Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump

TL;DR: In this article, a new transient feature extraction technique is proposed for gearbox fault diagnosis based on sparse representation in wavelet basis, which can extract both the impulse time and the period of transients.

TL;DR: A comparative study of Multilayer Feedforward Perceptron Neural Network which is trained with Back Propagation (MLP-BP) and also using hybrid training using Genetic Algorithm (GA), MLP-GABP, and Support Vector Machine (SVM) classifiers to classify the fault conditions of a centrifugal pump.

TL;DR: In this article , a detailed review of data mining techniques for structural health monitoring (SHM) applications is presented, where a brief background, models, functions, and classification of DM techniques are presented.

TL;DR: The theoretical foundations of both decision trees and random forests are reviewed and basic case-studies as well as applications from recent literature are illustrated.

TL;DR: Fault diagnostics of spur bevel gear box is treated as a pattern classification problem and the use of discrete wavelets for feature extraction and artificial neural network for classification is investigated.

TL;DR: In this paper, a discrete wavelet transform (DWT) was used to detect single and multiple bearing race faults in the ball bearings of the inner race, outer race, and the combination faults.

TL;DR: In this paper, the authors used the continuous wavelet transformation (CWT) to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD, which can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency.

TL;DR: 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.