This paper proposes a piezoelectric energy harvester to supply power for low-consumption devices in intelligent vehicle wheels. The harvester is embedded in the spoke and possesses the ability of self-tuning due to the centrifugal force in the rotational motion. A dynamic model suitable for design is derived through the Euler-Lagrange method and verified by experiments. In order to facilitate the design of the harvester, the concept of design factor is proposed and discussed. The wheel's rotational characteristics is measured by a road test and a harvester is designed for the R16 wheel. An experimental platform with Macro Fiber Composite is built and the output power of 0.61-6.28 mW is achieved at the wheel speeds from 40 to 120 kph on real road test. The results demonstrate that the proposed piezoelectric energy harvester can be applied to establish a wireless self-sufficient intelligent vehicle wheel system.

This paper proposed a strategy of tracking pipe PIG utilizing Fiber Optic Distributed Vibration Sensor (FODVS) and YOLO object detection algorithm. Phase sensitive Optical Time Domain Reflectometer (φ-OTDR) is employed as the distributed sensor to collect the real field vibration signal generated as a result of the collision between leather bowl and weld joint. The space-time graphs of φ-OTDR are prepared to construct data sets to train a typical YOLOv3 net model. The trained model is proved to be able to accurately capture the invert-V signature in the space-time graph thus revealing the real time position of a PIG. This paper provides a concept-proof preliminary demonstration on the promising combination of FODVS and object detection scheme for pursuing better performance of events recogntion.

This letter reports the idea of extending the sensing distance of phase sensitive optical time-domain reflectometry system based on remotely pumped erbium-doped fiber (EDF) amplification. A segment of EDF is spliced in the middle of the sensing fiber with the pump power injected from the front end to realize power compensation such as a repeater. The 75-km sensing distance under single-end operating scheme and the 10-dB tail-end SNR are demonstrated. Since the method eliminates the necessity of dual-end operation and field power supply, it provides a much more practical and feasible way of realizing long-range perimeter monitoring.

Weak vibration identification is one of the key challenges in the field of pipeline safety warning using distributed fiber optic sensor. In this paper, we proposed a novel algorithm that experimentally exhibits better performance concerning weak signal recognition over conventional programs. Empirical Mode Decomposition (EMD) was employed in the method considering that the temporal and spectral attributes could be better preserved and magnified in an intrinsic form than the original signal. After extensive observation and analysis, the second and third Intrinsic Mode Function (IMF) exhibited better consistency and identifiability among samples belonging to the same and different categories. As a substitute for the original signal, IMF ₂ and IMF ₃ were jointly utilized as the research target, where a series of carefully selected parameters were applied to evaluate the feasibility of the IMF features for weak vibration identification, giving birth to our classification vector V ₂ . Combining V ₂ with certain machine learning algorithms, precise identification of noise and five typical pipeline vibration signals was achieved. A conventional vector V ₁ and a deep learning network were also provided as the control groups for two mainstream algorithms. Experimental results show that the val_accuracy of SVM- V ₂ is 7.77% higher than V ₁ . In terms of precision and recall, SVM- V ₂ exceeds V ₁ by 10.83% and 7.97%, and surpasses deep learning by 8.58% and 7.97%.