N.R. Sakthivel

Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

The shaft and bearing are the most critical components in rotating machinery. Majority of problems arise from faulty bearings in turn affect the shaft. The vibration signals are widely used to determine the condition of machine elements. The vibration signals are used to extract the features to identify the status of a machine. This paper presents the use of c-SVC and nu-SVC models of support vector machine (SVM) with four kernel functions for classification of faults using statistical features extracted from vibration signals under good and faulty conditions of rotational mechanical system. Decision tree algorithm was used to select the prominent features. These features were given as inputs for training and testing the c-SVC and nu-SVC model of SVM and their fault classification accuracies were compared.

Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm

Tool wear and tool life are the principle areas are focus in any machining activity. The production rate, surface finish of machined component and the machine condition are directly related to the tool condition. This work on tool condition monitoring delves into data mining approach to discover the hidden information available in the tool vibration signals. The use of statistical features derived from the vibration data is used as the primary feature and Principle Component Analysis (PCA) transformed statistical features are evaluated as an alternative. In order to increase the robustness of the classifier and to reduce the data processing load, feature reduction is necessary. The feature reduction using (a) decision tree and (b) feature transformation and reduction using PCA are evaluated independently and the results are compared. The effective combination of feature reducer and classifier for designing the expert system is studied and reported.

Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for fault classification of monoblock centrifugal pump

Monoblock centrifugal pumps are widely used in a variety of applications. Defects and malfunctions (faults) of these pumps result in significant economic loss. Therefore, the pumps must be under constant monitoring. When a possible fault is detected, diagnosis is carried out to pinpoint it. In many applications, the role of monoblock centrifugal pumps is critical and condition monitoring is essential. Vibration-based condition monitoring and analysis using the machine-learning approach is gaining momentum. In particular, Artificial Neural Networks (ANNs), fuzzy logic and roughsets have been employed for condition monitoring and fault diagnosis. While it is difficult to train the neural network-based fault classifier, the classification accuracy in case of fuzzy logic- and roughest-based fault classifiers is not very high. This paper presents the use of Support Vector Machines (SVMs) and Proximal Support Vector Machines (PSVMs) for classifying faults using statistical features extracted from vibration signals under good and faulty conditions of a monoblock centrifugal pump. The Decision Tree (DT) algorithm is used to select prime features. These features are fed as inputs for training and testing SVMs and PSVMs and their fault classification accuracy is compared. The results are found to be better than neural network-, fuzzy- and roughest-based methods.

Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump

Rule learning based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. Fault detection and diagnosis of various mechanical components of centrifugal pump is essential to increase the productivity and reduce the breakdowns. This paper presents the use of rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a centrifugal pump. A fuzzy inference system (FIS) is built using rough set rules and tested using test data. The effect of different types of membership functions on the FIS performance is also presented. Finally, the performance of this classifier is compared to that of a fuzzy-antminer classifier and to multi-layer perceptron (MLP) based classifiers.

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.

Use of histogram features for decision tree-based fault diagnosis of monoblock centrifugal pump

Monoblock centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Machine learning-based approach to fault detection and diagnosis is becoming very popular, mainly due to their high accuracy when compared to older statistical methods. There are set of related activities involved in machine learning approach namely, data acquisition from the monoblock centrifugal pump, feature extraction from the acquired data, feature selection, and finally feature classification. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through histogram feature extracted from vibration signals of good and faulty conditions of monoblock centrifugal pump. The performance of the proposed system is compared to that of a Naive Bayes-based system to validate the superiority of the proposed system.

Decision support system using artificial immune recognition system for fault classification of centrifugal pump

Centrifugal pumps are a crucial part of many industrial plants. Early detection of faults in pumps can increase their reliability, reduce energy consumption, service and maintenance costs, and increase their life-cycle and safety, thus resulting in a significant reduction in life-time costs. Vibration analysis is a very popular tool for condition monitoring of machinery like pumps, turbines and compressors. The proposed method is based on a novel immune inspired supervised learning algorithm which is known as artificial immune recognition system (AIRS). This paper compares the fault classification efficiency of AIRS with hybrid systems such as principle component analysis (PCA)-Naive Bayes and PCA-Bayes Net. The robustness of the proposed method is examined using its classification accuracy and kappa statistics. It is observed that the AIRS-based system outperforms the other two methods considered in the present study.

Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange

Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-days trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naive-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.

Clustering stock price time series data to generate stock trading recommendations: An empirical study

Abstract Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The users job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.

Wavelet decomposition and support vector machine for fault diagnosis of monoblock centrifugal pump

Monoblock centrifugal pumps play a very critical role in a variety of applications and condition monitoring of the various mechanical components of centrifugal pump becomes essential which in turn increases the productivity and reduces the breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks fuzzy logic was employed for continuous monitoring and fault diagnosis. This paper presents the use of support vector machine (SVM) algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of centrifugal pump. The classification accuracies were computed for different types of classifiers such as artificial neural network (ANN), support vector machine (SVM) and J48 decision tree algorithm.