Zhipeng Feng

Gear Damage Assessment Based on Cyclic Spectral Analysis

With regard to the AMFM characteristics, and especially the cyclostationarity of gear vibrations, cyclic spectral analysis is used to extract the modulation features of gearbox vibration signals to detect and assess localized gear damage. The explicit equation for the cyclic spectral density in a closed form for AMFM signals is deduced, and its properties in the joint cyclic frequency-frequency domain are summarized. The ratio between the sum of the cyclic spectral density magnitude along the frequency axis at the cyclic frequencies of modulating frequency and 0 Hz varies monotonically with the amplitude modulation magnitude. Hence it is useful to track modulation magnitude. Localized gear damage generates periodic impulses, and its growth increases the magnitude of periodic impulses. Consequently, the amplitude modulation magnitude of gear AMFM vibration signals increases. Hence the ratio can be used as an indicator of the health condition of gearboxes. The analysis of both gear crack simulation vibration signals and gearbox lifetime experiments shows a globally monotonic increase as gear damage severity increases. The proposed approach has the potential to assess the health of gearboxes, and predict severe damage.

Adaptive Mode Decomposition Methods and Their Applications in Signal Analysis for Machinery Fault Diagnosis: A Review With Examples

Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information. Adaptive mode decomposition methods have excellent adaptability and high flexibility in describing arbitrary complicated signals, and are free from the limitations imposed by conventional basis expansion, thus being able to adapt to the signal characteristics, extract rich characteristic information, and therefore reveal the underlying physical nature. This paper presents a systematic and up-to-date review on adaptive mode decomposition in two major topics, i.e., mono-component decomposition algorithms (such as empirical mode composition, local mean decomposition, intrinsic time-scale decomposition, local characteristic scale decomposition, Hilbert vibration decomposition, empirical wavelet transform, variational mode decomposition, nonlinear mode decomposition, and adaptive local iterative filtering) and instantaneous frequency estimation approaches (including Hilbert-transform-based analytic signal, direct quadrature, and normalized Hilbert transform based on empirical AM-FM decomposition, as well as generalized zero-crossing and energy separation) reported in more than 80 representative articles published since 1998. Their fundamental principles, advantages and disadvantages, and applications to signal analysis in machinery fault diagnosis, are examined. Examples are provided to illustrate their performance.

Application of support vector machine based on pattern spectrum entropy in fault diagnostics of bearings

The fault diagnostics and identification of rolling element bearings have been the subject of extensive research. This paper presents a novel pattern classification approach for the fault diagnostics, which combines the morphological multi-scale analysis and the “one to others” support vector machine (SVM) classifiers. Morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multi-scale structuring elements. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vectors presenting different faults of the bearings. The “one to others” SVM algorithm is adopted to distinguish six kinds of fault bearing signals which were measured in the experimental test rig running under eight different working conditions. The recognition results of the SVM are ideal even though the training sample is few. The combination of the morphological pattern spectrum parameter analysis and the “one to others” multi-class SVM algorithm is suitable for the on-line automated fault diagnosis of the rolling element bearings. This application is promising and worth well exploiting.

Teager Energy Spectrum for Fault Diagnosis of Rolling Element Bearings

Localized damage of rolling element bearings generates periodic impulses during running. The repeating frequency of impulses is a key indicator for diagnosing the localized damage of bearings. A new method, called Teager energy spectrum, is proposed to diagnose the faults of rolling element bearings. It exploits the unique advantages of Teager energy operator in detecting transient components in signals to extract periodic impulses of bearing faults, and uses the Fourier spectrum of Teager energy to identify the characteristic frequency of bearing faults. The effectiveness of the proposed method is validated by analyzing the experimental bearing vibration signals.

Application of cyclic spectral analysis to gear damage assessment

Gear vibration signals can be modeled as amplitude modulation and frequency modulation (AMFM) processes, and have cyclostationarity, so cyclic spectral analysis is used to extract the modulation features of gearbox vibration signals, and to detect and assess localized gear damage. The cyclic spectral density of AMFM signals is deduced, and its properties in joint cyclic frequency-frequency domain are summarized. The ratio between the sum of cyclic spectral density magnitude along frequency axis at the cyclic frequencies of modulating frequency and 0 Hz can indicate the amplitude modulation magnitude, so it can be used as an indicator to assess the health status of gearboxes. The analysis of a gearbox run-to-failure test shows that the ratio has a monotonically increasing trend with the development of the gear damage. It has potential to early detect incipient damage, to prognosticate severe damage, and to assess gearbox damage.

Fault feature extraction of planetary gearboxes under nonstationary conditions based on reassigned wavelet scalogram

Planetary gearboxes often run under time-variant conditions, thus resulting in nonstationary signals. How to extract fault features from nonstationary vibration signals is a key issue of planetary gearbox fault diagnosis. Considering the merits of reassigned wavelet scalogram, i.e. fine time-frequency resolution and free from cross term interferences, it is used to analyze the vibration signals in joint time-frequency domain. The effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions is validated by both lab experimental and in-situ signals. For the lab experimental signals, the gear characteristic frequencies and their time evolving features are identified. From the comparison between normal and faulty signal analysis results, the sun gear fault is diagnosed. For the in-situ signals, their time-frequency structures are also resolved. According to the presence of periodical impulses and their repeating period, the planet gear fault is detected.

Gearbox diagnosis based on cyclic spectral analysis

With regards to the cyclostationarity of gear vibration, cyclic spectral analysis is used to extract the modulation features of gearbox vibration signals, so as to detect and locate localized gear damage. The cyclic spectral density of amplitude modulation and frequency modulation processes characteristic of gear vibration signals is deduced, and its properties in joint cyclic frequency-frequency domain are summarized. The performance of cyclic spectral analysis in gear fault diagnosis is illustrated by the vibration signal analysis of a gearbox under normal and faulty status. The gear tooth spalling is detected according to the presence of more peaks on the cyclic frequency-frequency plane, and is located according to the location of cyclic spectral peaks.

Fault diagnosis of gearbox based on matching pursuit

Matching pursuit is effective in matching the characteristic structure of signals and extracting the time-frequency features directly. It is employed to analyze the vibration signals of a gearbox under healthy and faulty statuses. Based on a compound dictionary, the periodic impulses characterizing the vibration of localized damaged gears are extracted in joint time-frequency domain, and the localized gear damage is detected and located. The analysis validates the effectiveness of matching pursuit in detecting and locating localized gear damage.

Defects diagnosis and classification for rolling bearing based on mathematical morphology

The defects diagnosis and pattern classification are presented in this paper. Morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multi-scale structuring elements. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vector presenting different defects of the rolling bearings. The support vector machinery (SVM) algorithm is adopted to distinguish different kinds of defective bearing signals. The recognition results of the SVM are ideal and more precise than that of the artificial neural network. The combination of the morphological pattern spectrum parameter analysis and the SVM algorithm is suitable for the on-line automated defect diagnosis of the rolling bearing.

Gear crack assessment based on cyclic correlation analysis

Gear vibration signals are mainly due to the gear rotating, gear pair meshing and their coupling effects, and are usually modeled as amplitude modulation and frequency modulation (AMFM) processes. The modulation feature of vibration signals is related to the health status of gears. As well, their statistics change periodically with gear rotation, i.e. they are cyclostationary, so cyclostationary analysis is suitable to extract the modulation features of gear vibration signals, so as to detect and assess gear damage. The cyclic auto-correlation function of AMFM signals is deduced, and its properties in cyclic frequency domain are summarized. The ratio between the cyclic autocorrelation slice magnitude at cyclic frequencies of modulating frequency and 0 Hz is a monotonically increasing function of the modulation magnitude, so it is able to indicate the modulation magnitude. Since the modulation magnitude of gear vibration signals is related to localized gear damage degree, the ratio is used to assess the condition of gears. In the analysis of gear crack simulation vibration signals, it shows a monotonically increasing trend with the development of the gear damage. It has potential to early detect incipient damage, to prognosticate severe damage, and to assess gearbox damage.