Abstract
With the increasing development of industrial technology, fault detection technology has been widely applied in many complex systems. Aiming at the problems of multi-operation conditions and the data length needed for fault detection using Kullback- Leibler Divergence (KLD), a novel fault detection method for complex systems based on optimized KLD under multi-operation conditions is proposed in this paper. In the first place, based on the historical data of the system, the complex operation conditions of the system are divided into several simple mutually exclusive operation conditions, and the division standard is established. In the next place, the optimal length can be quickly determined by autocorrelation analysis and applied to various operation conditions. After the next, in the training stage, the KLD values of all training data are calculated with the benchmark that is the initial optimal data length of each operation condition. And the maximum value is taken as the threshold for fault detection. Afterward, in the test phase, it starts by judging the type of operation condition that the data belongs to, then the corresponding KLD value is calculated, which is compared with the corresponding threshold, so as to determine whether the fault occurs. Eventually, this method is applied to the suspension system of the maglev train and respectively compared with the fault detection methods based on Euclidean distance or Mahalanobis distance. The results show that the proposed method possesses a low false alarm rate and high sensitivity.
Highlights
Nowadays, in order to achieve more functions and meet the needs of life and production, the structure of the system is becoming more and more complex, the degree of automation is higher and higher, and the relationship between the various parts of the system is getting closer
Aiming at the above two problems, this paper proposes a fault detection method based on optimized Kullback-Leibler Divergence (KLD) for a complex system under multiple-operation conditions
Considering the significance of the suspension system to the safe and stable operation of the maglev train, the method in this paper is applied to the fault detection of the maglev train suspension system
Summary
In order to achieve more functions and meet the needs of life and production, the structure of the system is becoming more and more complex, the degree of automation is higher and higher, and the relationship between the various parts of the system is getting closer. A chain reaction is often triggered by a small fault somewhere, leading to catastrophic damage to the whole system and even the environment related to the system. It doesn’t only cause huge economic losses and endanger personal safety. It is of great significance to study fault detection technology for a complex system. There are many documents studying on fault detection, among which Kullback-Leibler Divergence (KLD)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
