Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

Shaft angular misalignment detection using acoustic emission

Shaft angular misalignment (SAM) is a common and crucial problem in rotating machinery. Misalignment can produce several shortcomings such as premature bearing failure, increase in energy consumption, excessive seal lubricant leakage and coupling failure. Vibration analysis has been traditionally used to detect SAM; however, it presents some drawbacks i.e. high influence of machine operational conditions and strong impact of the coupling type and stiffness on vibration spectra. This paper presents an extensive experimental investigation in order to evaluate the possibility of detecting SAM, using acoustic emission (AE) technique. The test rig was operated at under different operational conditions of load and speed in order to evaluate the impact on the AE and vibration signature under normal operating conditions. To the best of the author’s knowledge, this is the first attempt to use AE for the detection of SAM under varying operational conditions. A comparative study of vibration and AE was carried out to demonstrate the potentially better performance of AE. The experimental results show that AE technique can be used as a reliable technique for SAM detection, providing enhancements over vibration analysis.     

Diagnosis of continuous rotor-stator rubbing in large scale turbine units using acoustic emissions

Continuous rubbing between the shaft and surrounding seals or end-glands of electricity generating turbine units can escalate into very severe vibration and costly rotor damage. Therefore such rotor-stator contacts require early diagnosis so as to minimize the financial consequences of any unplanned shutdowns. Acoustic emissions (AEs) or stress wave monitoring at the bearings has been identified as a sensitive non-destructive monitoring technique for such rub conditions [Electr. Eng. Jpn. 110(2) (1990); IEEE Proc. 6 (2000) 79; Hall and Mba, 14th International Congress on Condition Monitoring and Diagnostic Engineering Management (COMADEM’2001), Manchester, UK, 2001, p. 21]. However, experimental results from real turbines have been scarce. This paper presents a diagnosis of continuous rotor-stator rubbing in an operational 500 MW turbine unit via high frequency AE measurement within a 100 KHz-1 MHz ultrasonic band. As detailed by Sato [Electr. Eng. Jpn. 110(2) (1990)] and reported in this paper the onset of a continuous rub contact at a seal/gland was revealed by a sinusoidal modulation within the raw ‘rf’ AE response. By synchronous measurement at adjacent bearings, an estimation of the location of the rub was calculated using the phase delay between the adjacent AE modulations. Importantly, the AE diagnosis was closely corroborated by post-inspection of the turbine rotor.     

An analysis of the simulated acoustic emission sources with different propagation distances,types and depths for rail defect detection

Acoustic Emission (AE) technique is an effective nondestructive detecting method, and has a promising application for rail defect detection. So far, little attention has been paid to propagation distances, types, and depths of AE sources, which are important for rail defect detection accurately. This paper presents an experimental study on the simulated AE sources with different propagation distances, types and depths for rail defect detection. Three simulated AE sources with different frequencies are seeded on the cross section of rail, and the depths of AE sources are changed in the vertical direction. After receiving AE signals, wavelet transform and Rayleigh–Lamb equations are utilized to extract time–frequency features and modes. Based on the wavelet transform with corresponding group-velocity curves, the influences of different propagation distances, the features of different source types and the rules of different source depths are examined. It is concluded that the features of AE sources with different propagation distances, types and depths can be obtained by AE technique for rail defect detection. It is very useful to analyze and detect defects in rail defect detection.     

Low speed bearing fault diagnosis using acoustic emission sensors

In this paper, a new methodology for low speed bearing fault diagnosis is presented. This acoustic emission (AE) based technique starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies. Then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage. The resampling approach is able to segment the AE signal according to shaft crossing times such that an even number of data points are available to compute a single spectral average which is used to extract features and evaluate numerous condition indicators (CIs) for bearing fault diagnosis. Unlike existing averaging based noise reduction approaches that require the computation of multiple averages for each bearing fault type, the presented approach computes only one average for all bearing fault types. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The results in this paper have shown that the low sampled AE signals in combination with the presented approach can be utilized to effectively extract condition indicators to diagnose all four bearing fault types at multiple low shaft speeds below 10 Hz.     

超声在固体火箭发动机无损检测中的应用

在固体火箭发动机的研制中，无损检测技术非常重要。本文介绍了一些主要的超声检测方法及其应用。脉冲多次反射法用于检测固体火箭发动机壳体与衬层之间的粘结质量。特性参数和扫频超声法以多层介质理论为基础，用于检测固体火箭发动机的深层粘结质量。     

Mechanical behavior and health monitoring by Acoustic Emission of unidirectional and cross-ply laminates integrated by piezoelectric implant

Recent progress in sensor technologies, signal processing and electronics has made it possible to fulfill the need for the development of in-service structural health monitoring (SHM) systems. This study presents a health monitoring of composite materials integrated by piezoelectric sensor using Acoustic Emission (AE) technique. A series of specimens of composite laminates with and without piezoelectric implant were subject to three-point bending in static and creep tests while continuously monitoring the response by the AE technique. The analysis and observation of AE signals lead to the identification of the acoustic signatures of damage mechanisms in composite laminates. The mechanical behavior of composites with and without integrated sensor shows no difference in the form. The incorporation of piezoelectric sensor influences specially the fracture load and causes low degradation of mechanical properties of materials. One of the major differences between the two types of materials (with and without embedded sensor) is the intense acoustic activity in the integrated material.     

Feasibility of a phased acoustic array for monitoring acoustic signatures from meshing gear teeth

This paper investigates the feasibility of sensing damage emanating from rotating drivetrain elements such as bearings, gear teeth, and drive shafts via airborne paths. A planar phased acoustic array is evaluated as a potential fault detection scheme for detecting spatially filtered acoustic signatures radiating from gearbox components. Specifically, the use of beam focusing and steering to monitor individual tooth mesh dynamics is analyzed taking into consideration the constraints of the array/gearbox geometry and the spectral content of typical gear noise. Experimental results for a linear array are presented to illustrate the concepts of adaptive beam steering and spatial acoustic filtering. This feasibility study indicates that the planar array can be used to track the acoustic signatures at higher harmonics of the gear mesh frequency.     

Effective defect detection in thin film transistor liquid crystal display images using adaptive multi-level defect detection and probability density function

A new automated inspection algorithm is proposed for detecting critical defects based on adaptive multi-level defect detection and probability density function in thin film transistor liquid crystal display (TFT-LCD) images containing a background region’s non-uniform and random noises. To improve the detecting capability for a critical-defect-detecting algorithm, the background region’s non-uniformity is eliminated using statistical values such as the mean and standard deviation of a test image. For the defect detection, the candidate defects are collected on each detection level and used to find a probability density function based on Parzen-window technique. Through simulation it was verified that the proposed method has superior capability for detecting critical defects which results in smaller brightness difference between a defect and its neighbors.     

Time-delay estimation of acoustic emission signals using ICA

Acoustic emission (AE) analysis is used for characterization and location of developing defects in materials. AE sources often generate a mixture of various statistically independent signals. One difficult problem of AE analysis is the separation and characterization of signal components when the signals from various sources and the way in which the signals were mixed are unknown. Recently, blind source separation by independent component analysis (ICA) has been used to solve these problems. The main purpose of this paper is to demonstrate the applicability of ICA to time-delay (T-D) estimation of two independent continuous AE sources on an aluminum beam. It is shown that it is possible to estimate T-Ds by ICA, and thus to locate two independent simultaneously emitted sources.