The prediction of energy-saving electromagnetic flowmeter based on Kalman filtering method

As one of the most common used flow measuring method, the electromagnetic flowmeter are widely used in many industrial areas. Currently, the powers of most electromagnetic flowmeters are provided by various batteries. The traditional measure of electromagnetic flowmeter is a real-time and continuous process, and thus the battery is very energy-consuming. To solve this problem, we develop a measuring method of energy-saving electromagnetic flower by sampling measure instead of continuous one. To reduce the loss of original accuracy to its minimum, the Kalman filtering method is applied in a series of sampling values of time interval to approximate the original continuous measuring values. The three attributes of a group of continuous sampling values are extracted to construct the key state matrix in the Kalman filter method. The new developed electromagnetic flowmeter measure can averagely save 90% battery energy while the measuring accuracy is decreased 5% smaller than original measuring method. In principle, if the used data for Kalman filter can include all flow patterns, the approximate error to the original measuring data tends to infinitesimal. The experiments validate our proposed method in a group of typical flow patterns and verify the efficiency of the proposed energy-saving method.