An online performance monitoring method for analog circuit

• Published in: IEEE International Conference on Industrial Informatics (INDIN) pp. 452 - 456
• Main Authors: Aihua Zhang, Kailun Huang, Xing Huo, Zhiqiang Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2016
• Subjects: Analog circuits; Circuit faults; Kernel; Monitoring; Numerical models; Robustness; Support vector machines
• ISSN: 2378-363X
• DOI: 10.1109/INDIN.2016.7819203

Although electronic system has entered the digital age, analog circuit is still an essential part. Therefore the performance monitoring or evaluation of analog circuit is extremely important. However some problems about analog circuit performance monitoring is being, such as data acquisition online of the industry field with uncertainty, performance monitoring timeliness. Here an online performance monitoring method for analog circuit (OPM) subject to the data uncertainty is proposed. The main idea of OPM is to employ a learning machine same as least square support vector regression (LSSVR) to train and learn the data set. Considering the data from industrial field usually hold nonlinear feature, time varying feature and contain faults value, a novel robust LSSVR (RLSSVR) is presented to detection the performance of Electronic System. Moreover, the multi-kernel function is employed which has more flexibility than the single-kernel function, and can get more support vector numbers accuracy. At the same time, the novel robust learning algorithm is employed to process the data set which is with fault values. For the robust idea, the fault value is solved via the LSSVR model iteratively weights, and then the trained RLSSVR model is updated depend on the interaction between incremental learning and decrement learning. During the interaction, the interests of the history data and the control storage data are all be considered. Numerical experiment supported by the college analog electronic experiments, adopted eight indexes of one order low power amplifier to evaluate performance. The training data set were gotten via precision instrument evaluation in two years. Simulation results reveal that proposed RLSSVR can handle the regressive deviation caused by the nonlinear feature, time varying feature and contain faults value exist in the industry processing, and has speeder than the traditional LSSVR, and WLSSVR.