Artificial intelligence for fault diagnosis of rotating machinery: A review

Applications of machine learning to machine fault diagnosis: A review and roadmap

The Random Character of Stock Market Prices.

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Machine learning in manufacturing: advantages, challenges, and applications

Failure diagnosis using deep belief learning based health state classification

Monoblock centrifugal pumps are widely used in a variety of applications. In many applications the role of monoblock centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly artificial neural networks, fuzzy logic were employed for continuous monitoring and fault diagnosis. This paper presents the use of C4.5 decision tree algorithm for fault diagnosis through statistical feature extracted from vibration signals of good and faulty conditions.

Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

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.

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

of stock market trends has been an area of great interest both to those who wish to profit by trading stocks in the stock market and for researchers attempting to uncover the information hidden in the stock market data. Applications of data mining techniques for stock market prediction, is an area of research which has been receiving a lot of attention recently. This work presents the design and performance evaluation of a hybrid decision tree- rough set based system for predicting the next days" trend in the Bombay Stock Exchange (BSE- SENSEX). Technical indicators are used in the present study to extract features from the historical SENSEX data. C4.5 decision tree is then used to select the relevant features and a rough set based system is then used to induce rules from the extracted features. Performance of the hybrid rough set based system is compared to that of an artificial neural network based trend prediction system and a naive bayes based trend predictor. It is observed from the results that the proposed system outperforms both the neural network based system and the naive bayes based trend prediction system.

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N. R. Sakthivel

Papers

Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

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

A Decision Tree- Rough Set Hybrid System for Stock Market Trend Prediction

Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals