Application of Morphological Filtering and Dynamic Time Warping in Fault Diagnosis of Complex System

2014 ◽  
Vol 7 (11) ◽  
pp. 269-276
Author(s):  
Han Li ◽  
Chengli Xie
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Morphological Filtering ◽  
Dynamic Time

Research on a feature extraction method for local faults in planetary gearboxes based on improved dynamic time warping

2021 ◽  
Vol 63 (8) ◽  
pp. 465-471
Author(s):  
Shang Zhiwu ◽  
Yu Yan ◽  
Geng Rui ◽  
Gao Maosheng ◽  
Li Wanxiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Extraction Method ◽  
Dynamic Time Warping ◽  
Spectral Feature ◽  
Time Warping ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Dynamic Time

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Time-Domain Fault Diagnosis Method of Mechanical and Electrical Equipment Based Improved Dynamic Time Wraping

2016 ◽  
Vol 693 ◽  
pp. 1539-1544 ◽  
Author(s):  
Zhi Wu Shang ◽  
Zhen Wu Liu ◽  
Ya Feng Li ◽  
Tai Yong Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Statistical Parameter ◽  
Electrical Equipment ◽  
Time Warping ◽  
Trend Prediction ◽  
Signal Characteristics ◽  
Dynamic Time

Dynamic time warping used in speech recognition widely was migrated to fault feature extraction and diagnosis in time domain. Integration of phase compensation, slope weighted, derivative, sliding window connection, fast dynamic time planning method is applied to dynamic time warping method. And a new method of time-domain signal feature extraction and fault diagnostic based on improved dynamic time warping method of mechanical and electrical equipment was proposed. Identification and localization of fault signal characteristics may be done by improving dynamic time warping method to obtain a residual signal sequences with fault characterized sidebands and selecting the statistical characteristic parameters such as peak, RMS, kurtosis spectrum to complete identification and localization of fault signal characteristics. New time-domain fault trend prediction method of mechanical and electrical equipment was established based on new statistical parameter Thikat. A new idea and target was provided for fault diagnosis of mechanical and electrical equipment.

scholarly journals Fault Diagnosis Using Dynamic Time Warping

2007 ◽  
pp. 57-66 ◽  
Author(s):  
Rajshekhar ◽  
Ankur Gupta ◽  
A. N. Samanta ◽  
B. D. Kulkarni ◽  
V. K. Jayaraman
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time

Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis

2018 ◽  
Vol 41 (7) ◽  
pp. 1923-1932 ◽  
Author(s):  
Prem Shankar Kumar ◽  
Lakshmi Annamalai Kumaraswamidhas ◽  
Swarup Kumar Laha
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Rolling Element Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Warping ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Element Bearing ◽  
Dynamic Time

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.

Fault state recognition of wind turbine gearbox based on generalized multi-scale dynamic time warping

2020 ◽  
pp. 147592172097862
Author(s):  
Bin Pang ◽  
Tian Tian ◽  
Gui-Ji Tang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Dynamic Time Warping ◽  
Operating Efficiency ◽  
Maintenance Cost ◽  
Time Warping ◽  
Wind Turbine Gearbox ◽  
Multi Scale ◽  
Fault State ◽  
Dynamic Time

Fault diagnosis of wind turbine gearbox is significant to ensure the operating efficiency and reduce the maintenance cost of wind farms. The key to achieve an accurate fault diagnosis is to extract the evidence of fault state identification effectively. Dynamic time warping has been widely used as a classifier for automatic pattern recognition as the dynamic time warping distance can indicate the similarity between two data sequences. The similarity between the analyzed signal and the template signal can be an evidence for characterizing the fault types of the analyzed signal; a generalized multi-scale dynamic time warping algorithm was accordingly developed in this article to quantitatively evaluate the condition information of wind turbine gearbox by calculating the generalized multi-scale dynamic time warping distances between the template signal (i.e. the vibration signal of wind turbine gearbox in normal state) and the testing signal (i.e. the vibration signals of wind turbine gearbox to be analyzed). Then, the sensitive features of the condition information evaluation results obtained via the generalized multi-scale dynamic time warping algorithm were selected by the Laplace Score approach to construct the eigenvector. Finally, random forest was introduced to realize the intelligent fault recognition of wind turbine gearbox. The analysis results of both experimental and engineering signals indicate that the presented method can accurately identify different fault states of wind turbine gearbox. In addition, the proposed method performs a higher accuracy of fault state classification compared with some existing methods.

scholarly journals Fault Diagnosis for Compensating Capacitors of Jointless Track Circuit Based on Dynamic Time Warping

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Dong
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Peak Voltage ◽  
Precise Position ◽  
Diagnosis Method ◽  
Track Circuit ◽  
Function Relationship ◽  
Dynamic Time ◽  
First Time

Aiming at the problem of online fault diagnosis for compensating capacitors of jointless track circuit, a dynamic time warping (DTW) based diagnosis method is proposed in this paper. Different from the existing related works, this method only uses the ground indoor monitoring signals of track circuit to locate the faulty compensating capacitor, not depending on the shunt current of inspection train, which is an indispensable condition for existing methods. So, it can be used for online diagnosis of compensating capacitor, which has not yet been realized by existing methods. To overcome the key problem that track circuit cannot obtain the precise position of the train, the DTW method is used for the first time in this situation to recover the function relationship between receiver’s peak voltage and shunt position. The necessity, thinking, and procedure of the method are described in detail. Besides the classical DTW based method, two improved methods for improving classification quality and reducing computation complexity are proposed. Finally, the diagnosis experiments based on the simulation model of track circuit show the effectiveness of the proposed methods.

Fault diagnosis of railway point machines using dynamic time warping

2016 ◽  
Vol 52 (10) ◽  
pp. 818-819 ◽  
Author(s):  
H. Kim ◽  
J. Sa ◽  
Y. Chung ◽  
D. Park ◽  
S. Yoon
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time
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