| dc.description.abstract | Abstract. Anomalies in time series can be a symptom of an incoming failure. Thus, the detection of anomalous data can both reduce maintenance actions and asset unscheduled stops. To tackle this challenge, we exploit the capabilities of convolutional neural networks (CNNs), fed with images obtained from multivariate time series data, transformed by means of two different approaches. 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 transformation, goes through CNN development and ends with anomaly detection. The framework is applied to field data taken during normal operation of ten SGT-800 gas turbines, located in two different regions. The normal data covers 150 days of operation. Spike faults are implanted in two out of the 20 available measured variables, i.e., compressor discharge temperature and compressor discharge pressure, by considering different combinations of maximum fault magnitude and number of implanted spikes in each time series. The results demonstrate that both CNNs fed with images achieve significantly higher classification accuracy than both a TCN model fed with time series data and an SVM model. Moreover, the Markov transition field (MTF) method always proves more robust than Gramian angular summation field (GASF) method, and also allows higher accuracy values, in the range from 0.85 to 0.99. | |
