Real-time leak detection for natural gas gathering pipelines is critical to guarantee the safe transportation of energy in the source of production. Since leak samples are scarce in real pipelines, modeling healthy data becomes one of the mainstream solutions. However, due to the lack of prior knowledge of leaks, more effective feature learning for multivariate time series (MTS) of pipeline data is a prerequisite for reliable leak detection. Moreover, in-service gathering pipelines usually involve multiple operating conditions (MOCs), where the leak characteristics of MOCs could be significantly different. This poses a serious challenge to accurate leak detection. To address the abovementioned problems, we propose a hybrid leak detection method for gathering pipelines under MOCs. First, anomaly processing and long–short sequence construction are designed to provide high-quality data for leak detection. Then, reconstruction- and prediction-one-dimensional convolutional long short-term memory autoencoder is proposed to enhance the feature learning for MTS. Based on the learned features, a multimodel decision scheme with one-class support vector machine is developed to deal with leak detection under MOCs. Finally, experimental results on supervisory control and data acquisition data obtained from real-world natural gas gathering pipelines demonstrate the effectiveness of the proposed method.

We report a hollow type piezoelectric micromotor made of a PZT ceramic/metal composite cylinder with sizes of only 3.6 mm in diameter and 3.0 mm in length aiming at medical endoscope application. The hollow piezoelectric stator of the micromotor, acting as a nut, can excite E₀₂-mode traveling wave along its circumferential direction, and a hollow rotor with a fine lens inside, acting as a screw, is driven to produce a spiral motion along its axis direction inside the hollow stator via the traveling wave. The features of the developed micromotors are its hollow, fine structure and submicrometer step resolution, ensuring that the optical path passes in a narrow and limited space and that the optical focal length is tuned precisely inside the endoscope, which is meaningful in medical diagnosis.

Ship trajectory length prediction is vital for intelligent traffic signaling in the controlled waterways of the Yangtze River. In current intelligent traffic signaling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River, which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships' travel trajectory length based on historical data. In this paper, ships' historical trajectories are first grouped by using the fuzzy c-means clustering algorithm. The relationship between some known factors (i.e., ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type, and water level) and the resultant memberships are then modeled using artificial neural networks. The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers' length. To the best of our knowledge, this is the first time to predict the overall trajectory length of manually controlled ships. The experimental results show that the proposed method can reduce the probability of generating incorrect traffic control signals by 74.68% over existing ITSSs. This will significantly improve the efficiency of the Yangtze River traffic management system and increase the traffic capacity by reducing the traveling time.

Due to the abundant operation data (e.g., pressure, flow rate, and temperature) for natural gas (NG) gathering pipelines provided by the supervisory control and data acquisition (SCADA) system, the machine-learning-based real-time flow prediction has become a critical solution to enable the identification of the abnormality of pipelines, further to guarantee the safe operation of the pipelines. However, traditional machine-learning-based methods cannot always function well due to the temporal characteristics of the SCADA data often being ignored, resulting from a lack of time memory capability. Therefore, this paper proposes a method to automatically perform the feature mining of flow time series by considering the correlation of flow data at both ends of the pipeline, combined with the long short-term memory (LSTM) network. The current and historical data at both pipeline ends are used as input vectors of the LSTM network to predict the terminal output flow at the next moment. Furthermore, to solve the problem that the parameters of the LSTM model are configured with manual experience, a grid search algorithm (GSA) is introduced to optimize the parameters of the LSTM. Consequently, the effectiveness and superiority of the proposed method are carried out in a real-world NG gathering pipeline.