Selection of discrete wavelets for fault diagnosis of monoblock centrifugal pump using the j48 algorithm
TL;DR: The use of the J48 algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of a centrifugal pump is presented.
Abstract: Monoblock centrifugal pumps play an important role in a variety of engineering applications such as in the food industry, in wastewater treatment plants, in agriculture, in the oil and gas industry, in the paper and pulp industry, and others. Condition monitoring of the various mechanical components of centrifugal pumps becomes essential for increasing productivity and reducing the number of breakdowns. Vibration-based continuous monitoring and analysis using machine learning approaches are gaining momentum. Particularly, artificial neural networks and fuzzy logic have been employed for continuous monitoring and fault diagnosis. This article presents the use of the J48 algorithm for fault diagnosis through discrete wavelet features extracted from vibration signals of good and faulty conditions of the components of a centrifugal pump. The classification accuracies of different discrete wavelet families were calculated and compared in order to find the best wavelet for the fault diagnosis of the centrifugal pump.
Citations
More filters
Journal Article•
9,185 citations
01 Jan 1987
TL;DR: Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc.
Abstract: Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc [1-4]. These are becoming more critical due to the increasing concern and rapid development in electric and hybrid vehicles, which tend to exhibit different vehicle dynamics/stability and control characteristics when compared to conventional vehicles.
258 citations
TL;DR: In this article, support vector machine (SVM) and artificial neural networks were employed for continuous monitoring and fault diagnosis of monoblock centrifugal pump in order to reduce the unnecessary break downs.
96 citations
TL;DR: Vibration signals are used for fault diagnosis of centrifugal pumps using wavelet analysis and the results are presented in the form of confusion matrix which shows the classification capability of wavelet features with rough set and fuzzy logic for Fault diagnosis of monoblock centrifugal pump.
73 citations
TL;DR: In this paper, four advanced artificial intelligence models, namely, Naive Bayes (NB), multilayer perceptron (MLP), kernel logistic regression (KLR), and J48-bagging methods, were applied and compared.
Abstract: This study assesses the landslide susceptibility of the Youfang area, China. For this purpose, four advanced artificial intelligence models, namely, Naive Bayes (NB), multilayer perceptron (MLP), kernel logistic regression (KLR), and J48-bagging methods, were applied and compared. The relationship between landslides happening and landslide conditioning factors which include: slope, aspect, altitude, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), landuse, lithology, distance to faults, distance to roads, distance to rivers, and rainfall were analyzed by the frequency ratios method. These results indicated that MLP model exhibits the most stable and reasonable result, and the resultant landslide susceptibility maps are a useful tool for local government managers and policy planners for this study area and other areas.
28 citations
Cites background from "Selection of discrete wavelets for ..."
...The disadvantage of ID3 algorithm is that it tends to be an attribute with a large number of values (Muralidharan and Sugumaran 2013)....
[...]
...The disadvantage of ID3 algorithm is that it tends to be an attribute with a large number of values (Muralidharan and Sugumaran 2013)....
[...]
References
More filters
Book•
15 Oct 1992TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Abstract: From the Publisher:
Classifier systems play a major role in machine learning and knowledge-based systems, and Ross Quinlan's work on ID3 and C4.5 is widely acknowledged to have made some of the most significant contributions to their development. This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use , the source code (about 8,800 lines), and implementation notes. The source code and sample datasets are also available on a 3.5-inch floppy diskette for a Sun workstation.
C4.5 starts with large sets of cases belonging to known classes. The cases, described by any mixture of nominal and numeric properties, are scrutinized for patterns that allow the classes to be reliably discriminated. These patterns are then expressed as models, in the form of decision trees or sets of if-then rules, that can be used to classify new cases, with emphasis on making the models understandable as well as accurate. The system has been applied successfully to tasks involving tens of thousands of cases described by hundreds of properties. The book starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting. Advantages and disadvantages of the C4.5 approach are discussed and illustrated with several case studies.
This book and software should be of interest to developers of classification-based intelligent systems and to students in machine learning and expert systems courses.
21,674 citations
Book•
25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
20,196 citations
Journal Article•
9,185 citations
"Selection of discrete wavelets for ..." refers methods in this paper
...The J48 algorithm—a Waikato Environment for Knowledge Analysis (WEKA) implementation of the C4.5 algorithm—is widely used to construct decision trees (Quinlan 1996; Witten and Frank 2005)....
[...]
01 Jan 1994
TL;DR: In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Abstract: Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students.
8,046 citations
"Selection of discrete wavelets for ..." refers background in this paper
...can refer to Quinlan (1993) to learn in detail about what the parameters and their values mean....
[...]
...Readers can refer to Quinlan (1993) to learn in detail about what the parameters and their values mean....
[...]
TL;DR: A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes with an MDL-inspired penalty, leading to smaller decision trees with higher predictive accuracies.
Abstract: A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modifications lead to smaller decision trees with higher predictive accuracies. Results also confirm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.
1,832 citations
"Selection of discrete wavelets for ..." refers methods in this paper
...The J48 algorithm—a Waikato Environment for Knowledge Analysis (WEKA) implementation of the C4.5 algorithm—is widely used to construct decision trees (Quinlan 1996; Witten and Frank 2005)....
[...]