Gas Turbine Diagnostics by Means of Convolutional Neural Networks Fed With Time Series Data Encoded as Images

The presence of anomalies in time series, i.e., data points that deviate from the expected behavior, can be a symptom of an incoming failure that may lead to costly repair. Thus, the detection of anomalous data by means of diagnostic methodologies can both reduce maintenance actions and asset unscheduled stops. To tackle this challenge, for the first time in the literature, we exploit the capabilities of Convolutional Neural Networks (CNNs), by feeding them with images obtained from multivariate time series data, transformed by means of two different approaches, namely, Gramian Angular Summation Field (GASF) and Markov Transition Field (MTF). Two CNN architectures are investigated, i.e., VGG-19 and SqueezeNet. The performance of both CNNs fed with images is compared to that of i) a Temporal Convolutional Network (TCN) fed with time series data and ii) a Support Vector Machine (SVM) model. In this paper, we present the comprehensive framework, which starts from time series transformatio...