| Abstract |
With the rapid development of smart cities, urban infrastructure systems produce massive data that reflect their real-time operational conditions. These data provide insights for system monitoring and operation, and many existing studies develop various machine learning methods to understand recurrent system conditions. However, the extreme operational conditions, which could cause system failures, are not well explored. Importantly, methods for the recurrent conditions may not be suitable for modeling the failures. To fill this gap, this paper proposes a novel task of failure prediction, which aims to predict system failures before they happen. To solve this task, a generalized model that integrates survival analysis and the temporal convolutional networks, which is called TCNSurv in this paper, is developed to predict the distribution of system failure time. The model mainly contains three components: a data processing module, a time series module, and a survival analysis module. Specifically, the time series module employs Temporal Convolutional Networks to enable the modeling of temporal dependencies in time series data, and the survival analysis module explicitly formulates the probability of system failures. The proposed model is validated on three real-world datasets: vibration, traffic, and electricity, and results show that the developed model outperforms state-of-the-art regression-based models, survival analysis-based models, as well as integrated models. The research outcomes could help to understand the failure patterns of urban infrastructure systems and to develop early warning systems for smart cities. © 2024 Elsevier Ltd |
