Demodulation Analysis Research Articles

Compound fault diagnosis of gearboxes based on GFT component extraction

Compound fault diagnosis of gearboxes is of great importance to the long-term safe operation of rotating machines, and the key is to separate different fault components. In this paper, the path graph is introduced into the vibration signal analysis and the graph Fourier transform (GFT) of vibration signals are investigated from the graph spectrum domain. To better extract the fault components in gearboxes, a new adjacency weight matrix is defined and then the GFT of simulation signals of the gear and the bearing with localized faults are analyzed. Further, since the GFT graph spectrum of the gear fault component and the bearing fault component are mainly distributed in the low-order region and the high-order region, respectively, a novel method for the compound fault diagnosis of gearboxes based on GFT component extraction is proposed. In this method, the nonzero ratios, which are introduced to analyze the eigenvectors auxiliary, and the GFT of a gearbox vibration signal, are firstly calculated. Then, the order thresholds for reconstructed fault components are determined and the fault components are extracted. Finally, the Hilbert demodulation analyses are conducted. According to the envelope spectra of the fault components, the faults of the gear and the bearing can be diagnosed respectively. The performance of the proposed method is validated by the simulation data and the experiment signals from a gearbox with compound faults.

Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis

Abstract The vibration signals from defective rolling bearings are multi-component amplitude modulation (AM)-frequency modulation (FM) signals. Traditional envelope analysis method is based on a filter. The centre frequency and bandwidth of the filter are set according to experience. So the filter method will induce a demodulation error. This research proposes a rolling bearing fault diagnosis method based on adaptive local iterative filtering (ALIF) and envelope spectrum. The ALIF method is a new method for the analysis of non-stationary signals. It uses an iterative filters strategy together with an adaptive, data-driven filter length selection to achieve the necessary decomposition. Smooth filters with compact support from the solutions of the Fokker–Planck equations are used within the ALIF method. The ALIF method offers good performance in obtaining more accurate components of non-stationary signals and in suppressing mode mixing. The ALIF method can decompose a multi-component AM-FM signal into a number of stationary components. The envelope demodulation method is used to analyse those components containing fault information which, in turn, can reveal bearing fault features. The vibration signals from a rolling bearing with an outer race fault and an inner race fault are used to verify the proposed method. The results show that this method can effectively extract bearing fault features.

Amplitude and frequency demodulation analysis for fault diagnosis of planet bearings

The vibration signals of planet bearings feature more complex amplitude modulation and frequency modulation (AM-FM) than those of fixed-axis bearings, due to the effects of time-varying vibration transfer path, load zone passing and time-varying angles between gear pair mesh lines of action and impact force vector, in addition to that of bearing fault. Their Fourier spectra have a complex sideband structure, leading to difficulty in feature extraction and fault diagnosis. In order to address this issue, a joint amplitude and frequency demodulation analysis method is proposed to reveal the fault features, by considering the modulation characteristics. To thoroughly understand planet bearing vibration characteristics, the explicit equations for amplitude and frequency demodulated spectra of outer race, rolling element and inner race fault cases are derived, and the fault symptoms are summarized respectively. The signal is firstly decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), thus meeting the mono-component requirement by instantaneous frequency calculation. Then a sensitive component with the instantaneous frequency fluctuating around the resonance frequency is chosen for further frequency demodulation analysis. Finally, planet bearing fault can be diagnosed by matching the peaks identified in amplitude and frequency demodulated spectra with the theoretical fault characteristic frequencies. The proposed method is validated with both numerical simulated and lab experimental signal analyses.

Motor Current Demodulation Analysis applied with Neural Networks and Genetic Algorithms for Rotor Bar Faults Diagnosis

Motor Current Demodulation Analysis applied with Neural Networks and Genetic Algorithms for Rotor Bar Faults Diagnosis

Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform

Multi-fault detection is a challengeable task for fault feature extraction because the weak faults are always buried in intensive vibration energy, especially in the wind turbine gearbox consisting of numerous gears and bearings under severe operation condition. The vibration signals originated from a real multi-fault wind turbine gearbox with catastrophic failure are analyzed in this paper. Conventional narrow-band filtering and Hilbert transform are used to detect distinct harmonics representing broken teeth faults of gears. The cepstrum method is adopted to distinguish the approached frequency components. However, the fault features representing bearing failure doesn't emerge in the demodulation and cepstrum analysis. Complex wavelet transform can provide a multi-scale enveloping spectrogram (MuSEnS) to decompose and demodulate signals simultaneously. Using this method, the weak bearing fault features buried in intensive energies can be detected readily through analyzing the sclies of the MuSEnS at different scales. The disassembled results of the wind turbine gearbox demonstrate the effectiveness of the applied methods. The failure mechanism of the multiple faults in the wind turbine gearbox is discussed, which reveals that the weak bearing faults can lead to catastrophic failure.

Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what’s more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

A Time-gated Contactless Interrogation System for Frequency and Quality Factor Tracking in QCR to Investigate on Liquid Solution Microdroplets

In this work, a contactless interrogation via electromagnetic coupling based on time-gated techniques has been proposed for Quartz Crystal Resonator (QCR) to investigate on liquid solution microdroplets. The sensing of the resonator response is innovatively based on a heterodyne demodulation and autocorrelation analysis, providing high performances yet avoiding the use of expensive instruments and allowing to estimate both the sensor damped resonant frequency and the quality factor Q. The system works with interrogation distances up to few centimeters and has been experimentally validated by loading the QCR with microdroplets of different liquid microsamples.

Joint amplitude and frequency demodulation analysis based on intrinsic time-scale decomposition for planetary gearbox fault diagnosis

Planetary gearbox vibration signals feature complex modulations, thus leading to intricate sideband structure and resulting in difficulty in fault characteristic frequency identification. Intrinsic time-scale decomposition has unique merits, such as high adaptability to changes in signals, low computational complexity, good capability to suppress mode mixing and to preserve temporal information of transients, and excellent suitability for mono-component decomposition of complex multi-component signals. In order to address the issue with planetary gearbox fault diagnosis due to the multiple modulation sources, a joint amplitude and frequency demodulation analysis method is proposed, by exploiting the merits of intrinsic time-scale decomposition. The signal is firstly decomposed into a series of mono-component proper rotational components. Then the one with its instantaneous frequency fluctuating around the gear meshing frequency or its harmonics is selected as the sensitive component. Next, Fourier transformation is applied to the instantaneous amplitude and instantaneous frequency of the sensitive component to obtain the amplitude and frequency demodulated spectra respectively. Finally, a planetary gearbox fault is diagnosed by matching the peaks in the amplitude and frequency demodulated spectra with the theoretical gear fault characteristic frequencies. The proposed method is illustrated by a numerical simulated signal, and further validated by lab experimental signals of a planetary gearbox. The localized faults of sun, planet and ring gears are diagnosed, showing the effectiveness of the method.

Time–frequency demodulation analysis based on iterative generalized demodulation for fault diagnosis of planetary gearbox under nonstationary conditions

The vibration signal of planetary gearboxes exhibits the characteristics of both amplitude modulation (AM) and frequency modulation (FM), and thus has a complex sideband structure. Time-varying speed and/or load will result in time variant characteristic frequency components. Since the modulating frequency is related to the gear fault characteristic frequency, the AM and FM parts each alone contains the information of the gear fault. We propose a time–frequency amplitude and frequency demodulation analysis metbhod to avoid the complex time-variant sideband analysis, and thereby identify the time-variant gear fault characteristic frequency. We enhance the time–frequency analysis via iterative generalized demodulation (IGD). The time-varying amplitude and frequency demodulated spectra have fine time–frequency resolution and are free of cross term interferences. They do not involve complex time-variant sidebands, thus considerably facilitating fault diagnosis of planetary gearboxes under nonstationary conditions. The method is validated using both numerically simulated data and experimental signals.

Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

A Carrier Signal Approach for Intermittent Fault Detection and Health Monitoring for Electronics Interconnections System

Intermittent faults are completely missed out by traditional monitoring and detection techniques due to non-stationary nature of signals. These are the incipient events of a precursor of permanent faults to come. Intermittent faults in electrical interconnection are short duration transients which could be detected by some specific techniques but these do not provide enough information to understand the root cause of it. Due to random and non-predictable nature, the intermittent faults are the most frustrating, elusive, and expensive faults to detect in interconnection system. The novel approach of the author injects a fixed frequency sinusoidal signal into electronics interconnection system that modulates intermittent fault if persist. Intermittent faults and other channel effects are computed from received signal by demodulation and spectrum analysis. This paper describes technology for intermittent fault detection, and classification of intermittent fault, and channel characterization. The paper also reports the functionally tests of computational system of the proposed methods. This algorithm has been tested using experimental setup. It generate an intermittent signal by external vibration stress on connector and intermittency is detected by acquiring and processing propagating signal. The results demonstrate to detect and classify intermittent interconnection and noise variations due to intermittency. Monitoring the channel in-situ with low amplitude, and narrow band signal over electronics interconnection between a transmitter and a receiver provides the most effective tool for continuously watching the wire system for the random, unpredictable intermittent faults, the precursor of failure.

The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform

Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform (TQWT) and Hilbert transform such that fault feature adaptability is enabled. Within ESW, a parametric optimization is performed on the measured signal to obtain a quality TQWT basis that best demonstrate the hidden fault feature. TQWT is introduced as it provides a vast wavelet dictionary with time-frequency localization ability. The parametric optimization is guided according to the maximization of fault feature ratio, which is a new quantitative measure of periodic fault signatures. The fault feature ratio is derived from the digital Hilbert demodulation analysis with an insightful quantitative interpretation. The output of ESW on the measured signal is a selected wavelet scale with indicated fault features. It is verified via numerical simulations that ESW can match the oscillatory behavior of signals without artificially specified. The proposed method is applied to two engineering cases, signals of which were collected from wind turbine and steel temper mill, to verify its effectiveness. The processed results demonstrate that the proposed method is more effective in extracting weak fault features of induction motor bearings compared with Fourier transform, direct Hilbert envelope spectrum, different wavelet transforms and spectral kurtosis.

Analysis and Experimental Verification on Bias Drift of Silicon Microgyroscope

There is a bias drift of silicon microgyroscope when power on which limits its application in higher precision field. This paper analyzes and validates the main cause of bias drift, combined with theoretical analysis of demodulation and experimental results, which has a great significance for improving the performance of silicon microgyroscope. First of all, operating principle of silicon microgyroscope is introduced and theoretical analysis on bias drift is made on the basis of DC output voltage of microgyroscope. Secondly, demodulated signals’ amplitude and phase are analyzed in Matlab with the experimental data. By contrasting theoretical and experimental bias drift, the accuracy of theoretical formula is verified. It turns out that, the relative error of bias drift from 2-hours value to stable value is 5.5%, it’s 6.6% for 1-hour value to stable value and for beginning it’s just 1.7%. Experimental results agrees well with the theoretical analysis, which verifies that the main cause of bias drift of silicon microgyroscope is the phase difference between Vds and Vsense. This conclusion can provide some guidance for structure design optimization and circuits improving.

Bearing faults diagnostics based on hybrid LS-SVM and EMD method

In this paper, a novel method that integrates the LS-SVM and Empirical Mode Decomposition (EMD) is proposed to improve the performance of conventional EMD. The analyzed signal is preprocessed with the weighted Least Squares Support Vector Machines (LS-SVM) to suppress the interference of high-frequency intermittent components and other non-Gaussian noises. The denoised signal is extended with LS-SVM rolling forecast modeling. Next, the linear function is used to construct upper and lower envelopes of the extrapolated data in order to determine the temporary mean envelope curve which is then smoothed with the adaptive mapped LS-SVM to obtain the local mean curve. Signal decomposition is self-adaptively performed to achieve IMFs through removal of the smoothed local mean curve. The representative IMF containing fault information is selected for demodulation analysis to identify the fault characteristics. The effectiveness of the proposed method is verified by means of simulations and applications to bearing fault diagnosis.

Design and Implementation of Multi Tone Modulation System Based on Sound Card

A multi tone modulation system design and development with a low-cost and high performance was developed, taking LabVIEW as the software development platform and the computer sound card as the hardware platform. The system is capable of audio signal acquisition, multi tone modulation, demodulation, waveform and spectrum analysis, and other functions. Actual application shows that the system is not only high accuracy, and reliable, stable performance, easy to operate and extensions. The signal collection technology based on the sound card and the combination of virtual instrument has a broad application prospect in engineering survey and practical teaching.

Fault Diagnosis for Diesel Engine Cylinder Head Based on Genetic-SVM Classifier

Fault diagnosis of Diesel engine cylinder head is very complex, so it is difficult to use the mathematical model to describe their faults. In this study, support vector machine trained by genetic algorithm based on high frequency demodulation analysis is proposed to fault diagnosis of Diesel engine cylinder head. Genetic algorithm is used to determine training parameters of support vector machine in this model, which can optimize the support vector machine (SVM) an intelligent diagnostic model. The performance of the GSVM system proposed in this study is evaluated by Diesel engine cylinder head in the wood-wool production device. The application to fault diagnosis for diesel engine shows the effectiveness o f the method.

Fault Diagnosis Method of Mini Excavator Slewing Bearing

In order to collect the fault signal of slewing bearing, design and built up the slewing bearing test rig and the signal test system. Because slewing bearing fault signal is weak, the signal containing fault characteristics was resolved and reconstructed with the wavelet theory. With the application of the Hilbert transform in demodulation and detailed spectrum analysis, the fault characteristic frequency was extracted, and the slewing bearing fault was judged. All above work shows that Wavelet analysis combined with the Hilbert analysis is effective to the diagnosis of rotary bearing local fault.

Fault Diagnosis of the Armored Vehicle Gear Transmission System Based on Response Analysis

Gear transmission system is as an important part of the armored vehicle, and its work reliability is directly related to the working performance of the armored vehicle. Based on introducing the need on condition monitoring and fault diagnosis of gear transmission system, the response methods on condition monitoring and fault diagnosis are expressed in the gear transmission system. Finally, an armored vehicle transmission system is as the research object, the test point selection, data acquisition and processing is studied. The paper shows how to use differ method such as the amplitude domain, frequency domain, demodulation analysis and wavelet analysis to extract fault characters and fault identification in the armored vehicle gear transmission system.

Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time–frequency analysis

Planetary gearboxes play an important role in wind turbine (WT) drivetrains. WTs usually work under time-varying running conditions due to the volatile wind conditions. The planetary gearbox vibration signals in such an environment are hence highly nonstationary. Conventional spectral analysis and demodulation analysis methods are unable to identify the characteristic frequency of gear fault from such nonstationary signals. As such, this paper presents a time–frequency analysis methods to reveal the constituent frequency components of nonstationary signals and their time-varying features for WT planetary gearbox monitoring. More specifically, we exploit the adaptive optimal kernel (AOK) method for this challenging application because of its fine time–frequency resolution and cross-term free nature, as demonstrated by our simulation analysis. In this study, the AOK method has been applied to identify the time-varying characteristic frequencies of gear fault or to extract different levels of impulses induced by gear faults from lab WT experimental signals and in-situ WT signals under time-varying running conditions. We have demonstrated that the AOK is effective diagnosis of: (a) both the local damage (a single chipped tooth) and distributed faults (wear of all teeth), (b) both sun gear and planet gear faults, and (c) faults with very weak signature (e.g., the sun gear fault at the low speed stage of a WT planetary gearbox).