Figure - available from: Shock and Vibration
This content is subject to copyright. Terms and conditions apply.
Source publication
To predict the remaining life of ball screw, a screw remaining life prediction method based on quantum genetic algorithm (QGA) and support vector machine (SVM) is proposed. A screw accelerated test bench is introduced. Accelerometers are installed to monitor the performance degradation of ball screw. Combined with wavelet packet decomposition and i...
Similar publications
![Figure 1: Hidden Markov Model [37]](publication/372487472/figure/fig1/AS:11431281175818792@1689908931298/Hidden-Markov-Model-37_Q320.jpg)
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Network...
Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersio...
The power unit of self-propelled artillery rotational shell magazine is a vulnerable part in the whole system. Aiming at the problem of magazine fault diagnosis under multiple loads, the method of motor current characteristic analysis and support vector machine is used to diagnose the fault of reducer. Through the establishment of a virtual prototy...
Opinion mining has become one of the most sought-after interests by researchers because of the advent of the internet and relevant technologies. Analyzing people's opinions and emotions or sentiment analysis is a subdomain of text classification under NLP. Feature vectorizer is a technique in sentiment analysis frequently used in machine learning a...
This paper presents a non-intrusive solenoid valve fault diagnosis method, which adopts the eigenvalue extraction scheme and combines the time-frequency analysis and time-domain analysis of the current signal through the solenoid valve fault pattern recognition method. Specifically, the research group selected the solenoid valve driving end current...
Citations
... In practice, hybrid control methods are often used to achieve better control requirements. For example, GA is employed to optimize the parameters of SVM, which can enhance the accuracy of residual life prediction and fault diagnosis in BSFDS [211,214,226]. Fuzzy control and ANN are often used to deal with unknown nonlinearities and disturbances, as well as to identify and control complex systems. ...
Ball screw feed-drive system (BSFDS) is the precision transmission mechanism widely used in micron-scale positioning or motion trajectory control. Its desired specifications including high acceleration, speed, accuracy, and stability are challenged by vibration, friction, thermal error, uncertainty, etc. Inspired by these challenges, the modeling and control issues have been widely studied and discussed for decades. This paper presents an overview of modeling and control approaches, including identification, linear parameter varying, thermal error modeling and control, nonlinear control, and robust control. In particular, it reviews the emerging control issues and approaches, such as artificial intelligence, learning control, and data-driven control, which have increased in recent years.
... Accordingly, SVR is often adopted as the learning model in studies of lithium batteries [30][31][32]. Since the performance of SVR highly relies on the selection of model parameters especially the kernel parameters, many intelligent algorithms like genetic algorithm (GA) [33,34] and PSO [9,35] are used to optimize the SVR model. Compared with GA, the PSO has faster convergence speed [36]. ...
Estimation of remaining capacity is essential for ensuring the safety and reliability of lithium-ion batteries. In actual operation, batteries are seldom fully discharged. For a constant current-constant voltage charging mode, the incomplete discharging process affects not only the initial state but also processed variables of the subsequent charging profile, thereby mainly limiting the applications of many feature-based capacity estimation methods which rely on a whole cycling process. Since the charging information of the constant voltage profile can be completely saved whether the battery is fully discharged or not, a geometrical feature of the constant voltage charging profile is extracted to be a new aging feature of lithium-ion batteries under the incomplete discharging situation in this work. By introducing the quantum computing theory into the classical machine learning technique, an integrated quantum particle swarm optimization–based support vector regression estimation framework, as well as its application to characterize the relationship between extracted feature and battery remaining capacity, are presented and illustrated in detail. With the lithium-ion battery data provided by NASA, experiment and comparison results demonstrate the effectiveness, accuracy, and superiority of the proposed battery capacity estimation framework for the not entirely discharged condition.
