CA2783089A1 - Damage detection in pipes and joint systems - Google Patents

Abstract

The present invention describes an improved process of damage detection in pipes and joint systems whereby an electric hammer is used to improve the accuracy and efficiency of data collection. A Laser Doppler Vibrometer is employed to collect signal data remotely, thus negating the need to attach a sensor. A wireless carrier set-up with sensors wired to the carrier through an analog to digital converter module (ADC) is employed to enable high sampling rates that improve data collection speed and accuracy at a base computer station. The wireless carrier is easily mounted on remote locations of test sites.
A special purpose signal conditioner with an embedded analog amplifier assists in obtaining the maximum resolution of vibration data obtained through any sensor (especially in the case when piezoelectric sensors are used), from various site locations. This approach provides a process of improving acquisition of a signal within its bandwidth range, as well as a noise filtering system that can collectively facilitate detection of damage in structural joints.
Incorporation of a second IMF in the joint damage detection system has proven to yield greater signal resolution and hence greater reliability by combining t first and second IMF, which will enhance the predictive accuracy of the system.
Damage detection is achieved by processing of the captured vibration signals and processing the signal via a special process to evaluate the damage indices, specifically by using software that is either hosted on a remote computer or by directly installing the code into a remote processor.

Description

DAMAGE DETECTION IN PIPES AND JOINT SYSTEMS
FIELD OF THE INVENTION
The present invention concerns vibration-based damage detection systems and process that are particularly useful for assessing damage to joints of pipes or other structures.
BACKGROUND OF THE INVENTION
Vital resources such as oil, gas, water, and other fluid materials are transported through pipelines that span various terrains. Pipelines are critical transport elements, and their health and reliability through their designed service life are important issues for design and maintenance engineers. Factors such as changes in the structural support due to ground movement, aging, corrosion, impacts from heavy construction equipment, pressure cracks, thermal expansion and contraction, and defective welds can severely impact the integrity of pipelines and the joints connection them and thus, dramatically affect the service life of pipeline segments. These factors can cause economic and environmental problems for industry stakeholders, including the producers, pipeline operators, regulatory agencies, the public, and others who are adversely affected by pipeline leakage. Thus, the creation of a safe and reliable process for detecting damage in pipelines is important.
Piping systems in refineries, power plants, petrochemical and other industrial, commercial, institutional and municipal settings such as water and sewage treatment facilities are subject to various stressors, which cause deterioration in pipes and joints. Degradation due to corrosion is common and inspections must be conducted on a continual basis as part of preventative maintenance programs.
Piping systems may also require inspection after catastrophic events such as fires, floods and earthquakes that render infrastructure prone to damage. Some existing piping inspection process include; (a) ultrasonic (b) radiography (c) liquid penetration (d) magnetic particle (e) eddy current testing (f) acoustic emission and (g) vibration-based.
Traditional process for structural damage detection possess drawbacks such as the necessity of expensive equipment, poor sensitivity, and high operator related costs, while other process are not compatible with common structural pipeline materials such as various plastics or metals. Some of these process include visual inspection, impedance analysis, ultrasonic analysis, acoustic emission/transmission analysis, microwave analysis, magnetic flux leakage analysis, thermography, interferometry, and leaky lamb-wave analysis. Most of the above mentioned process require expansive equipment, high operator costs and involve elaborate and time-consuming operations, except for some of the vibration-based process.

DESCRIPTION OF PRIOR ART
Definitions As used herein, "detecting" means identifying the presence of a characteristic or an event. For example, "detecting structural damage" or "detecting damage" means identifying the presence of damage (e.g., cracks, disbonding on a joint, weak sections of pipe wall, lose fasteners (e.g., bolts, screws, or the like) on mechanically fastened joints, corrosion, or the like).
In another example, "detecting a vibrational response" means identifying the presence of a vibrational response and converting the vibrational response to a signal that can be transmitted, stored, processed, or otherwise manipulated.
As used herein, "relative damage" or "damage index" refers to the following mathematical expression:
iHealthy tenst DImn = m n __ m 'Healthy X100 'mn (1) where DI nin is the measure of relative damage at a joint when a test measurement is taken, / Healthy and / Test are the values of the integral of the processed response signal of the vibrated joint at the time of the damage detection and/or assessment, at the condition when the structure/joint is deemed "healthy" and a subsequent time, respectively. Several process of signal processing can be used to process the vibrational response of a joint and applied to the expression in equation (1) to determine the relative damage or damage index of the joint.
As used herein, "signal" refers to any time-varying quantity. Signals are often scalar-valued functions of time (waveforms), but may be vector valued and may be functions of any other relevant independent variable. For example, a signal produced from a sensor could be an electrical quantity or effect, such as current, voltage, or electromagnetic waves that can be varied in such a way as to convey information.
As used herein, "processing", "signal processing" and other verb tenses of "process" refer to the analysis, interpretation, and manipulation of one or more signals. Processing of signals, such as electrical signals, e.g., voltage, current, or electromagnetic waves, includes storage and reconstruction, separation of information from noise, compression, and feature extraction.
Signal processing process include Fourier Transformation processing (FT), Fast Fourier Transformation processing (FFT), Wavelet Transformation processing (WT), or Hilbert-Huang Transformation processing (HHT), without limitation.
As used herein, "noise" or "signal noise" refers to data without meaning; that is, data that is not being used to transmit a signal, but is simply produced as an unwanted by-product of other activities.
As used herein, a "processor" refers to an electronic device designed to accept data, perform prescribed mathematical and logical operations, and display the results of these operations. Examples of processors include digital and/or analogue computers, Central processing Units (CPUs), microprocessors, and the like.

As used herein, "vibrating", "vibrate", "vibrated" or "vibrational" each refers to mechanical oscillations about an equilibrium point. The oscillations may be periodic such as the motion of a pendulum or random such as the movement of a tire on a gravel road. For example, vibrating a structure or a pipe is to affect the structure or pipe such that at least a portion of the structure or pipe undergoes mechanical oscillations about an equilibrium point.
As used herein, "pipe" refers to a hollow tube used for the conveyance of a fluid such as water, gas, steam, petroleum, or the like. The cross section of a pipe can have any shape such as circular, elliptical or rectangular.
As used herein, "joint" refers to the place at which two things, or separate parts of one thing, are joined, mated or united, either rigidly or in such a way as to permit motion. For example, two pipes may be united at a joint, wherein a male terminus of one pipe is mated to a female terminus of a second pipe, or a male terminus of a pipe is mated with a female terminus located at a terminus opposite the male terminus in the same pipe. Another example would be the case where one end of one pipe is welded to one end of another pipe. Furthermore, two I-beams, two cables, or two pipes may be united to form a joint using any means of anchorage or adhesively bonding of such as flanges or the like.
As used herein, "dynamic response" or "vibrational response" refers to the mechanical oscillations experienced at the joint of a structure, e.g., a pipe joint, or other structural joint, when the structure is vibrated.
As used herein, "healthy" refers to a state of a structure wherein the structure is substantially free of damage. For example, a healthy pipe is a pipe that can convey a fluid throughout the pipe's length without leaking. A healthy pipe joint is a pipe joint that is substantially free of damage, wherein the term "joint" is defined above. A healthy pipe joint does not leak the fluid that it conveys. A healthy pipe joint can undergo excitation forces (e.g., vibrations or explosions in a closed field without significant loss of structural integrity, i.e., the joint does not leak and/or the joint can undergo future excitation forces. Furthermore, healthy pipes are substantially free of corrosion (e.g., a reduction of less than about 15 % of the joint wall thickness, a reduction of less than about 10 % of the joint wall thickness, a reduction of less than about 5 % of the joint wall thickness, a reduction of less than about 1 % of the joint wall thickness, or reduction of less than about 0.5 % of the joint wall thickness), and fluid leaks (e.g., less than about 0.5% of the fluid flow leaks, less than about 0.1 % of the fluid flow leaks, or less than about 0.05% of the fluid flow leaks).
As used herein, an "output device" is a device that creates an effect that is detectable using one of the human senses, i.e., sight, sound, smell, touch, or taste. For example, an output device could include a siren that produces an audio alarm, a computer monitor or television screen that produces images and/or displays information, or an output device can be a light bulb or LED
that emits a wavelength of electromagnetic radiation in the visible light spectrum when activated.
As used herein, "pipeline" refers to a structure that comprises more than one pipe, wherein each of the pipes is mated to at least one other pipe to form one or more joints.
As used herein "biasing" in electronics is the process of establishing pre-determined voltages or currents at various points in an electric circuit so as to set an appropriate operating point (i.e. Q-point) As used herein "analog filter" is any filter, which operates on continuous time signals As used herein "differential amplification" is when an amplifier will amplify the difference between two voltages, but does not amplify the particular voltages. Using differential amplification facilitates signal processing in highly dampened structures. In these circumstances, measurements would fall into the very narrow band of the data acquisition (DAQ) measurement. As a result, maximum resolution would not be achieved, which could potentially affect the resolution of a damage detection system Previous Art Bakis, C.E., et al., "Adiabatic Thermoelastic Measurements," Section VIIB of Manual on Experimental process for Mechanical Testing of Composites, R.L. Pendleton and M.E. Tuttle, Eds., Soc. for Experimental Mechanics, Bethel, PA, 1989 In the above work, the authors used Ometron SPATE 8000 apparatus, consists of a scanning infrared photon detector coupled to a correlator (lock-in amplifier) and computer to measure the small temperature changes and the adiabatic thermoelastic effect in elastic solids.
Biemans et al., "Crack Detection in Metallic Structures Using Broadband Excitation of Acousto-Ultrasonics", Journal of Intelligent Material Systems and Structures, August 2001, Vol.12, pp. 589 ¨
597.
The above work is concerned with a sensitivity study. In essence, the study examines the sensitivity of different statistical parameters (in three domains: time, frequency and wavelet) to the growing crack in an aluminum plate. Only two out of the seven statistical parameters extracted from the time and frequency domain analysis of the two sensor's data proved to be sensitive (capable of predicting the crack growth in a linear fashion). However, there is no quantitative report on the degree of the sensitivity of those parameters. Compared to this process, our process does not require the selection of a specifically sensitive parameter. Most importantly, our process is capable of detecting the progression of the damage with a reliable degree of sensitivity, while there is no indication that the statistical parameters-based process could do this.
Cheraghi et al., A Novel Approach for Detection of Damage in Adhesively Bonded Joints in Plastic Pipes Based on Vibration process Using Piezoelectric Sensors, Conference Proceedings - 2005 IEEE
International Conference on Systems, Man and Cybernetics October 2005, Vol. 4 pp. 3472 - 3478.
In the above work, conducted by the authors, they compare the damage indices obtained by applying an Empirical Mode-Decomposition (EMD), a less refined version of the process disclosed in this patent document (EMD-based), with those obtained by processing the vibration signals by the wavelet and Fast-Fourier Transform signal processing techniques, thereby demonstrating the advantage of using EMD as a signal processing technique.
Cheraghi, N & Taheri, F. (2007). A Damage Index for Structural Health Monitoring Based on Empirical Mode Decomposition, Mechanics of Materials and Structures, vol. 4, pp. 43-62.

This work is very similar to the aforementioned work, with one added example, in which the integrity of a pipe having damage in the form of a narrow segment of a pipe having a thinner thickness is considered.
Guyott, C.C.H, et al., Use of the Fokker Tester Joints with Varying Adhesive Thickness, Proceedings of the institution of Mechanical Engineering, Part B: Management and Engineering, Vol. 201, pp. 41-49.
In the above work, the authors detect damage in joints using a frequency-based process, not by an energy-based process. They used an instrument to monitor one of the natural frequencies of the system comprising a piezoelectric crystal coupled to the joint. They investigated the resonant frequencies of two different sizes of transducer coupled to both plain plates and adhesive joints, theoretically and experimentally. They demonstrate that the process is capable of identifying the location of disbands, but that it cannot distinguish between adhesive modulus (i.e. adhesive's stiffness) and adhesive layer thicknesses. This is a more or less a technique for adhesive properties characterization rather a damage detection technique.
Huang, N.E., et al. A new view of nonlinear water waves: the Hilbert spectrum, Annual Review of Fluid Mechanics, 1999, Vol. 31, pp. 417¨ 457. This publication provides a general description of the principles of the Hilbert spectrum approach.
Huang, N.E, et al., The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London-Series A, 1998, Vol. 454, pp. 903-995.
Huang et al (authors of the above two, and numerous other articles) are the folks that developed the EMD signal processing process. This process is the foundation of the damage index that has been established by our process. So, these papers provide the fundamentals of signal processing by the EMD approach.
Heslehurst, R. B., Obsevations in the structural response of adhesive bondline defects, Journal of Adhesion & Adhesive, 1999, Vol. 19, pp.133-154.
This paper addresses a practical non-destructive process for identification of adhesive related defects.
They examined several NDTs for damage detection of adhesively bonded joints and in agreement with us, state that most conventionally used NDTs (e.g., ultrasonic) are ineffective in identifying the weak bonds. They use a holographic interferometry test process. While this process is an effective process for establishing weak adhesive bonds, it is not suitable for real-time monitoring; it is a laboratory quality assurance type test process.
Kumar et al., Artificial Intelligence and Image processing Approaches in Damage Assessment and Material Evaluation, Conference Proceedings - 2005 International Conference on Computational Intelligence for Modelling, Control and Automation, November 2005, Vol.!, pp.
307 ¨ 313.
This is also a work produced by the author of this patent. In this work, a wavelet signal processing process was used for image processing purposes. The image could be that of damage or any other type of image. In essence, the process disclosed in this paper automates the interpretation of images obtained through ultrasonic or x-ray NDT process.
Rizzo et al., Defect Classification in Pipes by Neural Networks Using Multiple Guided Ultrasonic Wave Features Extracted After Wavelet processing", Journal of Pressure Vessel Technology -Transaction of the ASME, August 2005, Vol.127, pp. 294 ¨ 303.
The guided ultrasonic wave (GUW) process in this work is used for detecting and quantifying a notch with varying sizes. Briefly speaking, a transmitter is used to generate the guided waves to the pipe, and a receiver records the eco from the pipe ends and the notch. The signal from the receiver is processed through the discrete wavelet, HT, and FFT. A damage index is defined by comparing the selected features for the direct signal and the signal that is reflected from the notch.
Similar to the process described in the present work, the damage index proposed by the present authors also yields distinctive results in detecting the progression of the damage.
However, it has been shown that the sensitivity of the damage index decreases as the distance between the receiver sensor and the notch increases. This is postulated to be the wave attenuation. For example, when the receiver is placed at 100mm from the notch, the resultant damage index from one on the damage-sensitive features varies between 20-80% for the different notch sizes (ranging from lmm to 5mm notch depth in a pipe with 60mm OD and 5.1mm wall thickness and length of 3000mm). The value of the damage index decreases to 4-20% for the same damage cases when the receiver is placed 900mm away from the notch. This reduction in the sensitivity to the damage detection is similar to EMD-EDI in the cases when the damage site is far from a sensor. The authors have however not shown nor proven in the preliminary stage of their work, the applicability and reliability of their process for damage detection under various conditions.
Wegman, R.F., et al., Non-destructive inspection, Chapter 11. Handbook of adhesive bonded structural repair. Park Ridge, NJ: Noyes Pub1.1992.
This chapter contains a description of non-destructive inspection (NDI) process that are particularly applicable to the quantitative evaluation and detection of defects in adhesive bonded structures, not damage control systems.
Wickerhauser, M. V., Adapted Wavelet Analysis from Theory to Software, A K
Peters, Ltd., Wellesley, MA (1994). This is a general purpose textbook on the wavelet signal analysis process, which provides basic principles of wavelet theory.
Yang et al (also in this paper: Yang JN, Lei Y, Lin S and Huang N. Hilbert-Huang based approach for structural damage detection. J. Eng. Mech. 2004; 130(1): 85-95, actually introduced two damage detection process: the first process was used in detection of a sudden change created in the stiffness of a structure from the measured data due to the existence of damage, while the other process combined the Hilbert¨Huang Transform (HHT) with the EMD process to determine the changes in the structural natural frequencies. The first process actually works well if the damage involves an abrupt change in the structure's stiffness (such as say a large crack in a bridge girder); however, the process is not capable of detecting the damage caused by a gradual change in the structure stiffness, since it is based on the extraction of the damage spikes due to sudden changes in the structure. Therefore it is incapable of detecting local damages such as small cracks in (welded joints), delamination in composites, bolt loosening in a bolted joint, or defects in bonded joints, which are all detectable by our process.
The present invention is based upon previous work by the researchers whereby process of assessing damage on a joint that includes vibrating the test specimen, detecting the vibration of the specimen using one or more signal generating sensors, processing the signal(s), and applying a damage index to the processed signal(s), wherein the damage index incorporates a processed control signal generated by a sensor(s) at or near the test specimen when the specimen was healthy, i.e., in a substantially undamaged state.
Previous work relating to the invention provides process of detecting damage on any structure that can be vibrated and using the dynamic response to detect damage and/or determine the relative damage at a joint on the structure. Past process are also useful in detecting damage and/or determining the relative damage on a structural joint such as a pipe joint.
For example, one process (Cheraghi et al, 2005a) of detecting damage in a structural joint comprises vibrating a structure comprising at least one joint, e.g., a pipeline, detecting a vibrational response of the joint, transmitting the vibrational response to a processor as a signal, processing the signal, and applying the processed signal to a damage index to yield the relative damage of the joint.
'Healthy 'Damaged DI = x 100 'Healthy (2) where DI is the measure of relative damage at a joint when a test measurement is taken, 'Healthy is the value of the integral of the processed response signal of the vibrated healthy joint and 'Damaged is the value of the integral of the processed response signal of the vibrated joint at the time of the damage detection and/or relative damage determination. It is noted that at least some damage is present in the structural joint (e.g., pipeline joint) if DI is a nonzero number.
Relating to the present invention, many different signal processing process can be used to manipulate the vibrational response signal to create the processed signal that is applied to the damage index in equation (2) to detect damage and/or determine relative damage at a structural joint. For example, a signal can undergo FT, FFT, WT, or HHT and then be applied to the damage index expression in equation (1) to detect damage and/or determine relative damage in a structural joint.
For example (Cheraghi et al, 2005a), a structure comprising a joint is vibrated, a piezoelectric sensor detects the time domain) at the joint, which is transmitted to a processor.
The response signal is processed using FFT. Under FFT, the integral for the joint response is expressed as (3):
/ lx(c)ldco (3) wherein Ix is the value of the integral of the absolute value of the FFT
processed vibrational response signal, X(w).

In addition, (Cheraghi et al, 2005b), described the response signal as being processed using a Wavelet process of signal processing, and the expression of equation (3) becomes:
= = d (t)2 dt (4) wherein the wavelet packet component energy Ufik, n is the energy stored in the component signal dl' (t) . The recomposing dl , n is calculated according to expression (4b):
d +1,2n-1 d.r1,2n d"'n ¨ E g 1-2k (4b) wherein h(k) and g(k) are discrete filters as described in Wickerhauser, M.
V., (1994). Adapted Wavelet Analysis from Theory to Software, A K Peters, Ltd., Wellesley, MA, hereby incorporated by reference. The damage index is assessed according to equation (2).
In another example (Cheraghi et al, 2006) that employs an Empirical Mode Decomposition process of signal processing, the index used in equation (2) takes the following form:
0 (5) wherein IMF is the first intrinsic mode function of the signal.
For another example, the vibrational response signal of the joint is generated by a piezoelectric sensor, transmitted to a processor, and processed using HHT as described in Huang, N.E, Shen, Z., Long, S.R., Wu, M.C., Shih H.H., Zheng Q., Yen N.C, Tung C.C., and Liu H.H. "The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis".
Proceedings of the Royal Society of London-Series A, 1998, 454: pp. 903-995, which is hereby incorporated in its entirety by reference.
The Ix value above includes the FFT-processed response signal when the tested structure is healthy or the response signal when damage to the structure is assessed. The calculation of discrete approximation of FFT can be represented by:

X(CO) =x(rAt)ert At r=0 (6) Furthermore, in equation (1), m is the sensor number and the degree of freedom of the structure, n is the mode shape number. Damage is present in the structural joint when Din.n is a nonzero number. Damage indices can be similarly developed for signals processed using WT. In equation (7), DI x is the measure of relative damage at the joint when a test measurement is taken.

)(Healthy I )(Damaged Dix= __________ x100 !Healthy (7) hHealthy is the value of the integral of the wavelet- processed response signal of the vibrated healthy joint, IxDamped is the value of the integral of the wavelet-processed signal of the joint at the time of the damage detection and/or assessment. Structural damage is present in the joint if DI x is a nonzero number.
The damage index for an HHT-processed signal is:
imHealthy _ irntest Dime = ___ n n X100 'Healthy mn (8) wherein DI. is the measure of relative damage at the joint when a test measurement is taken, I=Healthy is the value of the energy of the HHT-processed response signal of the healthy joint as expressed in equation (5) above, IõõJest is the value of the energy of the HHT-processed response signal of the joint at the time of the damage detection and/or assessment.
The damage indices relating to the present invention, such as those described in equations (1), (2), (7), and (8), each have a term that represents the value of the integral of the processed response signal of a healthy joint. In the damage index, this term represents a control value that is used to measure the amount of relative damage in a structural joint at the time when the damage detection and/or relative damage is measured.
Moreover, during signal processing, a response signal can be transformed into different domains (e.g., voltage in a time domain, acceleration in a time domain, or strain in a time domain) to better interpret the physical characteristics inherent with the original signal.
In one aspect, a previous approach related to the present invention provides a process of detecting damage and/or determining the relative damage in a joint of a structure. In one example, the process of detecting damage and/or determining the amount of relative damage in a joint of a structure comprises vibrating a structure (e.g., a pipeline) having at least one joint, detecting or mapping a vibrational response from the vibrated joint, transmitting the vibrational response to a processor as a signal, processing the signal, and applying the processed signal to a damage index, which yields the amount of relative structural damage present in the joint.
Another aspect, related to the present invention involves a process of detecting damage and/or determining the amount of relative damage in a joint that mates more than one pipe (e.g., 2 or more pipes, 3 or more pipes, or 4 or more pipes). For example, the process comprises vibrating a pipeline, detecting or mapping the vibrational response from the vibrated joint, transmitting the vibrational response to a processor as a signal, processing the signal, and applying the processed signal to a damage index, which yields the amount of relative structural damage in the joint. A sensor that detects or maps the vibrational response from a vibrated joint can be situated anywhere such that the vibrational response of the exited joint is detected or mapped (e.g., the sensor is placed on the joint).

Processes related to the present invention are also useful for detecting damage and/or determining the amount of relative damage in joints on structures. Suitable structures include beams (e.g., I-beams or the like), pipes, cables or wires that can be vibrated (e.g., the cable or wire is under at least some tension), or the like. These structures can comprise members that are joined using any suitable coupling process. Such coupling process include, without limitation, adhesively bonding structural members, mechanically joining the members (e.g., with bolts, nails, screws, rivets, collars, friction, combinations thereof, or other fasteners), welding the members together, screwing a male member into a female member, combinations thereof, or the like.
In process related to the invention, the vibrational response of a vibrated joint is detected and/or mapped using any suitable sensor(s), the response is transmitted as a signal, processed using any suitable signal processing process, and applied to a damage index to yield the relative damage to the structural joint. Sensors useful in detecting and/or mapping the vibrational response of a joint and transmitting the response as a signal include, without limitation, piezoelectric sensors, accelerometers, dynamic displacement transducers, strain gauges, or the like. Structures can be vibrated using any suitable process. For example, a pipeline can be vibrated by suddenly closing or opening a valve upstream or downstream from the joint, while a fluid is flowing through the pipeline. In other examples, the structure or pipeline is vibrated by striking it with a hammer, contacting it with a piezoelectric actuator, contacting it with a tuning fork, contacting it with an electromagnetic actuator, exposing the joint to electromagnetic radiation, or the like.
A related response mechanism to the present invention provides a process of detecting damage and/or determining the amount of relative damage to a pipeline joint comprising vibrating the pipeline (e.g., using a piezoelectric actuator, or by suddenly closing or suddenly opening a valve upstream or downstream of the joint that halts or permits the flow of fluid there through), detecting the vibrational response of the joint using a piezoelectric sensor, transmitting the response as a signal to a processor, processing the signal using any suitable signal processing process (e.g., HHT, FT, FFT, WT, or the like), and applying the processed signal to a damage index to yield the relative damage to the structural joint.
The damage indices are useful for detecting structural damage in a joint as well as measuring the relative amount of damage. For instance, if the relative damage determination yields a nonzero number, then some damage is present in the joint.
Related to the present invention, the response signals obtained from the vibrated joint can undergo multiple signal processing process to remove excessive noise from the signal.
Another aspect related to the present invention provides process of quantifying the amount of damage to a structural joint comprising creating a calibration curve and applying an amount of relative damage corresponding to an unknown amount of actual damage to the curve to yield an actual amount of damage in the joint.
Previous problems have been addressed in the present invention by providing improvements within the process as follows:

Excitation process ¨
Previous work suggested that there needed to be an improvement in the excitation process of using a hammer, tuning fork, or other process, which could resolve inconsistency issues. The present invention addresses this problem by employing an electric hammer to create excitation of a test specimen.
Data Collection Sampling Rate ¨
Features such as integral rubber gaskets and bolted joints often present a challenge during vibration data collection because the system can become highly damped. Examination of the sampling rates of the vibration signals collected in the previous work has been deemed somewhat inadequate, thus altering the accuracy and integrity of the damage indices evaluated by previous process. The use of an electric hammer increased the excitation rate by five to ten times the previous rate, thus improving collection speed and accuracy.
Effective Signal Amplification -Generally, a signal conditioning step is mandatory in any acquisition system.
This pre-processing stage mostly includes the application of low and high pass analog filters and amplification. Analog filters efficiently help with the reduction of noise in the portion of the signal that suffers from aliasing, as well as the ambient noise. The differential amplification will come handy in highly damped structures, in such cases, the measurements would lay in the very narrow band of the data acquisition (DAQ) measurement. As a result, the maximum resolution could not be achieved, and hence could potentially affect the damage detection resolution.
To overcome this issue, in the present invention, a signal conditioner with an embedded analog amplifier is adopted to elevate and improve the quality of the collected vibration signals, hence exploiting the maximum achievable resolution of the data acquisition (DAQ) system. As a result, one can successfully collect the vibration data from a highly damped and bolted joint assembly of a real-scale pipe. As stated earlier, bolted joints used to mate pipes, include integral rubber gaskets, which highly dampen the vibration signals. The use of the appropriate amplification scheme effectively combats this challenge and resolves the issue, thereby improving signal quality (with respect to the signal noise ratio (SNR)).
Use of Wireless Data Acquisition system ¨
Razaei & Taheri (2009) postulated that the first oscillatory mode of a structure's vibration would be the most dominant element among the excited modes, while the incorporation of higher modes would jeopardize the damage detection's resolution, since the higher modes of vibration are generally adversely affected by noise. It was verified that the first frequency would not always be the dominant frequency component and its' effectiveness would depend upon the location of the sensor and the location at which the structure (component) is excited. Past approaches have not addressed this important problem. In these cases, the gathered vibration signals would have to be conditional and amplified with a suitable signal conditioner before digitizing in the DAQ, otherwise the resolution and accuracy of the damage detection would be significantly lowered.
The present invention employs a wireless DAQ, which increases the utility and robustness of the previous approach quite significantly because one can effectively monitor the health of systems that are in obscure and/or inaccessible locations (e.g. being surrounded by water, where a high damping is imposed inherently by a joint assembly and the effects of a gasket that sits between the mating flanges).
Effective Band-Pass Filtering ¨
In 2010, Rezaei & Taheri suggested that an appropriate action might be to improve the band width range of the noise filter to take into account the vibration modes that would effectively contribute to the vibration of the structure. Previous background studies by the researchers had determined that only the first empirical mode decomposition (IMF) would be sufficient to establish a damage index for a joint damage detection system. Also, in (2010), Rezaei and Taheri suggested that a second IMF might be sensitive to the presence of some damage types in structures, since it may contain necessary higher modes of vibration. Previous approaches to signal filtering were not able to provide an upper limit that could achieve a reliable frequency content of a signal.
In the present invention, an improved process for acquisition of a signal within its band width range, as well as a noise filtering system that can collectively facilitate determination of damage in joints enabled an upper limit to be established for the band-pass filter by retrieving the analog signal at a higher frequency. The present invention thus provides greater signal resolution and hence greater data reliability by incorporation of a second IMF in the joint damage detection process.
Improving the quality of Calculated Energy Indices ¨
Discrepancies in calculating damage indices of a joint damage detection system were due to inevitable inconsistent vibration excitation created through use of a conventional hand-held instrumented hammer.
The present invention solves this problem by using an electronically controlled impact hammer, by which the structure/joint can be excited quite consistently, thereby improving the reliability and repeatability of the proposed damage detection indices.
REFERENCES
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Cheraghi N., G. P. Zou and F. Taheri. Piezoelectric-Based Pipeline Damage Assessment Using Fourier and Wavelet Analyses, International Journal of Computer-Aided Civil and Infrastructure Engineering, 20,2005: 369-382.
Cheraghi, N, Riley, MJ & Taheri, F. (2005), 'A Novel Approach for Detection of Damage in Adhesively Bonded Joints in Plastic Pipes Based on Vibration process Using Piezoelectric Sensors', IEEE International Conference on Systems, Man and Cybernetics, v 4, p.3472-3478.
Cheraghi N., G. P. Zou and F. Taheri. Piezoelectric-Based Pipeline Damage Assessment Using Fourier and Wavelet Analyses, International Journal of Computer-Aided Civil and Infrastructure Engineering, 20, 2005: 369-382.

Cheraghi, N & Taheri, F. (2007), 'A Damage Index for Structural Health Monitoring Based on Empirical Mode Decomposition', Mechanics of Materials and Structures, vol 4, pp. 43-62.
Engelberg, S. (2008), Digital Signal processing: An Experimental Approach, Springer, London.
Cheraghi N. M.J. Riley and F. Taheri. Application of Hilbert-HuangTransform for Evaluation of Vibration Characteristics of Plastic Pipes Using Piezoelectric Sensors, J. of Structural Engineering and Mechanics, 25(6), April 2007: 653-675.
Rezaei, D., and Taheri, F., (2009), "Experimental Validation of a Novel Structural Damage Detection process Based on the Empirical Mode Decomposition", Journal of Smart Materials and Structures, 18, No.4, doi: 10.1088/0964-1726/18/4/045004.
Rezaei, D & Taheri, F.( 2010a), Damage Identification in Beams Using Empirical Mode Decomposition', Structural Health Monitoring, vol 10, no. 3, pp. 261-274.
Rezaei, D & Taheri, F. (2010b), Health Monitoring of Pipeline Girth Weld Using Empirical Mode Decomposition', Smart Materials and Structures, vol 19, no. 5.
Esmaeel, RA, Briand, J. and Taheri,F (2011). Computational Simulation and Experimental verification of A New Vibration-Based Structural Health Monitoring Approach Using Piezoelectric Sensors, Structural Health Monitoring, vol.11, no.2, p.237-250, March, 2012 Esmaeel, RA, Briand, J & Taheri, F. (2011), 'Computational Simulation and Experimental Verification of A New Vibration-Based Structural Health Monitoring Approach UsingPiezoelectric Sensors', Structural Health Monitoring, p. DOI:10.1177/1475921711414239 Razi, P, Esmaeel, RA & Taheri, F. (2011), 'Application of a Robust Vibration-Based Non-Destructive process for Detection of Fatigue Cracks in Structures', Smart Materials and Structures, vol 20, no.11, doi: 10.1088/0964-1726/20/11/115017.
Razi, P, Esmaeel, RA & Taheri, F. (2012a), Application of a Remote Health Monitoring System for Pipeline Bolted Joints, to be presented in ASME Pressure Vessles and Piping Conference, July 15-19, Toronto.
Razi, P, Esmaeel, RA & Taheri, F. (2012b), Improvement of a Vibration-Based Damage Detection Approach for Health Monitoring of Bolted Range Joints in Pipelines. Submitted to Structural Health Monitoring, May, 2012.
(Wikipedia, http://en.wikipedia.org/wiki/Biasing). Ambient noise generally means background noise or the total noise in the surrounding environment (http://medical-dictionary .thefreedictionaly .com).
(https://ccrma.stanford.edu/ ios/fp/Analog_Filters.html).
(http://en.wikipedia.org/wiki/Differential_amplifier).
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BRIEF DESCRIPTION OF THE DRAWINGS
The aspects and the attendant advantages of the embodiments described herein will become more readily apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings wherein:
FIG. 1 is a schematic describing the decision-making steps of the present process.
FIG. 2 illustrates a block diagram of a bolted joint showing dimensions and impact locations.
FIG. 3 illustrates a block diagram of the damage detection process/process.
FIG. 4 is a series of graphs displaying electric hammer signals triggered by different sampling rates.
FIG. 5 is a series of graphs displaying a typical signal from a PZT bonded flange, (a) before, and (b) after amplification.
FIG. 6 is a series of graphs that displays calculated energy indices at different sampling rates.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Past works regarding damage detection of pipe systems have addressed damage as determined from consideration of semi-real damage scenarios. The current work addresses damage detection by monitoring the occurrence and progression of damage in real life situations (Esmaeel et al, 2011) and (Razi et al, 2011). Signal processing is considered a mandatory step in any data acquisition system.
This is a pre-processing stage, which includes mostly the application of low and high-pass analog filters and appropriate signal amplification. Analog filters efficiently assist in the reduction of noise in the portion of the signal that suffers from a biasing along with ambient noise.
FIG.1 describes the decision-making steps of the present damage detection process. According to FIG.
1, excitation of a joint would be monitored in a healthy state and/or in a distressed state. Signals are then processed through the Empirical Mode Decomposition approach and the EMD
energy is evaluated for the test case in the current state and for the future state. Then, the EMD
energy of the first and/or second IMF must be evaluated to provide the basis for the final step of calculating the damage index.
This progression of testing enables the user to identify the presence of damage and to ultimately determine the location of the damage.

FIG. 2 is employed to illustrate various major refinements over prior art that are part of the present invention. The set-up in FIG. 2 consists of ASTM compliant schedule 40 Grade B
standard steel pipes (ASTM A53/A53M ¨07) (1) mated by a bolted flange type joint (2) that were chosen for illustration and verification purposes. The selected ANSI forged steel flanges (2) were of the following type:
nominal pipe size: 6 in., class: 150 lb, raised face, slip-on and material type A105N. SAE Grade 5 UNC hex head bolts were used (diameter: 3/4" and length: 4"), with the corresponding sized nuts and washers. The pipes (1) main dimensions and properties are summarized in Table 1.
In this case, piezo-ceramic sensors (3) are used to monitor the system's vibration; four piezo-ceramic sensors (PZT1:PZT4) (3) are bonded onto one of the flanges with an additional four sensors (PZT5:PZT8) (3) bonded on the circumference of the pipe (1) at equal angles as shown in FIG .2 For this specific joint configuration (FIG. 2), the maximum bolt torque was determined to be 124.7 N-m. The procedure for tightening the bolts followed the industry standard, which was a criss-cross pattern across the face of the flange, using a torque wrench, at increments of 30%, 60%, and then 100%
of the maximum torque.
Table 1: Pipe dimensions and properties Length (m) 3.52 Outer Diameter (mm) 168.3 Thickness (mm) 6.35 Density (kg/m3) 7800 Young's Modulus 200 (GPa) Poisson's ratio 0.3 According to FIG. 3, there is an electric hammer (4), which produces an excitation in a test specimen (5). Past approaches at excitation produced problematic inconsistencies in data collection. Previous approaches at overcoming these inconsistencies in order to derive a usable signal, involved a judicious procedure of discarding some of the collected signals, then applying an averaging technique to the remaining hammer signal data, which still produced less than accurate data as well as being time consuming and costly. Razi et al. (2012a) employed an electric hammer (4) to collect digital data for a joint damage detection process and found it greatly increased the accuracy and efficiency of data collection within the process, also without a need to discard any of the collected signals.
Considering the employment of an electric hammer (4) to create excitation in a test specimen (5), since the process is an energy-based approach, the consistency of the excitation is of paramount importance and governs the accuracy and integrity of the technique. The use of a conventional manually operated instrumented hammer (Razi et al, 2012a) can distort the outcome of the damage detection practice.
This is mainly due to the fact that the orientation, location, and amplitude of the impacts driven by a manual hammer could vary significantly from one excitation to another and from one operator to another.
Hence, the use of an electric hammer (4) could effectively address the abovementioned factors, thereby facilitating consistent and reproducible impacts. Furthermore, when using a conventional hammer, one must excite the system within several trials and take the average of the acquired signals, so as to reduce the inconsistency. The use of the electric hammer (4) will eliminate the requirement for multiple excitations (Razi et al, 2012a).
In the procedure disclosed during past research (Rezaei and Taheri, 2010), it was noted that the vibration signals gathered through piezoelectric sensors and that of the hammer "must be normalized"
with respect to the hammer's signal to reduce the inherent inconsistency in the signal(s) produced by the conventional instrumented hammer. Accordingly, the sensors' signals would have to be divided by the hammers' signal in the frequency domain. The resultant signal would be then transferred back into the time domain for further analysis. Importantly, this onerous and tedious normalization step can be skipped when an electric hammer is used, since it produces consistent impacts from the perspective of both frequency and amplitude.
Data acquisition at a high sampling rate, using an electric hammer (4), serves two main purposes; (a) to account for the higher modes, which are more indicative of potential damage, and (b) to facilitate the registration of the electric hammer signal with appropriate accuracy and resolution, since any misrepresentation of the hammer signal would adversely affect the efficiency of the normalization process that must be directed onto the impact signal.
A Lazer Doppler Vibrometer was used initially by Rezaei and Taheri (2010a) to test its applicability for data correlation. According to FIG .3, in the present invention, there is a Lazer Doppler Vibrometer (LDV)(6) and wireless sensors (7) used to collect digital signal data remotely, without the need to attach a sensor to the structure (Rezaei & Taheri, 2012-b). Also in the present invention, Razi et al.
(2012) adopted a wireless receiver (8) for data acquisition, which employs sensors that are wired to the carrier through an analog to digital converter module (ADC)(9) that actually sits in the wireless carrier to increase the effectiveness and robustness of the current damage detection process. The wireless data acquisition system (WDAQ)(9) implemented was a model WLS-9163, produced by the National Instruments Inc. (Texas, USA). This implementation increases the utility and robustness of the current technique quite significantly because, one can effectively monitor the health of systems that are in obscure and/or inaccessible locations (e.g., joints of risers on oil platforms, etc.). This approach permits remote collection of vibration data with high sampling rates, thus negating the need to attach a sensor to the structure.
The hammer' signal (10) and the vibration signals (11) registered with the bonded piezoelectric sensors (7) were digitized with the WDAQ (9) using a 50 kHz sampling rate.
Commercially available wireless nodes presently used in the industry could not accommodate the high sampling rate required by the present invention process (i.e. they are mostly limited to 10 kHz). The presently adopted WDAQ (9), however, can provide a sampling rate of 100 kHz. The WLS-9163 also embodies a 12 MB RAM, which ensures a secure transmission of data; this feature enables the device to temporarily store 14 seconds of data (sampled at the maximum rate). Thus, the present invention provides a very significant increase in efficiency and performance over past Art.

According to FIG .4, various hammer signals (10) are produced by different sampling rates, which range from 10kHz to 501(H. Typical hammer signals registered by different sampling rates, ranging from 10 kHz to 50 kHz are depicted in FIG. 4.
As can be seen, the impulsive force occurs in about 0.1 millisecond, thus it necessitates the sampling rate to be set to at least Fs =V(0.1 mSec/5) =50 kHz) (i.e., five times greater than the present harmonic of the signal). Any lower sampling rates may jeopardize the appropriate registration of the hammer signal both in terms of shape and peak amplitude (as seen from the results shown in (FIG. 4). As such, it is postulated that the inefficient sampling rate applied in the previous works (i.e., 10 kHz) together with variable orientations of the impact in different trials, were the major causes of inconsistency in the obtained energy indices and the subsequent damage (Razi et al, 2012b).
According to FIG. 3, under many practical circumstances, the vibration signals (11) are dampened by various factors (e.g., being surrounded by water, or in this particular case, as a result of the high damping that is imposed inherently by the joint assembly, as well as the gasket that sits in-between the mating flanges). In such cases, the gathered vibration signals (11) would "have to be" conditioned and amplified with a suitable signal conditioner (12) before digitizing in the WDAQ (9), otherwise the resolution and accuracy of the damage detection would be significantly lowered.
This step is therefore a necessary one and is performed to raise the voltage level of PZTs' signals (11) in order to benefit from the maximum resolution that can be achieved after the signals are digitized in the WDAQ (9). Moreover, this amplification step reduces the effect of the noise that is an inherent part of the measurement. This important step contributes to attaining more acceptable signal to noise ratios (SNRs), thereby producing more reliable signals. This will in turn improve the accuracy and quality of the energy indices that are commutated, based upon the signals.
According to FIG. 5, a qualitative comparison of a typical signal of a PZT on the flange taken before and after the amplification is performed on the signal. The resulting increases in the SNRs of the signals after the amplification are tabulated in Table 2. The SNRs for different signals are calculated using the following equation:
I
SNR =20 log signal (9) cynoise Table 2: Effect of amplification stage on the SNR
Signal to Noise Ratio (SNR) Before After Amplification Amplification Sensors on the pipe 108 125 Sensors on the flange 55 121 As it can be seen from Table 2 and FIG. 5, the SNR value is noticeably increased in some of the signals. This is because those sensors experience the severe local damping produced, in this case, by the flange assembly. The digitized data are then sent remotely to a computer station for analysis purposes through a reliable and secure communication.
According to FIG.3, it should be noted that the normalized signals "must be"
band-passed with a digital filter" (e.g., Butterworth filter) (13) to maintain the useful portion of the data. The bandwidth of the filter is selected such that it would yield digital signals whose shapes are very close to that of the analog ones, and with the same frequency content. The normalized signals are then individually processed with the EMD method.
Recently, in 2012, lab trials were conducted by researchers of the present invention, to establish an upper limit for the band pass filter (13) and to accurately retrieve the shape of the analog signal. It was also determined that when an appropriate upper limit for the band pass filter (13) was established, the shape of the analog signal could be accurately retrieved at a higher frequency. It was also determined that a sampling rate of at least five or ten times greater than the highest frequency component of the analog signal was sufficient for the recovery of the analog signal (Razi et al, 2012b).
This rule of thumb may be applied to energy-based approaches or the like, where the reconstruction of the signal is of paramount importance to the integrity of the process. In other words, application of this rule provides an upper limit for the reliable frequency content of a signal.
Moreover, the selected band-width would remove the portion of the signals that suffer from aliasing.
Selection of the appropriate frequency band-width based on the explained "rule of thumb" (e.g. upper-band limit) will reduce the confusion and the subsequent trials and errors noted in previous works (Razi et al. 2012b).
According to FIG. 6, recently, it has been postulated that discrepancies in calculating damage indices of a joint damage detection system have been due to the consistent vibration excitation of a conventional hand-held instrumented hammer (Razi et al, 2012b).
FIG. 6 sheds light on the tangible inconsistencies in the calculated energy indices (14), up to 62 %, that could be potentially raised in various trials of the electric hammer (4) due to the implementation of lower sampling rates.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus the present disclosure is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features as defined by the following claims.
EXAMPLE of the USAGE of the INVENTION
The invention can be employed to determine damage in joint systems in a nuclear power plants.

SUMMARY OF THE INVENTION
In accordance with the description, there is provided a process of detecting damage and/or assessing relative damage on a structural joint that includes vibrating the structure that comprises the joint, mapping the vibrational response of the joint using one or more signal generating sensors, transmitting the vibrational response as a signal(s) to a processor, processing the signal(s), and utilizing the processed signal(s) to produce damage indices that yields the relative damage at the joint.
Specifically, in accordance with the description there are provided improvements to a process of damage detection in joint systems, whereby an electric hammer is used to improve the accuracy and efficiency of collecting data to calculate damage indices in a damage detection system (DAQ).
In accordance with the description, there are provided improvements to a process of damage detection in pipe and joint systems, whereby a Laser Doppler Vibrometer (LDV) is employed to collect signal data remotely, thus negating the need to attach a sensor to the test element to improve the efficiency of data collection. In addition, there is provided a wireless carrier set-up with sensors wired to the carrier through an analog to digital converter module (ADC), which enables collection of data from high sampling rates that greatly improves data collection speed and accuracy at a base computer station, thereby facilitating remote data collection. The wireless carrier is easily mounted on remote locations at test sites.
In accordance with the description, there are provided improvements to a process of damage detection in pipe and joint systems, whereby a special purpose signal conditioner with an embedded analog amplifier to assist in obtaining maximum resolution of vibration data obtained through any sensor (especially in the case when piezoelectric sensors are used), from various site locations.
In accordance with the description, there are provided improvements to a process of damage detection in pipe and joint systems, which employs a process of improving acquisition of a signal within its bandwidth range, as well as a noise filtering system that can collectively facilitate detection of damage in structural joints. An upper limit is established for the band pass filter by retrieving the analog signal at a higher frequency. Incorporation of a second IMF in the joint damage detection system (JDDS) has proven to yield greater signal resolution and hence greater reliability than previous work, by combining a first and second IMF, which will enhance the predictive accuracy of the (JDDS).
In accordance with the description, there are provided improvements to a process of damage detection in pipe and joint systems, whereby normalized signals are band-passed with a digital filter to maintain the useful portion of the data. In addition, an appropriate upper limit was established such that the shape of the analog signal could be accurately retrieved at a higher frequency. A sampling rate of at least five or ten times greater than the highest frequency component of the analog signal was sufficient for recovery of the analog signal.
In accordance with the description, there are provided improvements to a process of damage detection in pipe and joint systems, whereby damage detection is achieved and expressed through indices by processing of the captured vibration signals and processing the signal via a special process to evaluate the damage indices, specifically by using software that is either hosted on a remote computer or by directly installing the code into a remote processor.

Other aspects, advantages and features of the present disclosure will become apparent after review of the entire application, including the following sections: Brief Description of the Drawings, Detailed Description of the Drawings and the Claims.

Claims (24)

1. Improved process for consistent vibration excitation of the test article.

2. The process in Claim 1, wherein an inefficient data collection procedure was overcome by employing an electric hammer that could deliver consistent and repeatable impacts in terms of both magnitude and frequency, which considerably improves the accuracy and efficiency of the damage detection process.

3. A process of improving the band width range of the noise filter in a system that detects damage in structural joints.

4. The process of Claim 3, wherein an upper limit for the band pass filter is established in light of the fact that the shape of the analog signal could be accurately retrieved at a higher frequency.

5. The process of Claim 4, wherein a sampling rate of at least five or ten times greater than the highest frequency component of the analog signal is deemed sufficient for nearly full recovery of said analog signal.

6. Improved process of incorporating IMF in a joint damage detection system.

7. The process in Claim 6, wherein the incorporation of a second IMF in a damage detection system was proven to yield greater resolution and thus greater reliability than prior work.

8. The process in Claims 6 and 7, wherein an effective combination of a first and second IMF will enhance the predictive accuracy of a joint damage detection system.

9. Improved process of collecting digital signal data in a damage detection system, wherein a Laser Doppler Vibrometer (LDV) is used to collect signal data remotely without the need to attach a sensor to the structure being tested, thus greatly improving the accuracy of data and efficiency of the operation.

10. Improved process of collecting data in a joint damage detection system, using a wireless carrier set-up for data acquisition, whereby said carrier employs sensors that are wired to said carrier through an analog to digital converter module (ADC) and said sensors actually sit in said wireless carrier.

11. The process in Claim 10, wherein said wireless carrier or node(s) are compact and easily mounted at locations in structural systems where damage is likely to occur and which are often difficult to access.

12. The process in Claim 11, wherein said mounting locations refer to places such as underwater oil production platform leg joints, pipe joints under the earth and piping systems in refineries, chemical plants, petrochemical complexes, water distribution and treatment facilities, sewerage treatment facilities and nuclear power plants and other similar industrial infrastructure.

13. The process in Claims 11 and 12, wherein said mounting procedure makes continual data acquisition from said remote locations much easier, quicker, cheaper and produces more reliable data.

14. The process in Claim 10, wherein said set-up is a data acquisition system that permits collection of vibration data with high sampling rates, whereby said data can be securely transmitted to a base computer station for storage and analysis, which provides a notable improvement in collection speed, accuracy and cost of operation.

15. Improved process of obtaining maximum achievable resolution after the signals are digitized in said data acquisition system, wherein a signal conditioner with an embedded analog amplifier is employed to elevate and improve the quality of the vibration data collection procedure.

16. The process in Claim 15, wherein the amplification procedural step reduces the effect of the noise that is an inherent characteristic of the test measurement.

17. The process in Claims 15 and 16, whereby said embedded amplifier permits the collection of vibration data from locations such as a highly damped and bolted joint assembly of a real scale pipe.

18. The process in Claims 15, 16 and 17, whereby this step is refined in terms of and contributes to achieving more acceptable signal to noise ratios (SNRs), which produces noticeably more reliable signals.

19. Improved process of maintaining the useful portion of process data by using a digital filter to band-pass the signals such that the distinct vibration modes that are more pronounced and that contribute to the systems' vibration can be extracted and preserved for further processing.

20. The process in Claim 19, wherein the bandwidth of the filter is selected such that it will yield digital signals whose shapes are very close to that of the analog shapes and which exhibit the same frequency content.

21. The process in Claim 19 and 20, wherein band-passing a signal through said digital filter will maintain the useful portion of the data, while removing signals that suffer from aliasing.

22. Improved process for calculating damage indices in a joint damage detection system.

23. The process in Claim 22, wherein inclusion of a second IMF signal, in some cases, will produce greater resolution in the damage detection indices.
All appropriate Claims herein pertaining to joint systems may also be considered as pertaining to pipes, pipelines and structural members.

indicated to be incorporated by reference. Should the meaning of the terms in any of the patents or publications incorporated by reference conflict with the meaning of the terms used in this disclosure, the meaning of the terms in this disclosure are intended to be controlling.
Furthermore, the foregoing discussion discloses and describes merely exemplary embodiments of the present invention. One skilled in the art will readily recognize from such discussion and from the accompanying drawings and claims, that various changes, modifications and variations can be made therein without departing from the spirit and scope of the invention as defined in the following claims.

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