مشخصات پژوهش

عنوان	An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Artificial intelligence, Convolutional neural network, Deep learning, Fault detection and isolation, Sensor data, Tennessee Eastman process
چکیده	An effective fault diagnosis scheme can improve system’s safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-toend structure with 13 layers that takes raw sensor’s data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.
پژوهشگران	مجید غنی ئی زارچ (نفر اول)، محسن سلطانی (نفر دوم)