Fault Diagnosis of Electric Machines Using Techniques Based on Frequency Domain

2017 ◽  
pp. 99-154
Author(s):  
Subhasis Nandi
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Electric Machines

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.

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

2011 ◽  
Vol 143-144 ◽  
pp. 613-617
Author(s):  
Shuang Xi Jing ◽  
Yong Chang ◽  
Jun Fa Leng
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Frequency Region ◽  
Analysis Method ◽  
Vibration Signals ◽  
Harmonic Wavelet ◽  
Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

The Engine Fault Diagnosis Based on Time Domain and Frequency Domain

2014 ◽  
Vol 936 ◽  
pp. 2243-2246 ◽  
Author(s):  
Zhu Ting Yao ◽  
Hong Xia Pan
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Time Domain ◽  
Water Pressure ◽  
Cooling Water ◽  
Experimental System ◽  
Vibration Signal ◽  
Cylinder Block ◽  
Fuel Temperature ◽  
Multiple Faults

Engine is as a power machine, the operating status is good or bad, directly affects the working status of equipment. The status monitoring and fault diagnosis is very necessary to ensure that the equipment runs in its best, and improves equipment maintenance quality and efficiency. The engine failure shows the complexity and diversity of the interaction and complex relationship between the various subsystems of the engine, that is the fault of complexity, ambiguity, correlation, relativity and multiple faults coexistence. The available information are much in the engine diagnosis, for example, the vibration signal from bearings, cylinder head or cylinder block surface; oil, cooling water, pressure of intake, exhaust and fuel; temperature signal; noise, speed or oil-sample signals. In this paper, an engine as an example, engine fault diagnosis experimental system is built, the normal state, left one and right six cylinders off the oil, air filter blockage (inlet wood blockage is 30%, the inlet has screen cloth.) in the load of 2565Nm, and the speeds of 1500r/min, 1800r/min, 2200r/min are studied. The experimental results analysis, feature extraction and fault diagnosis are finished based on the time domain and frequency domain. Keywords: engine, fault diagnosis, time domain, frequency domain.

scholarly journals Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 359 ◽  
Author(s):  
Jianghua Ge ◽  
Guibin Yin ◽  
Yaping Wang ◽  
Di Xu ◽  
Fen Wei
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Support Vector ◽  
Distance Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Evaluation ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved.

Online Diagnostics of Mechanical and Electrical Faults in Induction Motor Using Multiclass Support Vector Machine Algorithms Based on Frequency Domain Vibration and Current Signals

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Induction Motor ◽  
Sampling Rate ◽  
Computation Time ◽  
Current Data ◽  
Operating Conditions ◽  
Support Vector ◽  
Electrical Faults

This paper demonstrates the development of a flexible fault diagnosis methodology that can detect up to ten different faults in the induction motor (IM), simultaneously. The major IM electrical faults, such as the broken rotor bar (BRB), phase unbalance (PUF), and stator winding fault (SWF), and mechanical faults, such as bearing fault (BF), unbalanced rotor (UR), bowed rotor (BR), and misaligned rotor (MR), are considered with different fault severities for the diagnosis. The experiments are conducted with three varying loads and seven different speeds, and the frequency domain vibration and current data are acquired at a relatively low sampling rate of 1 kHz. Several statistical features are extracted and then the best feature-set is selected using the wrapper model. Thereafter, a data classification tool based on the support vector machine (SVM) is used for the fault characterization. Initially, a multi-fault diagnosis is performed by training and testing the SVM at the same operating conditions (i.e., load and speed). The performance of the classifier is found to be very good at all IM operating conditions. The main focus of this study lies in overcoming the fault diagnosis, where the data are unavailable at required operating conditions. This is accomplished by employing interpolation and extrapolation strategies for different loads and speeds. The proposed methodology not only solves practical problem of unavailability of data at different operating conditions but also shows good performance and takes low computation time, which are vital requirements of an online intelligent condition monitoring system.

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