Time Series Classification (TSC) is a problem with many applications in science and engineering. In reliability engineering, the time series data are used for equipment reliability analysis, fault detection etc. In this work, we examined several deep learning models on long-sequence time series. With the emergence of new technology, such as connected sensors, more long sequence of time series data become available. We developed several deep learning models to classify the high frequency / long sequence time series. Our approaches avoided heavy feature engineering, and attempted to make it “end-to-end.” The models were tested on a data set coming from real world high frequency voltage sensors to detect whether a partial discharge (PD) fault exists. We discussed the performance of the deep learning models, and their advantage and disadvantages to the non-deep-learning benchmark.
Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate risky scenarios by the traditional PRA methods. In this study, a new dynamic probabilistic risk assessment methodology is proposed that employs a new exploration strategy to generate risky scenarios. The proposed methodology consists of three main modules, including simulation, planner, and scheduler. In this methodology, the engineering knowledge of the system is explicitly used to guide the simulation module to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the planner module which is responsible for generating plans as a high-level map to guide the simulation. The scheduler module is responsible for guiding the simulation by controlling the timing and occurrence of the random events. In this paper, modules of the proposed methodology, and their interactions are explained in detail. The developed methodology is used to perform risk assessment of a Space Shuttle ascent phase, and results show the effectiveness of the proposed platform.