Monoblock Centrifugal Pump Research Articles

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.

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.

A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump

Mono-block centrifugal pump plays a key role in various applications. Any deviation in the functions of centrifugal pump would lead to a monetary loss. Thus, it becomes very essential to avoid the economic loss due to malfunctioning of centrifugal pump. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Over the past 25years, much research has been focused on vibration based techniques. Machine learning approach is one of the most widely used techniques using vibration signals in fault diagnosis. There are set of connected activities involved in machine learning approach namely, data acquisition, feature extraction, feature selection, and feature classification. Training and testing the classifier are the two important activities in the process of feature classification. When the histogram features are used as the representative of the vibration signals, a proper guideline has not been proposed so far to choose number of bins and number of samples required to train the classifier. This paper illustrates a systematic method to choose the number of bins and the minimum number of samples required to train the classifier with statistical stability so as to get best classification accuracy. In this study, power analysis method was employed to find the minimum number of samples required and a decision tree algorithm namely J48 was used to validate the results of power analysis and to find the optimum number of bins.

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

Mono-block centrifugal pumps are widely used in a variety of applications. In many applications the role of mono-block centrifugal pump is critical and condition monitoring is essential. Vibration based continuous monitoring and analysis using machine learning approach is gaining momentum. Particularly, artificial neural networks, fuzzy logic have been employed for continuous monitoring and fault diagnosis. This paper presents the use of decision tree and rough sets to generate the rules from statistical features extracted from vibration signals under good and faulty conditions of a mono-block centrifugal pump. A fuzzy classifier is built using decision tree and rough set rules and tested using test data. The results obtained using decision tree rules and those obtained using rough set rules are compared. Finally, the accuracy of a principle component analysis based decision tree-fuzzy system is also evaluated. The study reveals that overall classification accuracy obtained by the decision tree-fuzzy hybrid system is to some extent better than the rough set-fuzzy hybrid system.

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.

Vibration based fault diagnosis of monoblock centrifugal pump using decision tree

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.