Application of Information-Geometric Support Vector Machine on Fault Diagnosis of Hydraulic Pump

The growing demand for the safety and reliability in industries triggers the development of condition monitoring and fault diagnosis technologies. Hydraulic pump is the critical part of a hydraulic system. The diagnosis of hydraulic pump is very crucial for reliability. This paper presents a method based on information-geometric support vector machine (IG-SVM), which is employed for fault diagnosis of hydraulic pump. The IG-SVM, which uses information geometry to modify SVM, improves the performance in a data dependent way. To diagnose faults of hydraulic pump, a residual error generator is designed based on the IG-SVM. This residual error generator is firstly trained using data from normal state. Then, it can be used for fault clustering by analysis of the residual error. Its feasibility and efficiency has also been validated via a plunger pump test-bed

Support Vector Machine Approach for Non-Technical Losses Identification in Power Distribution Systems

Electricity consumer fraud is a problem faced by all power utilities. Finding efficient measurements for detecting fraudulent electricity consumption has been an active research area in recent years. In this paper,the approach towards nontechnical loss (NTL) detection in power utilities using an artificial intelligence based technique, Support Vector Machine (SVM), are presented. This approach provides a method of data mining, which involves feature extraction from past consumption data. This SVM based approach uses customer load profile information and additional attributes to expose abnormal behavior that is known to be highly correlated with NTL activities. Some key advantages of SVM in data clustering, among which is the easy way of using them to fit the data of a wide range of features are discussed here. Finally, some major weakness of using SVM in clustering for NTL identification are identified, which leads to motivate for the scope of Optimum-Path Forest, a new model of NTL identification

Exploring a Hybrid Algorithm for Price Volatility Prediction of Bitcoin

In recent years, the Bitcoin investment market has become increasingly popular. We collected existing literature on Bitcoin and found that predictions about the role of Bitcoin in investment portfolios and the volatility of Bitcoin price as well as return have become advanced research topics. This study shows our current work on the prediction of Bitcoin price volatility and proposes an idea for predicting the price volatility. We have designed an experiment that compares different combinations of machine learning algorithms with GARCH-type models, intending to compare the effects of these models in the prediction of Bitcoin time series and finally implement an optimized algorithm

Chaotic information-geometric support vector machine and its application to fault diagnosis of hydraulic pumps

Fault diagnosis of rotating machineries is becoming important because of the complexity of modern industrial systems and the increasing demands for quality, cost efficiency, reliability, and safety. In this study, an information-geometric support vector machine used in conjunction with chaos theory (chaotic IG-SVM) is presented and applied to practical fault diagnosis of hydraulic pumps, which are critical components of aircraft. First, the phase-space reconstruction of chaos theory is used to determine the dimensions of input vectors for IG-SVM, which uses information geometry to modify SVM and improves performance in a data-dependent manner without prior knowledge or manual intervention. Chaotic IG-SVM is trained by using the dataset from the normal state without fault, and a residual error generator is then designed to detect failures based on the trained chaotic IG-SVM. Failures can be diagnosed by analyzing residual error. Chaotic IG-SVM can then be used for fault clustering by analyzing residual error. Finally, two case studies are presented, and the performance and effectiveness of the proposed method are validated

Control Flow Graph Based Multiclass Malware Detection Using Bi-normal Separation

Control flow graphs (CFG) and OpCodes extracted from disassembled executable files are widely used for malware detection. Most of the research in static analysis is focused on binary class malware detection which only classifies an executable as benign or malware. To overcome this issue, CFG based multiclass malware detection system that automatically classifies the malware into their respective families is proposed. The use Bi-normal separation (BNS) as a feature scoring metric. Experimental results show that proposed method using BNS outperforms compared to hitherto use technique of document Frequency for multiclass metamorphic malware detection and achieves detection accuracy of 99.5 per cent.