Rujiang Hao

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.

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.

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 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.

Application of cyclic spectral analysis to gear crack assessment

With regards to the cyclostationarity of gear vibration, a novel indicator based on cyclic spectral analysis is proposed to assess localized gear damage. The cyclic spectral density of amplitude modulation and frequency modulation 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 indicates the amplitude modulation magnitude. In the analysis of gear crack simulation vibration signals, the ratio shows a monotonically increasing trend with the development of the gear crack level. It has potential to assess gearbox damage.

Gearbox fault diagnosis based on frame decomposition

Frame decomposition is flexible in representing arbitrary signals, and thereby is effective in matching the characteristic structure of and extracting the time-frequency features directly. It is applied to analyzing the gearbox vibration signals under healthy and faulty statuses. Based on a wavelet frame, the periodic impulses characteristic of localized damaged gear vibration are extracted in joint time-frequency domain, and the localized gear damage is detected and located. The analysis validates the effectiveness of frame decomposition in fault diagnosis of gearboxes.

Application of Cyclic Correlation Analysis to Gearbox Damage Assessment

With regard to the cyclostationarity of gear vibration, cyclic correlation is used to extract the modulation features of gear vibration signals, so as to detect and assess localized gear damage. The cyclic auto-correlation of amplitude modulation and frequency modulation (AMFM) signals is deduced. The ratio between the cyclic auto-correlation slice (with time lag of 0) magnitude at cyclic frequencies of modulating frequency and 0 Hz is a monotonically increasing function of the amplitude modulation magnitude. Since AMFM is characteristic of gear vibration signals, and the modulation magnitude is related to localized gear damage degree, the ratio is used to assess the health status of gears. In the analysis of a gearbox experimental vibration signals, the ratio shows a monotonically increasing trend with the development of the gear tooth spalling damage. It has potential to early detect incipient damage, to prognosticate severe damage, and to assess gearbox damage.