The disclosure relates to robotic inspection of a part and, more particularly, to an automated system and method for inspecting mechanical components, especially turbine blades.
It is well known that mechanical components may incur imperfections during manufacturing and may suffer wear and damage during operation. These components, therefore, are episodically or periodically inspected for imperfections, damage, deformation, and wear. In particular, mechanical components such as turbine disks and airfoils have complex mechanical shapes that are difficult to manufacture and are operated under stresses that cause damage, e.g., leading edge erosion, hot corrosion (sulfidation), cracks, dents, nicks, gouges, etc. from foreign object damage. These turbine disks and airfoils are currently inspected manually by visual inspection or by non-destructive evaluation (NDE) techniques such as eddy current, ultrasound, or fluorescent penetrant inspection. These inspections are tedious, time consuming, imprecise, and error prone. Techniques to automate these types of inspection have been emerging, but the automated detection and operation can be improved.
Manual turbine blade damage detection is known in the prior art, e.g. using embedded vibroacoustic sensing and strain sensing. This prior art uses embedded or contacting sensors and is intended for wind turbines where the blades to not suffer the impact, erosional, or corrosion that gas turbine blades suffer. Also, blade damage detection for gas turbines is known, e.g. using eddy current sensors or by using mechanical touch probes, e.g., a coordinate measuring machine (CMM), but these require slow, laborious scanning of the blade surface.
It is known to process borescope video of blades in an engine to determine damage. This approach analyzes two dimensional (2D) images for differences between the current image and a model learned from other 2D images in a blade row. This is not suitable for determining damage in absolute units for components outside an engine. There is additional prior art for 2D (image-based) inspection systems wherein many steps are performed to determine the pose of an inspection device (a camera) with respect to the part and an a priori engineering model so that differences between the part and the model may be determined. This particular approach is unnecessarily inefficient and error prone. Some methods use X-rays, which requires special, shielded equipment.
In accordance with the present disclosure, there is provided a method for robotic inspection of a part, which comprises the steps of: supporting the part with a robot mechanism; obtaining part-related sensor input with a sensor positioned to inspect the part supported by the robot mechanism; controlling movement of the robot mechanism relative to the sensor, wherein the controlling is done by a feedback control unit which receives the sensor input, and the feedback control unit is configured to control the robot mechanism based upon the sensor input.
In accordance with a further non-limiting embodiment, the method further comprises the steps of storing the part-related sensor input, or information derived therefrom, as past sensor input in a storage in communication with the feedback control unit and controlling movement of the robot mechanism based upon current sensor input and the past sensor input.
In a further non-limiting embodiment, the feedback control unit is further configured to plan a path of movement, relative to the sensor, of the part supported by the robot mechanism, wherein the path of movement is determined based upon the past sensor input.
In a further non-limiting embodiment, the sensor has a controllable lens and the feedback control unit is configured to control the lens based upon the part-related sensor input.
In a further non-limiting embodiment, the sensor further comprises a plurality of lenses and an automated lens changing system for positioning a lens of said plurality of lenses along a line of sight from the sensor to the part, and the method further comprises the step of changing the lens along the line of sight based upon the part-related sensor input.
In a further non-limiting embodiment, at least one filter is positioned along a line of sight from the sensor to the part, and the method further comprises the step of operating the filter based upon the part-related sensor input.
In a further non-limiting embodiment, the method further comprises illuminating the part with an illumination mechanism, and the feedback control unit is configured to control the illumination mechanism based on the part-related sensor input.
In a further non-limiting embodiment, at least one filter is positioned along a line of sight from the light mechanism to the part, and the method further comprises the step of operating the filter based upon the part-related sensor input.
In a further non-limiting embodiment, the illumination mechanism further comprises a controllable lens, and the feedback control unit is configured to control the lens based upon the part-related sensor input.
In a further non-limiting embodiment, the feedback control unit is configured to control the robot mechanism based upon the sensor input and manual input.
In a further non-limiting embodiment, the method further comprises the step of annotating the part based upon the part-related sensor input.
In a further non-limiting embodiment, an inspection system for robotic inspection of a part, comprises a robot mechanism configured to support the part, the robot mechanism being moveable to adjust position and pose of the part; a sensor positioned to obtain part-related sensor input of the part supported by the robot mechanism; and a feedback control unit in communication with the sensor to receive the part-related sensor input, the feedback control unit being configured to control movement of the robot mechanism based on the part-related sensor input.
In a further non-limiting embodiment, the system further comprises a storage in communication with at least one of the sensor and the feedback control unit, the storage being configured to receive and store the part-related sensor input or information derived therefrom.
In a further non-limiting embodiment, the feedback control unit is further configured to plan a path of movement, relative to the sensor, of the part supported by the robot mechanism, wherein the path of movement is determined based upon the past sensor input.
In a further non-limiting embodiment, the sensor has a controllable lens and the feedback control unit is configured to control the lens based upon the part-related sensor input.
In a further non-limiting embodiment, the sensor further comprises a plurality of lenses and an automated lens changing system for positioning a lens of said plurality of lenses along a line of sight from the sensor to the part, and the feedback control unit is configured to change the lens along the line of sight based upon the part-related sensor input.
In a further non-limiting embodiment, at least one filter is positioned along a line of sight from the sensor to the part, and the feedback control unit is configured to operate the filter based upon the part-related sensor input.
In a further non-limiting embodiment, an illumination mechanism is provided for illuminating the part, and the feedback control unit is configured to control the illumination mechanism based on the part-related sensor input.
In a further non-limiting embodiment, at least one filter is positioned along a line of sight from the illumination mechanism to the part, and the feedback control unit is configured to operate the filter based upon the part-related sensor input.
In a further non-limiting embodiment, the illumination mechanism further comprises a controllable lens, and the feedback control unit is configured to control the lens based upon the part-related sensor input.
In a further non-limiting embodiment, the feedback control unit is configured to control the robot mechanism based upon the sensor input and manual input.
Other details of the process are set forth in the following detailed description and the accompanying drawings wherein like reference numerals depict like elements.
The present disclosure relates to the automated inspection of a part such as a turbine blade or the like.
Various mechanical system components such as turbine blades, disks, and airfoils, require inspection for damage, defects, the need for repair and/or maintenance, and the like. One form of such inspection is automated inspection. During such inspection, the part is supported by a robot mechanism in a position relative to a sensor such that the position and pose of the part relative to the sensor can be adjusted primarily through movement of the robot mechanism. As disclosed herein, the position and pose of the part relative to the sensor are adjusted based upon part-related sensor input so that control of the position and pose of the part can be coupled to automated detection or inspection results. Thus, following this approach, an inspection can be conducted to focus more specifically on a location of a part which the current and/or past inspection has indicated a reason for further and/or more focused inspection to be conducted. Thus, according to the disclosure, robot motion is coupled to automated detection results. Another approach would be a “hybrid” approach, wherein the system is mostly automatic, but is configured to reach out to some other system or person for either confirmation, for example to help avoid re-certifying the inspection process, for annotation, and for guidance such as, for example, where to pay attention next, or for further analysis. For example, the system could reach out to a human on site or at a remote station, humans in the cloud or a more powerful image processing system in the cloud.
Robot mechanism 12 can have various support mechanisms to hold part 18, such support mechanisms being schematically illustrated by graspers 19 in
Robot mechanism 12 is typically a fully articulated arm configured to allow multi direction adjustment of the position of a part, and particularly including rotation and orientation of the part at a particular location, which is referred to herein as the pose of the part. The position in which robot mechanism 12 holds part 18 is with respect to sensor mechanism 14, and the different positioning and pose allows for full inspection of the part.
Sensor mechanism 14 can be any of a wide variety of different sensors, such as image sensors, thermal sensors, or the like. Sensor mechanism 14 can, by way of further non-limiting example, comprise one or more two-dimensional (2D) cameras, three-dimensional (3D) depth sensors, and/or sonic sensor arrays, operating in any portion of the electromagnetic spectrum or acoustic spectrum (as relevant), to capture current information of a part under inspection. In the non-limiting embodiment of
The information or sensor input is processed and stored in a database, for example in storage unit 22, in such a way that relationships between the current inspection and previous inspections are established. The results of the current and previous inspections are provided to feedback control unit 16. One way to establish relationships is to add metadata about each inspection to the data stored in the database such as the individual part type, serial number, inspection date and time, inspection software version number, and the like.
Feedback control unit 16 can be any of a wide variety of processing units configured to execute and/or send various machine language commands including but not limited to commands which can be sent to robot mechanism 12 for controlling movement thereof. In addition, feedback control unit 16 can be configured to process sensor input from sensor mechanism 14 or receive processed sensor input from image processing unit 20. Feedback control unit 16 can also be in communication with storage unit 22 for accessing past part-related sensor input, for example. Feedback control unit 16 can also be in communication with a separate storage unit 24, which may contain various programming and machine executable instructions for controlling operation of the feedback control unit 16.
Of particular interest in the present disclosure, feedback control unit 16 is advantageously configured to control movement of the robot mechanism based on feedback from the sensor mechanism of a current inspection. Thus, if the part-related sensor input received in real time from the sensor mechanism indicates an area of a part needs to be inspected more closely or at a different pose, feedback control unit 16 is configured to send instructions to the robot mechanism to make such adjustments and enhance the inspection. In one non-limiting embodiment, feedback control 16 may implement a feedback control algorithm designed to reduce uncertainty or resolve a non-binary probability in damage detection of part 18. That is, image processing unit 20 may use a statistical damage detection algorithm that provides a probability of detection. If in any instance this probability is not zero or one, feedback control unit 16 may iteratively change the pose, illumination, and/or sensing of part 18 to drive the probability or detection to either zero or one. The feedback control may be considered or implemented as an optimization process with uncertainty as its objective function. In one case, the optimization may comprise a complete exploration of the parameter space.
Also of interest, since feedback control unit 16 has access to past part-related sensor input, which for example may be stored in storage unit 22, the initial movement path of robot mechanism 12 can be set or adjusted to focus specifically on areas of already identified interest, such as defects already detected in a disk, blade row, or the like. Thus, feedback control unit 16 provides path planning based on prior information such as past part-related sensor input.
A further aspect of interest with respect to interaction of feedback control unit 16 in system 10, related to the path planning mentioned above, is to design the initial movement path of robot mechanism based on past part-related sensor input such that a maximum amount of information is gained by the inspection. In other words, the position and pose of the part can be selected by feedback control unit 16 such that knowledge of a particular part being inspected is maximized per each movement path of robot mechanism 12 and part 18 supported thereby relative to sensor mechanism 14.
In a further non-limiting aspect of the present disclosure, an illumination mechanism 26 can be provided for illuminating part 18 supported by robot mechanism 12 such that sensor mechanism 14 can obtain more clear and well illuminated part-related sensor input. Illumination mechanism 26 can be any suitable source of light which is suitable for enhancing inspection of a part. Such light can be in the visible spectrum, or in other spectra suitable to various types of inspection which may be desired. Further, illumination mechanism 26 can be controlled by feedback control unit 16 in a basic sense to power on or off, and also to increase or decrease intensity and/or type of illumination, such as wavelength, band of wavelengths, polarization, spatial structure, and the like. Controllable filters can be included in both the illumination mechanism 26 and sensor mechanism 14 to allow automated selection of wavelength and polarization.
All such control can be based upon current or past part-related sensor input. For example, if past part-related sensor input indicated a portion of a complex structure was not properly illuminated during inspection, position and pose of the part can be adjusted, as can intensity of light from illumination mechanism 26, to obtain better part-related sensor input in the next inspection and/or for the remainder of the current inspection.
Returning to sensor mechanism 14, a further non-limiting embodiment includes sensor mechanism 14 having a controllable lens schematically illustrated at 28. Controllable lens 28 can allow for enhanced specific inspection of a particular area of a part, thus enhancing proper positioning and pose of the part relative to the sensor by allowing focus from the sensor side as well. Lens 28 can be controlled by feedback control unit 16 as with other components of system 10, again so that results of current and past part inspection can be used to enhance movement of robot mechanism 12 and focus of sensor mechanism 14 to produce more accurate and effective inspection. It should also be appreciated that within the scope of a controllable lens 28 is a lens system with physical lens changes, for example where the desired or intended change (e.g. magnification) is more than can be accomplished with a single adjustable lens. In this configuration, the system can be configured to operate an automated lens changing system.
As set forth above, one or more filters can also be incorporated into system 10 to modify operation of either sensor mechanism 14 and lens 28, or illumination mechanism 26 and/or lens 30.
Similarly, illumination mechanism 26 can have a controllable lens 30 which can be used to focus, diffuse, or otherwise adjust light being directed to part 18 during inspection. Feedback control unit 16 is configured and in communication with illumination mechanism 26 to control lens 30 to focus light from illumination mechanism 26 in a way which is directed by results of current or past inspection and part-related sensor input. Again, by way of non-limiting example, if a portion of a part is not clearly seen in images obtained during either a prior or current inspection, this will be identified by feedback control unit 16 and instructions sent to robot mechanism 12 to change position or pose of the part relative to sensor mechanism 14, and also in this case to adjust focus of light emitted by illumination mechanism 26 to more fully illuminate a formerly obscured or not clearly inspected area of the part. Lens 28 and/or filters 31, 33 could also be adjusted.
Still referring to
The method of operation of system 10 can be further discussed and understood through consideration of
As disclosed herein, feedback control unit 16 operates to provide one or more of the following:
Repositioning based on the current inspection, e.g., to re-inspect from a different relative location or pose and combine the new inspection results with the previous results using, for instance, a Bayesian damage estimator, to produce a first fused inspection result. The re-inspection process may now continue with the first fused inspection result used as the previous inspection result. This iterative process can terminate when the fused result is the same as the previous result or when a predetermined number of iterations is reached, for example.
Inspection path planning can be based on prior information such as already detected defects in a disk, blade row, and the like. That is, a default sequence of location and pose robot controls may be modified based on the spatial probability of damage and the probability that the current part is related to previous parts. This path planning may exploit UAV probabilistic search path planning as modified for part similarity.
Inspection path planning can also be based on expected information gain. The expected information gain is the change in information from a prior state, or previous inspection, to a state that takes some information as known (current inspection). That is, a default sequence of location and pose robot controls may be modified to make a next inspection where the expected information gain is maximized. The information gain may be learned from previous inspections, which can serve as training examples, and may be fixed after training, or may be adapted continuously during inspection.
There has been provided a system and method for automated inspection of a part which provides for a coupling of robot motion with current and/or past inspection results, thereby reducing uncertainty in detection and producing a more fully automated and reliable part inspection. While the system and method have been described in the context of specific embodiments thereof, other unforeseen alternatives, modifications, and variations may become apparent to those skilled in the art having read the foregoing description. Accordingly, it is intended to embrace those alternatives, modifications, and variations which fall within the broad scope of the appended claims.
Number | Name | Date | Kind |
---|---|---|---|
3804397 | Neumann | Apr 1974 | A |
4402053 | Kelley et al. | Aug 1983 | A |
4403294 | Hamada et al. | Sep 1983 | A |
4873651 | Raviv | Oct 1989 | A |
5064291 | Reiser | Nov 1991 | A |
5119678 | Bashyam et al. | Jun 1992 | A |
5345514 | Mandavieh et al. | Sep 1994 | A |
5345515 | Nishi et al. | Sep 1994 | A |
5351078 | Lemelson | Sep 1994 | A |
5963328 | Yoshida et al. | Oct 1999 | A |
6023637 | Liu et al. | Feb 2000 | A |
6153889 | Jones | Nov 2000 | A |
6177682 | Bartulovic et al. | Jan 2001 | B1 |
6271520 | Tao et al. | Aug 2001 | B1 |
6399948 | Thomas | Jun 2002 | B1 |
6434267 | Smith | Aug 2002 | B1 |
6462813 | Haven et al. | Oct 2002 | B1 |
6690016 | Watkins et al. | Feb 2004 | B1 |
6737648 | Fedder et al. | May 2004 | B2 |
6759659 | Thomas et al. | Jul 2004 | B2 |
6804622 | Bunker et al. | Oct 2004 | B2 |
6907358 | Suh et al. | Jun 2005 | B2 |
6965120 | Beyerer et al. | Oct 2005 | B1 |
7026811 | Roney, Jr. et al. | Apr 2006 | B2 |
7064330 | Raulerson et al. | Jun 2006 | B2 |
7119338 | Thompson et al. | Oct 2006 | B2 |
7122801 | Favro et al. | Oct 2006 | B2 |
7129492 | Saito et al. | Oct 2006 | B2 |
7164146 | Weir et al. | Jan 2007 | B2 |
7190162 | Tenley et al. | Mar 2007 | B2 |
7220966 | Saito et al. | May 2007 | B2 |
7233867 | Pisupati et al. | Jun 2007 | B2 |
7240556 | Georgeson et al. | Jul 2007 | B2 |
7272529 | Hogan et al. | Sep 2007 | B2 |
7313961 | Tenley et al. | Jan 2008 | B2 |
7415882 | Fetzer et al. | Aug 2008 | B2 |
7446886 | Aufmuth et al. | Nov 2008 | B2 |
7489811 | Brummel et al. | Feb 2009 | B2 |
7602963 | Nightingale et al. | Oct 2009 | B2 |
7689030 | Suh et al. | Mar 2010 | B2 |
7724925 | Shepard | May 2010 | B2 |
7738725 | Raskar et al. | Jun 2010 | B2 |
7823451 | Sarr | Nov 2010 | B2 |
7966883 | Lorraine et al. | Jun 2011 | B2 |
8050491 | Vaidyanathan | Nov 2011 | B2 |
8204294 | Alloo et al. | Jun 2012 | B2 |
8208711 | Venkatachalam et al. | Jun 2012 | B2 |
8221825 | Reitz et al. | Jul 2012 | B2 |
8239424 | Haigh et al. | Aug 2012 | B2 |
8431917 | Wang et al. | Apr 2013 | B2 |
8449176 | Shepard | May 2013 | B2 |
8520931 | Tateno | Aug 2013 | B2 |
8528317 | Gerez et al. | Sep 2013 | B2 |
8692887 | Ringermacher et al. | Apr 2014 | B2 |
8744166 | Scheid et al. | Jun 2014 | B2 |
8761490 | Scheid et al. | Jun 2014 | B2 |
8781209 | Scheid et al. | Jul 2014 | B2 |
8781210 | Scheid et al. | Jul 2014 | B2 |
8792705 | Scheid et al. | Jul 2014 | B2 |
8913825 | Taguchi et al. | Dec 2014 | B2 |
8983794 | Motzer et al. | Mar 2015 | B1 |
9037381 | Care | May 2015 | B2 |
9046497 | Kush | Jun 2015 | B2 |
9066028 | Koshti | Jun 2015 | B1 |
9080453 | Shepard et al. | Jul 2015 | B2 |
9116071 | Hatcher, Jr. et al. | Aug 2015 | B2 |
9134280 | Cataldo et al. | Sep 2015 | B2 |
9146205 | Renshaw et al. | Sep 2015 | B2 |
9151698 | Jahnke et al. | Oct 2015 | B2 |
9154743 | Hatcher, Jr. et al. | Oct 2015 | B2 |
9240049 | Ciurea et al. | Jan 2016 | B2 |
9251582 | Lim et al. | Feb 2016 | B2 |
9300865 | Wang et al. | Mar 2016 | B2 |
9305345 | Lim | Apr 2016 | B2 |
9458735 | Diwinsky et al. | Oct 2016 | B1 |
9465385 | Kamioka et al. | Oct 2016 | B2 |
9467628 | Geng et al. | Oct 2016 | B2 |
9471057 | Scheid et al. | Oct 2016 | B2 |
9476798 | Pandey et al. | Oct 2016 | B2 |
9476842 | Drescher et al. | Oct 2016 | B2 |
9483820 | Lim et al. | Nov 2016 | B2 |
9488592 | Maresca et al. | Nov 2016 | B1 |
9519844 | Thompson et al. | Dec 2016 | B1 |
9594059 | Brady et al. | Mar 2017 | B1 |
9734568 | Vajaria et al. | May 2017 | B2 |
9785919 | Diwinsky et al. | Oct 2017 | B2 |
9804997 | Sharp et al. | Oct 2017 | B2 |
9808933 | Lin et al. | Nov 2017 | B2 |
9981382 | Strauss | May 2018 | B1 |
10438036 | Reome et al. | Oct 2019 | B1 |
20020121602 | Thomas et al. | Sep 2002 | A1 |
20020167660 | Zaslavsky | Nov 2002 | A1 |
20030117395 | Yoon | Jun 2003 | A1 |
20030205671 | Thomas et al. | Nov 2003 | A1 |
20040089811 | Lewis et al. | May 2004 | A1 |
20040089812 | Favro et al. | May 2004 | A1 |
20040139805 | Antonelli et al. | Jul 2004 | A1 |
20040201672 | Varadarajan et al. | Oct 2004 | A1 |
20040240600 | Freyer et al. | Dec 2004 | A1 |
20040245469 | Favro et al. | Dec 2004 | A1 |
20040247170 | Furze et al. | Dec 2004 | A1 |
20050008215 | Shepard | Jan 2005 | A1 |
20050113060 | Lowery | May 2005 | A1 |
20050151083 | Favro et al. | Jul 2005 | A1 |
20050167596 | Rothenfusser et al. | Aug 2005 | A1 |
20050276907 | Harris et al. | Dec 2005 | A1 |
20060012790 | Furze et al. | Jan 2006 | A1 |
20060078193 | Brummel | Apr 2006 | A1 |
20060086912 | Weir et al. | Apr 2006 | A1 |
20070007733 | Hogarth et al. | Jan 2007 | A1 |
20070017297 | Georgeson et al. | Jan 2007 | A1 |
20070045544 | Favro et al. | Mar 2007 | A1 |
20080022775 | Sathish et al. | Jan 2008 | A1 |
20080053234 | Staroselsky et al. | Mar 2008 | A1 |
20080111074 | Weir et al. | May 2008 | A1 |
20080183402 | Malkin et al. | Jul 2008 | A1 |
20080229834 | Bossi et al. | Sep 2008 | A1 |
20080247635 | Davis et al. | Oct 2008 | A1 |
20080247636 | Davis et al. | Oct 2008 | A1 |
20090000382 | Sathish et al. | Jan 2009 | A1 |
20090010507 | Geng | Jan 2009 | A1 |
20090066939 | Venkatachalam et al. | Mar 2009 | A1 |
20090128643 | Kondo et al. | May 2009 | A1 |
20090252987 | Greene, Jr. | Oct 2009 | A1 |
20090279772 | Sun et al. | Nov 2009 | A1 |
20090312956 | Zombo et al. | Dec 2009 | A1 |
20100212430 | Murai et al. | Aug 2010 | A1 |
20100220910 | Kaucic et al. | Sep 2010 | A1 |
20110062339 | Ruhge et al. | Mar 2011 | A1 |
20110083705 | Stone et al. | Apr 2011 | A1 |
20110119020 | Key | May 2011 | A1 |
20110123093 | Alloo et al. | May 2011 | A1 |
20110299752 | Sun | Dec 2011 | A1 |
20110302694 | Wang et al. | Dec 2011 | A1 |
20120154599 | Huang | Jun 2012 | A1 |
20120188380 | Drescher et al. | Jul 2012 | A1 |
20120249959 | You et al. | Oct 2012 | A1 |
20120275667 | Lu | Nov 2012 | A1 |
20120293647 | Singh et al. | Nov 2012 | A1 |
20130028478 | St-Pierre et al. | Jan 2013 | A1 |
20130041614 | Shepard et al. | Feb 2013 | A1 |
20130070897 | Jacotin | Mar 2013 | A1 |
20130113914 | Scheid et al. | May 2013 | A1 |
20130113916 | Scheid et al. | May 2013 | A1 |
20130163849 | Jahnke et al. | Jun 2013 | A1 |
20130235897 | Bouteyre et al. | Sep 2013 | A1 |
20130250067 | Laxhuber | Sep 2013 | A1 |
20140022357 | Yu et al. | Jan 2014 | A1 |
20140056507 | Doyle et al. | Feb 2014 | A1 |
20140098836 | Bird | Apr 2014 | A1 |
20140184786 | Georgeson et al. | Jul 2014 | A1 |
20140185912 | Lim et al. | Jul 2014 | A1 |
20140198185 | Haugen et al. | Jul 2014 | A1 |
20140200832 | Troy et al. | Jul 2014 | A1 |
20140350338 | Tanaka et al. | Nov 2014 | A1 |
20150041654 | Barychev et al. | Feb 2015 | A1 |
20150046098 | Jack et al. | Feb 2015 | A1 |
20150086083 | Chaudhry et al. | Mar 2015 | A1 |
20150128709 | Stewart et al. | May 2015 | A1 |
20150138342 | Brdar et al. | May 2015 | A1 |
20150185128 | Chang et al. | Jul 2015 | A1 |
20150233714 | Kim | Aug 2015 | A1 |
20150253266 | Lucon et al. | Sep 2015 | A1 |
20150314901 | Murray et al. | Nov 2015 | A1 |
20160012588 | Taguchi et al. | Jan 2016 | A1 |
20160043008 | Murray et al. | Feb 2016 | A1 |
20160109283 | Broussais-Colella et al. | Apr 2016 | A1 |
20160178532 | Lim et al. | Jun 2016 | A1 |
20160241793 | Ravirala et al. | Aug 2016 | A1 |
20160284098 | Okumura et al. | Sep 2016 | A1 |
20160314571 | Finn et al. | Oct 2016 | A1 |
20160328835 | Maresca, Jr. et al. | Nov 2016 | A1 |
20160334284 | Kaplun Mucharrafille et al. | Nov 2016 | A1 |
20170011503 | Newman | Jan 2017 | A1 |
20170023505 | Maione et al. | Jan 2017 | A1 |
20170052152 | Tat et al. | Feb 2017 | A1 |
20170085760 | Ernst et al. | Mar 2017 | A1 |
20170090458 | Lim | Mar 2017 | A1 |
20170122123 | Kell et al. | May 2017 | A1 |
20170142302 | Shaw et al. | May 2017 | A1 |
20170184469 | Chang et al. | Jun 2017 | A1 |
20170184549 | Reed et al. | Jun 2017 | A1 |
20170184650 | Chang et al. | Jun 2017 | A1 |
20170211408 | Ahmadian et al. | Jul 2017 | A1 |
20170219815 | Letter et al. | Aug 2017 | A1 |
20170221274 | Chen et al. | Aug 2017 | A1 |
20170234837 | Hall et al. | Aug 2017 | A1 |
20170241286 | Roberts et al. | Aug 2017 | A1 |
20170258391 | Finn et al. | Sep 2017 | A1 |
20170262965 | Xiong et al. | Sep 2017 | A1 |
20170262977 | Finn et al. | Sep 2017 | A1 |
20170262979 | Xiong et al. | Sep 2017 | A1 |
20170262985 | Finn et al. | Sep 2017 | A1 |
20170262986 | Xiong et al. | Sep 2017 | A1 |
20170270651 | Bailey et al. | Sep 2017 | A1 |
20170297095 | Zalameda et al. | Oct 2017 | A1 |
20170284971 | Hall | Nov 2017 | A1 |
20180002039 | Finn et al. | Jan 2018 | A1 |
20180005362 | Wang et al. | Jan 2018 | A1 |
20180013959 | Slavens et al. | Jan 2018 | A1 |
20180019097 | Harada et al. | Jan 2018 | A1 |
20180098000 | Park et al. | Apr 2018 | A1 |
20180111239 | Zak et al. | Apr 2018 | A1 |
20190299542 | Webb | Oct 2019 | A1 |
20190338666 | Finn et al. | Nov 2019 | A1 |
20190339131 | Finn et al. | Nov 2019 | A1 |
20190339165 | Finn et al. | Nov 2019 | A1 |
20190339206 | Xiong et al. | Nov 2019 | A1 |
20190339207 | Finn et al. | Nov 2019 | A1 |
20190339234 | Finn et al. | Nov 2019 | A1 |
20190339235 | Finn et al. | Nov 2019 | A1 |
20190340742 | Finn et al. | Nov 2019 | A1 |
20190340805 | Xiong et al. | Nov 2019 | A1 |
20190342499 | Xiong et al. | Nov 2019 | A1 |
Number | Date | Country |
---|---|---|
2820732 | Dec 2014 | CA |
19710743 | Sep 1998 | DE |
1961919 | Aug 2008 | EP |
2545271 | Jun 2017 | GB |
06235700 | Aug 1994 | JP |
06235700 | Aug 1994 | JP |
2015161247 | Sep 2015 | JP |
191452 | Jul 2013 | SG |
2013088709 | Jun 2013 | WO |
2016112018 | Jul 2016 | WO |
2016123508 | Aug 2016 | WO |
2016176524 | Nov 2016 | WO |
Entry |
---|
U.S. Final Office Action dated Mar. 12, 2020 for corresponding U.S. Appl. No. 15/971,194. |
E. J. Candes, X. Li, Y. Ma, and J. Wright, “Robust Principal Component Analysis”, submitted. http://www-stat.stanford.edu/˜candes/papers/RobustPCA.pdf. |
M. Sznaier, O. Camps, N. Ozay, T. Ding, G. Tadmor and D. Brooks, “The Role of Dynamics in Extracting Information Sparsely Encoded in High Dimensional Data Streams”, in Dynamics of Information Systems, Hirsch, M.J.; Pardalos, P.M.; Murphey, R. (Eds.), pp. 1-28, Springer Verlag, 2010. |
M. Fazel, H. Hindi, and S. Boyd, “A Rank Minimization Heuristic with Application to Minimum Order System Approximation”, American Control Conference, Arlington, Virginia, pp. 4734-4739, Jun. 2001. |
Meola et al., ‘An Excursus on Infrared Thermography Imaging’, J. Imaging 2016, 2, 36 http://www.mdpi.com/2313-433X/2/4/36/pdf. |
Yu et al., ‘ASIFT: An Algorithm for Fully Affine Invariant Comparison’, Image Processing on Line on Feb. 24, 2011. http://www.ipol.im/pub/art/2011/my-asift/article.pdf. |
Schemmel et al., ‘Measurement of Direct Strain Optic Coefficient of YSZ Thermal Barrier Coatings at Ghz Frequencies’, Optics Express, v.25, n. 17, Aug. 21, 2017, https://doi.org/10.1364/OE.25.019968. |
Jean-Yves Bouguet, “Camera Calibration Toolbox for Matlab”, http://www.vision.caltech.edu/bouguetj/calib_doc/, accessed on Nov. 10, 2017. |
https://www.qualitymag.com/articles/91976-from-human-to-machine-how-to-be-prepared-for-integration-of-automated-visual-inspection. |
http://www.yxlon.com/products/x-ray-and-ct-inspection-systems/yxlon-mu56-tb. |
Yu et al. ‘Shadow Graphs and 3D Texture Reconstruction’, IJCV, vol. 62, No. 1-2, 2005, pp. 35-60. |
U.S. Non-Final Office Action dated Nov. 26, 2019 for corresponding U.S. Appl. No. 15/971,194. |
U.S. Non-Final Office Action dated Nov. 29, 2019 for corresponding U.S. Appl. No. 15/971,242. |
Gao et al., ‘A Statistical Method for Crack Detection from Vibrothermography Inspection Data’,(2010) Statistics Preprints. Paper 68. http://lib.dr.iastate.edu/stat_las_preprints/68. |
Li1 Ming; Holland1 Stephen D.; and Meeker1 William Q.1 “Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data” (2010). Statistics Preprints. 69. |
Henneke et al. ‘Detection of Damage in Composite Materials by Vibrothermography’, ASTM special technical publication (696), American Society for Testing and Materials, 1979, pp. 83-95. |
http://www.npl.co.uk/commercial-services/sector-case-studies/thermal-imaging-reveals-the-invisible; Apr. 17, 2012. |
Tian et al., ‘A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images’, Technometrics, 59, 247-261. Feb. 1, 2017. |
Emmanuel J. Cand'es1,2, Xiaodong Li2, Yi Ma3,4, and John Wright4, “Robust Principal Component Analysis”, (1)Department of Statistics, Stanford University, Stanford, CA; (2)Department of Mathematics, Stanford University, Stanford, CA; (3, 4) Electrical and Computer Engineering, UIUC, Urbana, IL (4) Microsoft Research Asia, Beijing, China, Dec. 17, 2009. |
Sebastien Parent; “From Human to Machine: How to Be Prepared for Integration of Automated Visual Inspection” Quality Magazine, https://www.qualitymag.com/articles/91976. Jul. 2, 2014. |
http://www.yxlon.com/products/x-ray-and-ct-inspection-systems/yxlon-mu56-tb, 2016. |
U.S. Office action dated Jul. 23, 2018 issued in corresponding U.S. Appl. No. 15/971,254. |
U.S. Non-Final Office Action dated Mar. 5, 2019 for corresponding U.S. Appl. No. 15/971,227. |
U.S. Non-Final Office Action dated May 28, 2019 for corresponding U.S. Appl. No. 15/971,214. |
U.S. Non-Final Office Action dated Apr. 16, 2019 for corresponding U.S. Appl. No. 15/970,985. |
Blachnio et al, “Assessment of Technical Condition Demonstrated by Gas Turbine Blades by Processing of Images of Their Surfaces”, Journal of KONBiN, 1(21), 2012, pp. 41-50. |
Raskar et al., ‘A Non-photorealistic Camera: Depth Edge Detection and Stylized Rendering using Multi-flash Imaging’ ACM Transactions on Graphics, 2004 http://www.merl.com/publications/docs/TR2006-107.pdf. |
Feris et al., ‘Specular Reflection Reduction with Multi-Flash Imaging’, 17th Brazilian Symposium on Computer Graphics and Image Processing, 2004. http://rogerioferis.com/publications/FerisSIB04.pdf. |
Holland, “First Measurements from a New Broadband Vibrothermography Measurement System”, AIP Conference Proceedings, 894 (2007), pp. 478-483. http://link.aip.org/link/doi/10.1063/1.2718010 \. |
Gao et al., ‘Detecting Cracks in Aircraft Engine Fan Blades Using Vibrothermography Nondestructive Evaluation’, RESS Special Issue on Accelerated Testing, 2014, http://dx.doi.org/10.1016/j.ress.2014.05.009. |
Gao et al., ‘A Statistical Method for Crack Detection from Vibrothermography Inspection Data’, Statistics Preprints. Paper 68. http://lib.dr.iastate.edu/stat_las_preprints/68. |
Holland, ‘Thermographic Signal Reconstruction for Vibrothermography’, Infrared Physics & Technology 54 (2011) 503-511. |
Li et al., ‘Statistical Methods for Automatic Crack Detection Based on Vibrothermography Sequence-of-Images Data’, Statistics Preprints. Paper 69. http://lib.dr.iastate.edu/stat_las_preprints/69. |
Tian et al., ‘A Statistical Framework for Improved Automatic Flaw Detection in Nondestructive Evaluation Images’, Technometrics, 59, 247-261. |
Henneke et al. ‘Detection of Damage in Composite Materials by Vibrothermography’, ASTM special technical publication (696), 1979, pp. 83-95. |
http://www.npl.co.uk/commercial-services/sector-case-studies/thermal-imaging-reveals-the-invisible. |
U.S. Non-Final Office Action dated Feb. 25, 2020 for corresponding U.S. Appl. No. 15/971,214. |
U.S. Non-Final Office Action dated Apr. 30, 2020 issued for corresponding U.S. Appl. No. 15/970,944. |
U.S. Final Office Action dated Jul. 28, 2020 issued for corresponding U.S. Appl. No. 15/971,214. |
U.S. Non-Final Office Action dated May 21, 2020 issued for corresponding U.S. Appl. No. 15/971,236. |
U.S. Non-Final Office Action dated Aug. 28, 2020 issued for corresponding U.S. Appl. No. 15/971,194. |
U.S. Non-Final Office Action dated Jun. 23, 2020 issued for corresponding U.S. Appl. No. 15/971,205. |
U.S. Notice of Allowance dated Oct. 19, 2020 issued for corresponding U.S. Appl. No. 15/971,270. |
U.S. Final Office Action dated Jan. 3, 2019 for corresponding U.S. Appl. No. 15/971,254. |
Number | Date | Country | |
---|---|---|---|
20190340721 A1 | Nov 2019 | US |