scholarly journals Fault Detection through Vibration Signal Analysis based on HSM with TRIPPY Classifier

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Fault Detection ◽  
Vibration Signal ◽  
Crest Factor ◽  
Detection Model ◽  
Rotating Machine ◽  
Good Path ◽  
Signal Features ◽  
Vibration Signal Analysis ◽  
Learned Features

A proficient fault detection model has to be sketched for detecting slight variations of the vibrating signal of rotating machine whereas the diagnosis process prominently stuck with the inefficient extraction of effectual features of a signal in reduced time. Existence of above stated hilarious issue results in the confinement of inventive Harmonized Swan Machine (HSM) based on the stochastic characteristics of swan, which could collect the RKC (RMS, Kurtosis, Crest factor) signal features for every instantaneous signal unit which eliminates noise thereby reducing pre-processing task which in turn lessens time consumption and at the end yields learned extracted faulty features. Accurate classification of faulty features can be accomplished by casting inimitable Trippy classifier which is designed based on selective predictive character of trippy fish which provokes a good path to provide accurate classification based on learned features. This responsible classifier collectively organises the RKC features of respective signal units and do accurate classification of faulty occurrences based on the features in less time.

scholarly journals Bearing Condition Recognition and Degradation Assessment under Varying Running Conditions Using NPE and SOM

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shaohui Zhang ◽  
Weihua Li
Keyword(s):  
Feature Space ◽  
Vibration Signal ◽  
Fault Classification ◽  
Self Organizing Map ◽  
Performance Simulation ◽  
Rotating Machine ◽  
Time Domain Signal ◽  
Machine Condition Monitoring ◽  
Signal Features ◽  
Simulation And Experiment

Manifold learning methods have been widely used in machine condition monitoring and fault diagnosis. However, the results reported in these studies focus on the machine faults under stable loading and rotational speeds, which cannot interpret the practical machine running. Rotating machine is always running under variable speeds and loading, which makes the vibration signal more complicated. To address such concern, the NPE (neighborhood preserving embedding) is applied for bearing fault classification. Compared with other algorithms (PCA, LPP, LDA, and ISOP), the NPE performs well in feature extraction. Since the traditional time domain signal denoising is time consuming and memory consuming, we denoise the signal features directly in feature space. Furthermore, NPE and SOM (self-organizing map) are combined to assess the bearing degradation performance. Simulation and experiment results validate the effectiveness of the proposed method.

Health Monitoring of a Gear Box Using Vibration Signal Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 1012-1017 ◽  
Author(s):  
M.R. Praveen ◽  
M. Saimurugan
Keyword(s):  
Frequency Domain ◽  
Health Monitoring ◽  
Crucial Role ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Nearest Neighbours ◽  
Gear Box ◽  
Vibration Signal Analysis

A gear plays a crucial role in the performance of a gear box. The faults in a gear reduces the gear life and if problem arises in shaft it affects bearing. Gear box is finally affected due to these faults. Vibration signals carries information about condition of a gear box which are captured using piezoelectric accelerometer. In this paper, features are extracted and classified using K nearest neighbours (KNN) algorithms for both time and frequency domain. The effectiveness of KNN in classification of gear faults for both time and frequency domain is discussed and compared.

A Gear Chain Fault Detection Method Using an Adaptive Interference Canceling

2007 ◽  
Vol 345-346 ◽  
pp. 1303-1306
Author(s):  
Bum Won Bae ◽  
In Pil Kang ◽  
Yeon Sun Choi
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Detection Method ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Vibration Signal Analysis ◽  
Processing Techniques ◽  
Signal Processing Techniques

A fault diagnosis method based on wavelet and adaptive interference canceling is presented for the identification of a damaged gear tooth. A damaged tooth of a certain gear chain generates impulsive signals that could be informative to fault detections. Many publications are available not only for the impulsive vibration signal analysis but the application of signal processing techniques to the impulsive signal detections. However, most of the studies about the gear fault detection using the impulsive vibration signals of a driving gear chain are limited to the verification of damage existence on a gear pair. Requirements for more advanced method locating damaged tooth in a driving gear chain should be a motivation of further studies. In this work an adaptive interference canceling combined with wavelet method is used for a successful identification of the damaged tooth location. An application of the wavelet technique provides a superior resolution for the damage detection to the traditional frequency spectrum based methods. An analysis and experiment with three pair gear chain show the feasibility of this study yielding a precise location of the damaged gear tooth.

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