Dynamic calibration for multi-beam bathymetric system based on characteristic data processing
The geometric installation error of the multi-beam bathymetric system will affect measure accuracy of the depth and location, which must be faced and resolved in the engineering survey urgently. The most important three kinds of the installation error are roll error, pitch error and yaw error. Through quantitative analysis of impact of installation error to the measure result, a dynamic calibration method has been proposed in the paper. In the method, installation errors can be obtained by analyzing bathymetric result from the characteristic terrain seafloor and the errors will be used as the parameters to calibrate other bathymetric result. Validity of the method has been proved by processing the measure data from Songhua Lake test.
The methods correcting roll and pitch biases of multi-beam bathymetric system
Compared with the traditional single beam system, multi-beam bathymetric system measuring water depth in the form of belt can adjust parameters such as real time velocity of sound and vessel posture. The pitch and roll biases during transducer installing are important error sources in the bathymetric system. The effects of these two parameters on precision of bathymetry have been analyzed. According to the error properties in terrain profile, parameter testing methods by both real time measurement and overlaid profile are discussed, especially, a visible operating interface for overlaid profile is designed for the convenience in adjusting field and indoor parameters. The pitch and roll biases obtained from testing this multi-beam system are used to adjust real time water depth. In addition, field data can also be reprocessed in indoor data processing based on parameter corrections to improve data quality.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.