Fingerprinting using Fine Timing Measurement

Ensuring the reliability of the steam turbine is the key for its long life. For this purpose monitoring systems are standardly used. Early detection of any failure can avoid possible economical and material losses. A monitoring of rotating blades vibration belongs to the very important tasks of the turbomachinery state assessment. Especially in terms of the last stages of low-pressure part, where the longest blades are vibrating at most. Commonly used methods for blade vibration monitoring are based on contact measurement using strain gauges or non-contact approach based on blade tip timing measurement. Rising demand for low-cost monitoring systems has initiated development of a new approach in blade vibration monitoring task. The presented approach is based on usage of relative rotor vibration signals. Its advantage is in using of standardly installed sensors making this approach economically interesting for the turbine operators compared to the traditionally used methods, mentioned above. This paper summarizes the symptoms of blade vibration phenomenon in relative shaft vibration signals, the impact of operating conditions on the blade vibration amplitude and its comparison to blade tip-timing measurement results. In addition of several examples, the article also describes an evaluation of proposed method in operation of steam turbine with power of 170MW.

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A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Applied Sciences ◽

10.3390/app10030956 ◽

2020 ◽

Vol 10 (3) ◽

pp. 956 ◽

Cited By ~ 5

Author(s):

Minghao Si ◽

Yunjia Wang ◽

Shenglei Xu ◽

Meng Sun ◽

Hongji Cao

Keyword(s):

Numerical Experiments ◽

New Technologies ◽

Gaussian Model ◽

Indoor Positioning ◽

Least Square ◽

Line Of Sight ◽

Positioning Accuracy ◽

Timing Measurement ◽

Positioning Method ◽

Non Line Of Sight

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

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