Block-wise recursive APES aided with frequency-squeezing postprocessing and the application in online analysis of vibration monitoring signals

Abstract A recent advent has been seen in the usage of Internet of things (IoT) for autonomous devices for exchange of data. A large number of transformers are required to distribute the power over a wide area. To ensure the normal operation of transformer, live detection and fault diagnosis methods of power transformers are studied. This article presents an IoT-based approach for condition monitoring and controlling a large number of distribution transformers utilized in a power distribution network. In this article, the vibration analysis method is used to carry out the research. The results show that the accuracy of the improved diagnosis algorithm is 99.01, 100, and 100% for normal, aging, and fault transformers. The system designed in this article can effectively monitor the healthy operation of power transformers in remote and real-time. The safety, stability, and reliability of transformer operation are improved.

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A Bilateral Second-order Synchrosqueezing Transform and Application to Vibration Monitoring of Aerospace Engine

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3054010 ◽

2021 ◽

pp. 1-1

Author(s):

Zhenyi Chen ◽

Yanyang Zi ◽

Zhongmin Xiao ◽

Yu Wang ◽

Shun Qing

Keyword(s):

Second Order ◽

Vibration Monitoring ◽

Synchrosqueezing Transform

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Selective Separation of Amines from Continuous Processes using Automated pH Controlled Extraction

Reaction Chemistry & Engineering ◽

10.1039/d1re00205h ◽

2021 ◽

Author(s):

Luke Power ◽

Adam D. Clayton ◽

William Reynolds ◽

David Hose ◽

Caroline Ainsworth ◽

...

Keyword(s):

Liquid Extraction ◽

Selective Separation ◽

Continuous Processing ◽

Online Analysis ◽

Continuous Processes ◽

Liquid Liquid Extraction ◽

Ph Controlled

We present a rapid continuous processing methodology to screen for the optimal, selective, liquid-liquid extraction conditions, from a typical post-reaction mixture of amines, using both inline and online analysis to...

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Railway crossing vertical vibration response prediction using a data-driven neuro-fuzzy model – Influence of train factors

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/0954409720986666 ◽

2021 ◽

pp. 095440972098666

Author(s):

Kaveh Mehrzad ◽

Shervan Ataei

Keyword(s):

Model Comparison ◽

Selection Process ◽

Fuzzy Model ◽

Response Prediction ◽

Vibration Response ◽

Vibration Monitoring ◽

Data Driven ◽

Steady Increase ◽

Neuro Fuzzy ◽

Railway Crossing

This paper provides a data-driven model of the vibration response of a railway crossing during vehicle passages. Many of the features of trains passing through instrumented crossing are extracted from measured data. Based on the feature selection process, speed, dynamic axle load and the number of wagons are found proper inputs in the prediction model. Train-crossing interaction response at a crossing due to passing trains is modeled from a data-driven Neuro-Fuzzy soft computing approach. Locally Linear Model Tree (LOLIMOT) is applied to predict the crossing nose acceleration. The model comparison against measurements shows that the ability to predict the extrapolation cases at off-range speeds has satisfactory compatibility. The monitored passing trains are ranked based on the LOLIMOT input space dimension cuts and extrapolation of the model up to higher train speeds. The influence of train factors (i.e. speed, dynamic axle load, number of wagons) on crossing response is demonstrated. Also, based on the analysis results, it is concluded that with a steady increase in train speeds, some trains show a greater amplification in vibration response than others. The results can be applied in data processing in the crossing vibration monitoring and detection of trains with crossing impact sensitive to speed increasing that can lead to proper operation policies to reduce damages and maintenance costs.

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