cloud-based data processing integration. The prototype system proved to have on average 83.33 % accuracy for the vibration-based condition monitoring model across the three different rotation speed of 15Hz, 9Hz and 12Hz with 1 second latency for different fault conditions to be reflected on the DT counterpart.
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Cross Ref
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Index Terms
- An AI Driven Real-time 3-D Representation of an Off-shore WT for Fault Diagnosis and Monitoring
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