A real-time data compression algorithm for gear fault signals

Measurement ◽  
2016 ◽  
Vol 88 ◽  
pp. 165-175 ◽  
Author(s):  
Shu Han ◽  
Xiaoming Liu ◽  
Jia Chen ◽  
Jin Wu ◽  
Xiaofei Ruan
2014 ◽  
Vol 519-520 ◽  
pp. 70-73 ◽  
Author(s):  
Jing Bai ◽  
Tie Cheng Pu

Aiming at storing and transmitting the real time data of energy management system in the industrial production, an online data compression technique is proposed. Firstly, the auto regression model of a group of sequence is established. Secondly, the next sampled data can be predicted by the model. If the estimated error is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied in electricity energy data compression of a beer production. The application result verifies the effectiveness of the proposed method.


Author(s):  
Fang Zhang ◽  
Lin Cheng ◽  
Xiong Li ◽  
Yuanzhang Sun ◽  
Wenzhong Gao ◽  
...  

1992 ◽  
Author(s):  
Shen-en Qian ◽  
Ruqin Wang ◽  
Shuqiu Li ◽  
Yisong Dai

2012 ◽  
Vol 430-432 ◽  
pp. 1298-1301
Author(s):  
Xiao Jian Zheng

Most existing real-time data compressing algorithms are focused on dynamic and inconstancy of the process data, but a basic observation is ignored with some unexpectedness: on condition that sampling interval is not large, difference between amplitudes of real-time process data from two neighboring samples is relatively small, and most of data amplitudes are in the same range. In this paper we propose a compression algorithm based on the observation and experimentally evaluate the proposed approach and demonstrate that our algorithm is promising and efficient.


2014 ◽  
Vol 104 (11) ◽  
pp. 111101 ◽  
Author(s):  
Mohammad H. Asghari ◽  
Bahram Jalali

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