Fault Detection of Broken Rotor Bar Using an Improved form of Hilbert–Huang Transform

2018 ◽  
Vol 17 (02) ◽  
pp. 1850012 ◽  
Author(s):  
F. Sabbaghian-Bidgoli ◽  
J. Poshtan
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Hilbert Transform ◽  
Wavelet Packet ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
External Noise ◽  
Vibration Signals ◽  
Broken Rotor Bar ◽  
Hilbert Huang Transform

Signal processing is an integral part in signal-based fault diagnosis of rotary machinery. Signal processing converts the raw data into useful features to make the diagnostic operations. These features should be independent from the normal working conditions of the machine and the external noise. The extracted features should be sensitive only to faults in the machine. Therefore, applying more efficient processing techniques in order to achieve more useful features that bring faster and more accurate fault detection procedure has attracted the attention of researchers. This paper attempts to improve Hilbert–Huang transform (HHT) using wavelet packet transform (WPT) as a preprocessor instead of ensemble empirical mode decomposition (EEMD) to decompose the signal into narrow frequency bands and extract instantaneous frequency and compares the efficiency of the proposed method named “wavelet packet-based Hilbert transform (WPHT)” with the HHT in the extraction of broken rotor bar frequency components from vibration signals. These methods are tested on vibration signals of an electro-pump experimental setup. Moreover, this project applies wavelet packet de-noising to remove the noise of vibration signal before applying both methods mentioned and thereby achieves more useful features from vibration signals for the next stages of diagnosis procedure. The comparison of Hilbert transform amplitude spectrum and the values and numbers of detected instantaneous frequencies using HHT and WPHT techniques indicates the superiority of the WPHT technique to detect fault-related frequencies as an improved form of HHT.

HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings

2004 ◽  
Vol 127 (4) ◽  
pp. 299-306 ◽  
Author(s):  
Hasan Ocak ◽  
Kenneth A. Loparo
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Markov Modeling ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Hidden Markov Modeling ◽  
Rolling Element ◽  
Detection And Diagnosis

In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.

scholarly journals Engine Fault Detection Approach Based on Angle Domain Signal Model

2021 ◽  
Vol 2068 (1) ◽  
pp. 012034
Author(s):  
Hai Zeng ◽  
Ning Zeng ◽  
Jin Han ◽  
Yan Ding
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Failure Detection ◽  
Vibration Signal ◽  
Bench Test ◽  
Signal Model ◽  
Vibration Signals ◽  
Engine Vibration ◽  
Detection Approach ◽  
Diagnosis Approach

Abstract Engine vibration signals include strong noise and non-stationary signals. By the time domain signal processing approach, it is hard to extract the failure features of engine vibration signals, so it is hard to identify engine failures. For improving the success rate of engine failure detection, an engine angle domain vibration signal model is established and an engine fault detection approach based on the signal model is proposed. The angle domain signal model reveals the modulation feature of the engine angular signal. The engine fault diagnosis approach based on the angle domain signal model involves equal angle sampling and envelope analysis of engine vibration signals. The engine bench test verifies the effectiveness of the engine fault diagnosis approach based on the angle domain signal model. In addition, this approach indicates a new path of engine fault diagnosis and detection.

Study on the Method of Vibration Signal De-Noising Using Wavelet Packet Based on QPSO

2014 ◽  
Vol 599-601 ◽  
pp. 1738-1744
Author(s):  
Kai Zhao ◽  
Ben Wei Li ◽  
Jing Chen
Keyword(s):  
Experimental Verification ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Function ◽  
Threshold Function ◽  
Vibration Signals ◽  
Selection Of

Although many wavelet de-noising methods have been studied and proposed, the parameters of them are obtained by experience mostly, which makes the de-noising effect instable. To solve the issues, the solutions, such as the selection of wavelet function and threshold function, the calculation of decomposition levels, the optimal wavelet packet basis and the thresholds obtained based on QPSO, have been studied in this paper. Every parameter is obtained by calculation. This method is applied to the de-noising experiment of sine and vibration signals. Through the experimental verification, the effect of this de-noising method is obvious.

Evaluation of Diagnostic Performance for the Gear Box Considering the Measurement Position Effect With Wavelet Packet Transform

2009 ◽  
Author(s):  
Young-Sun Hong ◽  
Gil-Yong Lee ◽  
Young-Man Cho ◽  
Sung-Hoon Ahn ◽  
Chul-Ki Song
Keyword(s):  
Wavelet Packet ◽  
Position Effect ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Wavelet Packet Transform ◽  
Bevel Gear ◽  
Diagnostic Methods ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fault Features

There has been much research into monitoring techniques for mechanical systems to ensure stable production levels in modern industries. This is particularly true for the diagnostic monitoring of rotary machinery, because faults in this type of equipment appear frequently and quickly cause severe problems. Such diagnostic methods are often based on the analysis of vibration signals because they are directly related to physical faults. Even though the magnitude of vibration signals depends on the measurement position, the effect of measurement position is generally not considered. This paper describes an investigation of the effect of the measurement position on the fault features in vibration signals. The signals for normal and broken bevel gears were measured at the base, gearbox, and bevel gear, simultaneously, of a machine fault simulator (MFS). These vibration signals were compared to each other and used to estimate the classification efficiency of a diagnostic method using wavelet packet transform. From this experiment, the fault features are more prominently in the vibration signal from the measurement position of the bevel gear than from the base and gearbox. The results of this analysis will assist in selecting the appropriate measurement position in real industrial applications and precision diagnostics.

Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

2019 ◽  
Vol 34 (07) ◽  
pp. 2059019 ◽  
Author(s):  
Hanxin Chen ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
Chenghao Cao ◽  
Liu Yang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Hypothesis Test ◽  
Wavelet Packet ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Ratio Test ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Sequential Probability

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

scholarly journals Damped CAPES 2D Spectral Estimation for Real-Valued Vibration Signals

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Danhui Dan ◽  
Jiongxin Gong ◽  
Yiming Zhao
Keyword(s):  
Spectral Estimation ◽  
Amplitude Spectrum ◽  
Vibration Signal ◽  
Angular Frequency ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
Vibration Signals ◽  
Vibration Data ◽  
Decay Factor ◽  
Sine Term

We propose a 2D representation in the frequency-decay factor plane of an arbitrary real-world vibration signal. The signal is expressed as the sum of a decayed-attenuation sine term modulated by an amplitude function and a noise residue. We extend the combined approach of Capon estimation and amplitude and phase estimation (CAPES) to damped real vibration signals (DR-CAPES). In the proposed DR-CAPES method, the high-resolution amplitude and phase are estimated simultaneously for both angular frequency and decay factor grids. The performance of the proposed approach is tested numerically with noisy vibration data. Results show that the DR-CAPES method has an excellent frequency resolution, which helps to overcome difficulties in spectrum estimation when vibration modes are very close, and a small bias, which makes it suitable for obtaining accurate amplitude spectrums. The results also indicate that the proposed method can accurately estimate the amplitude spectrum with the use of averaging and denoising processes.

Diagnosis of Ball Screw Preload Loss by Vibration Signals through the Hilbert-Huang Transform and Multiscale Measure

2011 ◽  
Vol 58-60 ◽  
pp. 636-641
Author(s):  
Yan Chen Shin ◽  
Yi Cheng Huang ◽  
Jen Ai Chao
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ball Screw ◽  
Multiscale Entropy ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Diagnosis Method

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.

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