Robert X. Gao

Wavelets for fault diagnosis of rotary machines: A review with applications

Over the last 20 years, particularly in last 10 years, great progress has been made in the theory and applications of wavelets and many publications have been seen in the field of fault diagnosis. This paper attempts to provide a review on recent applications of the wavelets with focus on rotary machine fault diagnosis. After brief introduction of the theoretical background on both classical wavelet transform and second generation wavelet transform, applications of wavelets in rotary machine fault diagnosis are summarized according to the following categories: continuous wavelet transform-based fault diagnosis, discrete wavelet transform-based fault diagnosis, wavelet packet transform-based fault diagnosis, and second generation wavelet transform-based fault diagnosis. In addition, some new research trends, including wavelet finite element method, dual-tree complex wavelet transform, wavelet function selection, new wavelet function design, and multi-wavelets that advance the development of wavelet-based fault diagnosis are also discussed.

PCA-based feature selection scheme for machine defect classification

The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.

Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

This paper presents a signal analysis technique for machine health monitoring based on the Hilbert-Huang Transform (HHT). The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of the HHT is introduced, based on a synthetic analytic signal, and its effectiveness is experimentally evaluated using vibration signals measured on a test bearing. The results demonstrate that HHT is suited for capturing transient events in dynamic systems such as the propagation of structural defects in a rolling bearing, thus providing a viable signal processing tool for machine health monitoring

Wavelet transform with spectral post-processing for enhanced feature extraction

The quality of machine condition monitoring techniques as well as their applicability in the industry are determined by the effectiveness and efficiency with which characteristic signal features are extracted and identified. Because of the weak amplitude and short duration of structural defect signals at the incipient stage, it is generally difficult to extract hidden features from the data measured using conventional spectral techniques. A new approach based on a combined wavelet and Fourier transformations is presented in this paper. Experimental studies on a rolling bearing with a localized point defect of 0.25 mm diameter has shown that this new technique provides significantly improved feature extraction capability over the spectral techniques.

Rotary Machine Health Diagnosis Based on Empirical Mode Decomposition

This paper presents a signal decomposition and feature extraction technique for the health diagnosis of rotary machines, based on the empirical mode decomposition. Vibration signal measured from a defective rolling bearing is decomposed into a number of intrinsic mode functions (IMFs), with each IMF corresponding to a specific range of frequency components contained within the vibration signal. Two criteria, the energy measure and correlation measure, are investigated to determine the most representative IMF for extracting defect-induced characteristic features out of vibration signals. The envelope spectrum of the selected IMF is investigated as an indicator for both the existence and the specific location of structural defects within the bearing. Theoretical foundation of the technique is introduced, and its performance is experimentally verified.

Condition monitoring and control for intelligent manufacturing

Monitoring and Control of Machining Precision Manfacturing Process Monitoring with Acoustic Emission Tool Condition Monitoring in Machining Monitoring System for Grinding Processes Condition Monitoring of Rotary Machines Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings Sensor Placement and Signal Processing for Bearing Condition Monitoring Monitoring and Diagnosis of Sheet Metal Stamping Processes Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling Signal Processing in Manufacturing Monitoring Autonomous Active-Sensor Networks for High-Accuracy Monitoring in Manfacturing Remote Monitoring and Control in Distributed Manufacturing Environment An Intelligent Nanofabrication Probe with Function of Surface Displacement/Profile Measurement Smart Transducer Interface Standards for Condition Monitoring and Control of Machines Rocket Testing and Integrated System Health Management

Wavelet transform with spectral post-processing for enhanced feature extraction [machine condition monitoring]

The quality of machine condition monitoring techniques and their applicability in the industry are determined by the effectiveness and efficiency, with which characteristic signal features are extracted and identified. Because of the weak amplitude and short duration of structural defect signals at the incipient stage, it is generally difficult to extract hidden features from the data measured using conventional spectral techniques. A new approach, based on a combined wavelet and Fourier transformation, is presented in this paper. Experimental studies on a rolling bearing with a localized point defect of 0.25 mm diameter have shown that this new technique provides significantly improved feature extraction capability over the spectral technique.

Complexity as a measure for machine health evaluation

This paper presents a machine health evaluation technique using the Lempel-Ziv complexity as a numerical measure. Comparing to conventional techniques such as spectral and time-frequency analysis, the presented approach does not require a linear transfer function of the physical system to be evaluated, and is thus suited for the condition monitoring of machine systems under varying operation and loading conditions. Theoretical foundation of the technique is introduced, and its performance is investigated through experimental study of realistic vibration signals measured from a rolling bearing system. The results demonstrated that complexity provides an effective measure for machine health condition evaluation.

Multisensor Data Fusion for Physical Activity Assessment

This paper presents a sensor fusion method for assessing physical activity (PA) of human subjects, based on support vector machines (SVMs). Specifically, acceleration and ventilation measured by a wearable multisensor device on 50 test subjects performing 13 types of activities of varying intensities are analyzed, from which activity type and energy expenditure are derived. The results show that the method correctly recognized the 13 activity types 88.1% of the time, which is 12.3% higher than using a hip accelerometer alone. Also, the method predicted energy expenditure with a root mean square error of 0.42 METs, 22.2% lower than using a hip accelerometer alone. Furthermore, the fusion method was effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition, especially when data from the ventilation sensor were added to the fusion model. These results demonstrate that the multisensor fusion technique presented is more effective in identifying activity type and energy expenditure than the traditional accelerometer-alone-based methods.

Energy-Based Feature Extraction for Defect Diagnosis in Rotary Machines

This paper presents an energy-based approach to defect diagnosis in rotary machines and machine components, which enhances the ability of the continuous wavelet transform in feature extraction from vibration signals. Specifically, the energy content of the wavelet coefficients of vibration signal measured on rolling bearings has been evaluated for selecting appropriate base wavelet and decomposition scale such that identification of defect-induced signal features is significantly improved. Through subsequent envelope spectral analysis of the extracted signal features, the location of structural defect in the bearing being monitored can be identified. An experimental study performed on two ball bearings has shown that the developed approach is more effective in diagnosing bearing defects than using the traditional techniques.