Fault Diagnosis Method Based on Independent Component Analysis and Dynamic Time Warping

2006 ◽  
Author(s):  
Xiaogang Deng ◽  
Xuemin Tian
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Dynamic Time Warping ◽  
Component Analysis ◽  
Independent Component ◽  
Time Warping ◽  
Diagnosis Method ◽  
Dynamic Time

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.

Research on Fault Diagnosis for Petrochemical Running Equipments Based on ICA

2011 ◽  
Vol 48-49 ◽  
pp. 950-953
Author(s):  
Zhi Gang Chen ◽  
Xiao Jiao Lian ◽  
Ming Zhou
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Signal Separation ◽  
Signal Extraction ◽  
Vibration Signals ◽  
Signal Preprocessing

For solving the difficulty of feature signal extraction from vibration signals, a new method based on Independent Component Analysis (ICA) is proposed to realize separation and filtering for multi-source vibration signals. Firstly, the principal and algorithm of ICA used to separate mixed signals is introduced. Secondly, application in signal separation and filtering with ICA is studied in diagnosis. In addition, imitation and field examples are given. The experiments show it is feasible to separate and extract feature signal from multi-source vibration signals and it is an effective method in signal preprocessing in fault diagnosis.

Application of Fault Diagnosis Method Based on cICA to Gearbox

2014 ◽  
Vol 664 ◽  
pp. 148-152
Author(s):  
Shuang Xi Jing ◽  
Song Tao Guo ◽  
Jun Fa Leng ◽  
Xing Yu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Signal To Noise Ratio ◽  
Component Analysis ◽  
Independent Component ◽  
Early Application ◽  
Independent Components ◽  
Gear Fault ◽  
Constrained Independent Component Analysis ◽  
Non Gaussian

Constrained independent component analysis (cICA) is a new theory and new method derived from the independent component analysis (ICA).It can extract the desired independent components (ICs) from the data based on some prior information, thus overcoming the uncertainty of the traditional ICA. Early gearbox fault signals is often very weak ,characterized by non-Gaussian,low signal-to-noise ratio (SNR), which make the existing diagnosis methods in the diagnosis of early application restricted. In this paper,cICA algorithm is applied to gear fault diagnosis. Through the case studies verify the feasibility of this method to extract the desired independent components (ICs), indicating the applicability and effectiveness of the method.

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