Experimental application of nonlinear minimum variance estimation for fault detection systems

Abstract

The purpose of this paper is to present an experimental design and application of a novel model-based fault detection technique by using a nonlinear minimum variance (NMV) estimator. The NMV estimation technique is used to generate a residual signal which is then used to detect faults in the system. The main advantage of the approach is the simplicity of the nonlinear estimator theory and the straightforward structure of the resulting solution. The proposed method is implemented and validated experimentally on DC servo system. Experimental results demonstrate that the technique can produce acceptable performance in terms of fault detection and false alarm.

The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network

“…With the development of automation and wireless communication technology, data acquisition, data processing, data transmission, data control and data storage are not only convenient and fast, but also safe and reliable in a complicated network [1,2]. Combining distributed computing, computer science, automatic control theory, wireless sensor, and microelectronics manufacturing, intelligent network is a kind of large-scale distributed network systems, which is the integration of data-aware, intelligent learning, dynamic optimization, and wireless data communication [3,4]. There are many kinds of intelligent networks, which mainly includes wireless sensor network, cognitive network sensing, cognitive radio network, cognitive radar, smart grid, multi-robot network and so on [5].…”