Universal Random Vibration Nomograph

Noise Control ◽  
1961 ◽  
Vol 7 (5) ◽  
pp. 27-28
Author(s):  
Harry Himelblau ◽  
Ben West
Keyword(s):  
Random Vibration

Evaluation of flight equipment acceleration caused by random vibration loads

AIAA Journal ◽  
2001 ◽  
Vol 39 ◽  
pp. 2001-1758
Author(s):  
Romualdo Ruotolo ◽  
Massimiliano Cotterchio
Keyword(s):  
Random Vibration ◽  
Vibration Loads

Improving the efficiency of cyclic devices for collecting and processing information in monitoring and vibration diagnostics systems

2017 ◽  
Vol 11 (1) ◽  
pp. 81-94 ◽  
Author(s):  
A. P. Moroz ◽  
T. S. Abbasova ◽  
M. E. Stavrovsky
Keyword(s):  
Random Vibration ◽  
Printed Circuit Boards ◽  
Monitoring Systems ◽  
Circuit Boards ◽  
Vibration Diagnostics ◽  
Printed Circuit ◽  
Impact Processes ◽  
Collection Device ◽  
Processing Information ◽  
Operation Characteristics

Identified the problem of increasing the efficiency of the collection device and the processing of information in telemetry monitoring systems and vibration diagnostics. The possibilities of CAD-programs for the calculation of vibro-impact processes and random vibration devices and printed circuit boards. A study of the frequency and random vibration study on the example of the PCB in SolidWorks environment. Abstract purpose, principle of operation, characteristics of the onboard telemetry information «Pyrite» system for measuring the parameters of aircraft, which are characterized by different phases and the duration of the flight telemetered site; It shows that the «Pyrite» equipment can be effectively used for the integrated telemetry information slowly evolving processes and rapidly changing processes of aircraft.

An Equivalent Shape-preserving Clipping Method for the Control Spectrum to Avoid Over-testing of Triaxial Random Vibration

2021 ◽  
pp. 116060
Author(s):  
Jin Bai ◽  
Yuanying Qiu ◽  
Jing Li ◽  
Haidong Wang ◽  
Zhaoxi Wang
Keyword(s):  
Random Vibration ◽  
Shape Preserving ◽  
Control Spectrum

scholarly journals A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1181
Author(s):  
Chenhao Zhu ◽  
Sheng Cai ◽  
Yifan Yang ◽  
Wei Xu ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Error Compensation ◽  
Random Vibration ◽  
Short Term Memory ◽  
Combined Method ◽  
Short Term ◽  
Mems Gyroscope ◽  
Long Short Term Memory ◽  
Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

Random Vibration of Composite Saddle Membrane Structure under the Impact Loading

2021 ◽  
pp. 114020
Author(s):  
Changjiang Liu ◽  
Haibing Xie ◽  
Xiaowei Deng ◽  
Jian Liu ◽  
Mengfei Wang ◽  
...  
Keyword(s):  
Impact Loading ◽  
Membrane Structure ◽  
Random Vibration ◽  
The Impact
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