scholarly journals A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiafei Long ◽  
Ping Yang ◽  
Hongxia Guo ◽  
Zhuoli Zhao ◽  
Xiwen Wu
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Wind Turbines ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Wind Turbine Gearbox ◽  
Operational Conditions ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.

scholarly journals Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Sheng Fu ◽  
Kun Liu ◽  
Yonggang Xu ◽  
Yi Liu
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Early Stage ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Rolling Bearing ◽  
Domain Analysis ◽  
Data Matrix ◽  
Adaptive Fuzzy ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzyC-means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adaptive fuzzyC-means clustering, the optimal clustering number can be gotten then to recognize different fault types. Moreover, five parameters, including variance, RMS, kurtosis, skewness, and crest factor, of the nine are selected as the new eigenvector matrix to be clustered for more optimal clustering performance. The test results demonstrate that the proposed approach has a sensitive reflection towards fault identifications, including slight fault.

Fault Diagnosis of Hoist Gearbox Based on Time-Domain Analysis of EMD and Fuzzy Clustering

2011 ◽  
Vol 328-330 ◽  
pp. 1717-1720
Author(s):  
Zi Gui Li ◽  
Bi Juan Yan
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Intrinsic Mode Functions ◽  
Normal Surface ◽  
Surface Fatigue ◽  
Mode Decomposition ◽  
The Time Domain

In this paper, the method of combining the time-domain analysis of empirical mode decomposition (EMD) and fuzzy clustering is explored for the hoist gearbox fault diagnosis. Firstly, it adopts the EMD technique to decompose the signal of vibration. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMFs). Then a number of IMFs containing main fault information were selected, from which time domain feature parameters-- variance and kurtosis coefficient were extracted. At last, fuzzy clustering is used to diagnose and identify the kind of fault. The numerical simulation and the analysis of the response signal data from the hoist gearbox show that the method is effective at discriminating the three condition of the gear, i.e. the normal, surface fatigue pitting and cracked tooth.

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