A descriptive study on development of a transfer learning based fault detection model using 2D CNN for air compressors
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Fault Detection is essential for the safe and efficient operation of industrial manufacturing. Successful detection of fault features allows us to maintain a stableproduc- tion line. Therefore, establishing a reliable and accurate fault detection method has become a huge priority now. Historically, various artificial intelligence-based models are used to predict faults in machines accurately to some extent. Mainly, machine learning and deep learning-based processes are being used. However, there are some shortcomings in those processes. Firstly, machine learning is mostly dependent on previous data and fails to recognize new issues that have not been introduced to the model during the training phase. Secondly, with deep learning, it is very time- consuming to reliably classify faults and difficult to establish an effective model for complex systems of current days. Thus, in our paper, we are proposing to use a transfer learning-based optimization of the deep learning process to meet the re- quirements ofreal-time fault classification and accurate detection of faultsin adverse operational conditions. We will be using wavelet transformation of raw signal data to 2D images and constructing a DCNN based transfer learning architecture toex- tract the fault features of the machine. Finally, we will be feeding the network with data from our target domain for fine-tuning the network to work accurately in the target domain. We will be testing with two cases to find the accuracy and accuracy optimization over the deep learning (AAG) values of our system. Finally, we will be comparing our architecture with state-of-the-art transfer learning architectures from Keras.