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.

scholarly journals Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 593 ◽  
Author(s):  
Guiji Tang ◽  
Bin Pang ◽  
Yuling He ◽  
Tian Tian
Keyword(s):  
Fault Diagnosis ◽  
Energy Density ◽  
Dynamic Time Warping ◽  
Vibration Signal ◽  
Time Warping ◽  
Efficient Operation ◽  
Fault Features ◽  
Diagnosis Method ◽  
Dynamic Time ◽  
Instantaneous Energy

The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

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.

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