Application of standalone system and hybrid system for fault diagnosis of centrifugal pump using time domain signals and statistical features

2012 ◽  
Vol 4 (1) ◽  
pp. 74
Author(s):  
N.R. Sakthivel ◽  
Binoy B. Nair ◽  
V. Sugumaran ◽  
Rajakumar S. Roy
Keyword(s):  
Fault Diagnosis ◽  
Centrifugal Pump ◽  
Hybrid System ◽  
Time Domain ◽  
Statistical Features

scholarly journals Vibration Analysis of Gearbox Fault Diagnosis using DWT and Statistical Features

2021 ◽  
Author(s):  
Setti Suresh ◽  
◽  
Srinivas M ◽  
Naidu VPS ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Discrete Wavelet Transform ◽  
Time Domain ◽  
Vibration Analysis ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Statistical Features ◽  
Reconstructed Signal

The gearbox is one of the critical subsystems in any rotating machinery, which plays a significant role in machine-driven power transmission in terms of change in speed and torque. It plays a vital role in patching different industrial functionalities. The advent of developing different gear technologies and the requirement to fulfil the desired mechanical benefits lead to add more importance to the gearbox health condition monitoring from various types of fault occurrences at an earlier stage. This study presents the vibration analysis of gearbox fault diagnosis using discrete wavelet transform (DWT) and statistical features. It is observed that, using wavelet reconstruction in the fault diagnosis better fault classification is achieved. The fault diagnosis has presented with an emphasis in time-domain followed by two different approaches. The approach-1 is illustrated as windowing of raw signal, feature extraction and feature classification using support vector machine (SVM). In the approach-2, after windowing the raw signal - each window of original vibration signal has converted into wavelet coefficients reconstructed signals (without leaving the time domain) using discrete wavelet transform (DWT) at different levels of decomposition followed by approach-1. The fault diagnostic accuracy of SVM is presented with 100 Monte-Carlo -runs to validate the consistency in the accomplished result. By observing the success rates in two approaches, it is clear that approach-2 with wavelet coefficient’s reconstructed signal is providing better classification accuracy, which can be practically deployed to diagnose the gearbox fault.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

Pipeline Leak Detection by Using Time-Domain Statistical Features

2017 ◽  
Vol 17 (19) ◽  
pp. 6431-6442 ◽  
Author(s):  
Fang Wang ◽  
Weiguo Lin ◽  
Zheng Liu ◽  
Shuochen Wu ◽  
Xiaobo Qiu
Keyword(s):  
Time Domain ◽  
Leak Detection ◽  
Statistical Features

scholarly journals A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

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