DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Allowable Subject Matter
Claims 6-9 and 12-16 and 18-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 10, 15, and 17 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
U.S. Patent Application Publication No. 2016/0041070 (Wascat).
Claim 1:
The cited prior art describes a method for managing performance of an asset installed in an industrial facility, the method comprising: (Wascat: “This invention relates generally to the collection and analysis of machine diagnostic data, such as vibration data, temperature, and rotation speed. More particularly, this invention relates to methods and apparatus for automatically performing machine diagnosis on site during machine diagnostic data collection.” Paragraph 0002)
monitoring, operating parameters of a component from amongst one or more components of the asset, wherein a range of values is predefined for each of the operating parameters of the component, the range of values being indicative of normal operational behavior of the asset; (Wascat: see the data collection process 164 and the collected data 280 as illustrated in figure 9 and as described in paragraph 0099; “In an example embodiment the embedded stroboscope 40 senses rotation speed of a machine part within a range of 30-15000 revolutions per minute (rpm) and a flash duration of 0.5 to 15°. The embedded pyrometer 38 includes a laser sight, along with a pyrometer data interface for moving collected pyrometry data into storage 32 or to a communication interface (e.g., interfaces 44, 46, 48). The pyrometer 38 performs contactless temperature measurement at a location on the machine 12 upon which the laser sight impinges. In an example embodiment temperature is sensed with a one second time response and 95% emissivity within a range of 0° C. to 200° C. to an accuracy of +/−3° C. for ambient temperature, and with a resolution of 0.5° C. in a field of view of 5° at 50% (e.g., 4 cm target at 50 cm distance).” Paragraph 0039; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
identifying, one or more operating parameters of the component to deviate from corresponding predefined range of values, deviation in an operating parameter being a symptom of a fault; (Wascat: see the automatic diagnosis 158 and the symptoms 388 as illustrated in figure 11 and as described in paragraph 0046; “The automatic diagnosis modules 158 configure the data collection and analysis device 14 to perform vibration analysis, temperature analysis, shock pulse measuring, spectrum analysis of shock pulse results, fast Fourier transform of vibration data, fault detection, tachometry, and other machine diagnostic and predictive maintenance analysis for one or more test points of one or more machines.” Paragraph 0046; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
assigning, a severity index to each of one or more symptoms of the fault based on an amount of the deviation in the respective operating parameters from the corresponding predefined range of values; (Wascat: see the fault diagnosis 160 as illustrated in figure 11; see the determine probability Pi (i.e., severity) of potential fault 420 as illustrated in figure 12; “The fault diagnosis module 160 configures the data collection and analysis device 14 to process diagnostic data collected from one or more test points of a machine to automatically detect (on site) common types of faults, including mass unbalance, misalignment, a mounting defect, moving part looseness, structural resonance, a lubrication defect, rolling element bearings defects (e.g., pitting, general wear), gear defects (e.g., tooth wear, broken tooth, backlash), and cavitation.” Paragraph 0048; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
determining, a fault severity indicator for the fault associated with the component based on the severity indexes of the each of the one or more symptoms of the fault; and (Wascat: see the fault severity 394 as illustrated in figure 11 and corresponding confidence level for the faults as described in paragraphs 0009, 0010; “Automatic fault diagnosis is performed on machine diagnostic data, including vibration data, sensed from a rotating machine. When a fault is detected a severity and confidence level are provided for such fault. The severity is based on a Bayesian probability calculated for the fault. The confidence level is based on a similarity between characteristic symptoms to be diagnosed for the fault and characteristic symptoms to be diagnosed for other faults of the machine. The greater the dis-similarity between sets of characteristic symptoms the more confidence that the diagnosed fault is actually present in the machine. Among the benefits of the severity and confidence level indications is that they result in warnings and recommendations advising a less experienced technician on when to seek assistance from more a experienced technician having expertise in vibration analysis.” Paragraph 0006)
causing a corrective action when the fault severity indicator is above a predetermined threshold. (Wascat: “Recommendations are generated automatically and displayed to the technician based on the patterns and confidence levels of the fault(s).” paragraph 0111; “The confidence level for each potential fault is used to determine a confidence indicator for the potential fault. For a potential fault where the confidence level is less than 0.5, the presence of the fault is doubtful, so the potential fault is not diagnosed as being present—regardless of the P(i) value for such potential fault. Accordingly, a potential fault is not diagnosed when either one of the following conditions are met: its Bayesian probability is less than a prescribed value (e.g., 0.51), or its confidence level is less than a prescribed level (e.g., 0.5). When the confidence level for fault i is 0.875 (and the probability is ≧0.51), the confidence indicator indicates certainty of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.75≦Ci<0.875, (and the probability is ≧0.51), the confidence indicator indicates a high probability of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.625≦Ci<0.75, (and the probability is ≧0.51), the confidence indicator indicates a good probability of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.5≦Pi<0.625, (and the probability is ≧0.51), the confidence indicator indicates that the presence/diagnosis of the fault i is suspect. In various embodiments the number of intervals into which the derived confidence level is divided for reporting purposed and the size and boundaries of such intervals vary. For diagnosed faults detected as suspect due to the relatively low confidence level, additional warnings are presented to the technician in some embodiments suggesting the technician request a complementary analysis by a technician experienced in vibration analysis to confirm the fault defect before taking any maintenance action.” Paragraph 0149)
Claim 3:
The cited prior art describes the method as claimed in claim 1,
wherein determining the fault severity indicator for the fault associated with the component further comprises: (Wascat: see the fault severity 394 as illustrated in figure 11)
calculating a weight of the fault based on a predefined value indicative of an impact of the fault on the corresponding component of the asset, a count of the one or more symptoms of the fault and the severity indexes of the one or more symptoms of the fault; and (Wascat: see the weights 390 as illustrated in figure 11 and the assignment of weights as described in paragraphs 0009, 0139, 0140)
determining the fault severity indicator by integrating the severity indexes of each of the one or more symptoms of the fault and the weight of the fault using a first fusion function implementing data fusion operations. (Wascat: see the similarity among faults and the corresponding confidence 392 as illustrated in figure 11; “. Such similarity is weighted by the Bayesian probability (i.e., determined severity) of the second fault to determine the second fault's influence on the confidence level of the first fault. The similarity contributions regarding each other fault are similarly calculated. Because a high similarity is less indicative of confidence that the first fault is actually present, the value 1—the sum of the similarity contributions for each other fault (i.e., dis-similarity) is used to report the confidence level for a given fault.” Paragraph 0009)
Claim 10:
The cited prior art describes a system for managing performance of assets involved in an industrial process, the system comprising: (Wascat: “This invention relates generally to the collection and analysis of machine diagnostic data, such as vibration data, temperature, and rotation speed. More particularly, this invention relates to methods and apparatus for automatically performing machine diagnosis on site during machine diagnostic data collection.” Paragraph 0002)
a processor to: (Wascat: see the processor 20 as illustrated in figure 1)
for a component or a subsystem of an asset, determine a first severity index of a first symptom of a fault associated with the component or the subsystem based on an amount of deviation in value of a first operating parameter of the component or the subsystem from a corresponding predefined mean value indicative of acceptable values of the first operating parameter; (Wascat: see the fault diagnosis 160 as illustrated in figure 11; see the determine probability Pi (i.e., severity) of potential fault 420 as illustrated in figure 12; “The fault diagnosis module 160 configures the data collection and analysis device 14 to process diagnostic data collected from one or more test points of a machine to automatically detect (on site) common types of faults, including mass unbalance, misalignment, a mounting defect, moving part looseness, structural resonance, a lubrication defect, rolling element bearings defects (e.g., pitting, general wear), gear defects (e.g., tooth wear, broken tooth, backlash), and cavitation.” Paragraph 0048; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
determine a second severity index of a second symptom of the fault based on an amount of deviation in value of a second operating parameter of the component or the subsystem from a corresponding predefined mean value indicative of acceptable values of the second operating parameter; (Wascat: see the fault diagnosis 160 as illustrated in figure 11 and the first and second symptoms as described in paragraphs 0086, 0087, 0136; see the determine probability Pi (i.e., severity) of potential fault 420 as illustrated in figure 12; “The fault diagnosis module 160 configures the data collection and analysis device 14 to process diagnostic data collected from one or more test points of a machine to automatically detect (on site) common types of faults, including mass unbalance, misalignment, a mounting defect, moving part looseness, structural resonance, a lubrication defect, rolling element bearings defects (e.g., pitting, general wear), gear defects (e.g., tooth wear, broken tooth, backlash), and cavitation.” Paragraph 0048; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
calculate a fault severity indicator for the fault associated with the component or the subsystem by integrating the first severity index and the second severity index using data fusion operations; and and (Wascat: see the fault severity 394 as illustrated in figure 11 and corresponding confidence level for the faults as described in paragraphs 0009, 0010; “Automatic fault diagnosis is performed on machine diagnostic data, including vibration data, sensed from a rotating machine. When a fault is detected a severity and confidence level are provided for such fault. The severity is based on a Bayesian probability calculated for the fault. The confidence level is based on a similarity between characteristic symptoms to be diagnosed for the fault and characteristic symptoms to be diagnosed for other faults of the machine. The greater the dis-similarity between sets of characteristic symptoms the more confidence that the diagnosed fault is actually present in the machine. Among the benefits of the severity and confidence level indications is that they result in warnings and recommendations advising a less experienced technician on when to seek assistance from more a experienced technician having expertise in vibration analysis.” Paragraph 0006)
cause an alert notification to be generated based on the fault severity indicator of the fault. (Wascat: “Recommendations are generated automatically and displayed to the technician based on the patterns and confidence levels of the fault(s).” paragraph 0111; “The confidence level for each potential fault is used to determine a confidence indicator for the potential fault. For a potential fault where the confidence level is less than 0.5, the presence of the fault is doubtful, so the potential fault is not diagnosed as being present—regardless of the P(i) value for such potential fault. Accordingly, a potential fault is not diagnosed when either one of the following conditions are met: its Bayesian probability is less than a prescribed value (e.g., 0.51), or its confidence level is less than a prescribed level (e.g., 0.5). When the confidence level for fault i is 0.875 (and the probability is ≧0.51), the confidence indicator indicates certainty of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.75≦Ci<0.875, (and the probability is ≧0.51), the confidence indicator indicates a high probability of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.625≦Ci<0.75, (and the probability is ≧0.51), the confidence indicator indicates a good probability of the presence/diagnosis of the fault i. When the confidence level for fault i is 0.5≦Pi<0.625, (and the probability is ≧0.51), the confidence indicator indicates that the presence/diagnosis of the fault i is suspect. In various embodiments the number of intervals into which the derived confidence level is divided for reporting purposed and the size and boundaries of such intervals vary. For diagnosed faults detected as suspect due to the relatively low confidence level, additional warnings are presented to the technician in some embodiments suggesting the technician request a complementary analysis by a technician experienced in vibration analysis to confirm the fault defect before taking any maintenance action.” Paragraph 0149)
Claim 15:
The cited prior art describes the system as claimed in claim 10, wherein the processor is to: display, in a control room associated with the industrial process, the fault severity indicator in one of plurality of zones, wherein a zone in the plurality of zones corresponds to a predefined range of values of the fault severity indicator. (Wascat: “After data collection is complete for a given test point, the results 280 of the data collection are displayed (262 in FIG. 9), and the results 282 of the related automatic diagnosis are displayed (263 in FIG. 10), as applicable. Automatic diagnosis is performed automatically in the background (or displayed in the foreground) during the data collection process or immediately thereafter as the data becomes available. Such automatic diagnosis is started automatically by the data collection process 164, as per data collection setup parameters.” Paragraph 0105; “The Bayesian probability for each potential fault is used to determine a qualitative severity assessment for the potential fault. For a potential fault where the Bayesian probability is less than 0.51, the potential fault is not diagnosed as being present for the machine 12. Where the probability is 0.51≦Pi<0.65, a first severity level is indicated (e.g., a yellow flag) on a report or display. Where the probability is 0.65≦Pi<0.80, a second severity level is indicated (e.g., an orange flag) on a report or display. Where the probability is 0.80, highest severity level is indicated (e.g., a red flag) on a report or display. In various embodiments the number of intervals into which the probability is divided for reporting the severity assessment and the size and boundaries of such intervals vary.” Paragraph 0148)
Claim 17:
Claim 17 is substantially similar to claim 10 and is rejected based on the same reasons and rationale.
17. A non-transitory computer-readable medium comprising instructions executable by a processing resource to:
for a component of an asset:
detect a first fault based on a symptom of the first fault associated with the component, and a second fault based on a symptom of the second fault associated with the component, the symptom of a fault being a deviation in value of an operating parameter of the component from corresponding predefined mean value, the mean value being indicative of normal operational behavior of the component of the asset;
calculate a fault severity indicator for the first fault based on a severity index of the symptom of the first fault and for the second fault based on a severity index of the symptom of the second fault, the severity index of a symptom of a fault being based on an amount of deviation in value of the respective operating parameter from the corresponding predefined mean value;
determine a component level degradation indicator for the component, by integrating the fault severity indicators of the first fault and the second fault using data fusion operations; and
cause an alert notification to be generated based on the component level degradation indicator.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2016/0041070 (Wascat) in view of
U.S. Patent Application Publication No. 2019/0130294 (Volponi).
Claim 2:
Wascat does not explicitly describe normalization as described below. However, Volponi teaches the normalization as described below.
The cited prior art describes the method as claimed in claim 1, wherein assigning the severity index to each of one or more symptoms of the fault comprises conforming the severity index to a normalized range based on an amount of deviation in the respective operating parameters from a mean of the corresponding predefined range of values. (Volponi: see the normalization as described in paragraphs 0039, 0064; “The measure of closeness is a measurement error norm, which is a normalized weighted error term between the observed (measurement) AA parameter shifts and the expected AA parameter shifts assuming a specific single fault scenario.” Paragraph 0039) (Wascat: see the fault diagnosis 160 as illustrated in figure 11; see the determine probability Pi (i.e., severity) of potential fault 420 as illustrated in figure 12; “The fault diagnosis module 160 configures the data collection and analysis device 14 to process diagnostic data collected from one or more test points of a machine to automatically detect (on site) common types of faults, including mass unbalance, misalignment, a mounting defect, moving part looseness, structural resonance, a lubrication defect, rolling element bearings defects (e.g., pitting, general wear), gear defects (e.g., tooth wear, broken tooth, backlash), and cavitation.” Paragraph 0048; “The spectral analysis module 174 includes use of envelopes, zoom factors, different frequent ranges of analysis, weighting and synchronous analysis. The vector analysis module 176 performs vector measurements for different frequency ranges and includes synchronous averaging. Results of the modules 170-176 include indicators used during fault diagnosis to detect a fault.” Paragraph 0047)
One of ordinary skill in the art would have recognized that applying the known technique of Wascat, namely, automatic fault diagnosis for a machine, with the known techniques of Volponi, namely, system fault resolution, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Wascat to analyze data to determine faults with the teachings of Volponi to analyze data to determine faults would have been recognized by those of ordinary skill in the art as resulting in an improved distributed fault determination system. In other words, the combination of references provides for a fault determination system that utilizes various fault analysis techniques based on the teachings of analyzing data for fault detection in Wascat and the teachings of analyzing data using various data techniques for fault detection in Volponi.
Claims 4-5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2016/0041070 (Wascat) in view of
U.S. Patent No. 10,964,130 (Dixit).
Claim 4:
The cited prior art describes the method as claimed in claim 3 further comprising:
determining, a fault severity indicator for each fault from amongst a plurality of faults associated with the component; (Wascat: “The addition of other, less common faults in some instances may lower the reliability of the confidence level analysis of the automatic fault diagnosis module 160. Accordingly, configuring the automatic process of fault diagnosis for common faults is preferable.” Paragraph 0069; see the fault severity 394 as illustrated in figure 11 and corresponding confidence level for the faults as described in paragraphs 0009, 0010; “Automatic fault diagnosis is performed on machine diagnostic data, including vibration data, sensed from a rotating machine. When a fault is detected a severity and confidence level are provided for such fault. The severity is based on a Bayesian probability calculated for the fault. The confidence level is based on a similarity between characteristic symptoms to be diagnosed for the fault and characteristic symptoms to be diagnosed for other faults of the machine. The greater the dis-similarity between sets of characteristic symptoms the more confidence that the diagnosed fault is actually present in the machine. Among the benefits of the severity and confidence level indications is that they result in warnings and recommendations advising a less experienced technician on when to seek assistance from more a experienced technician having expertise in vibration analysis.” Paragraph 0006)
calculating a weight of a failure of the component based on a predefined value indicative of an impact of the failure of the component on the asset, a number of the faults and the fault severity indicators of each fault associated with the component; and (Wascat: see the weights 390 as illustrated in figure 11 and the assignment of weights as described in paragraphs 0009, 0139, 0140)
Wascat does not explicitly describe data fusion as described below. However, Volponi teaches the data fusion as described below.
determining, a component level degradation indicator for the component by integrating the fault severity indicators of each of the plurality of faults associated with the component and the weight of the failure of the component using a second fusion function implementing data fusion operations. (Dixit: see the data fusion 1170 from the data 1165, 1125, 1145 from the detectors 1160, 1120, 1140 as illustrated in figure 11; “A ground-based computing system receives data of performance parameters for like components disposed on like aircraft, and determines corresponding levels of degradation and rates of change of degradation for the respective like components.” abstract) (Wascat: see the similarity among faults and the corresponding confidence 392 as illustrated in figure 11; “. Such similarity is weighted by the Bayesian probability (i.e., determined severity) of the second fault to determine the second fault's influence on the confidence level of the first fault. The similarity contributions regarding each other fault are similarly calculated. Because a high similarity is less indicative of confidence that the first fault is actually present, the value 1—the sum of the similarity contributions for each other fault (i.e., dis-similarity) is used to report the confidence level for a given fault.” Paragraph 0009)
One of ordinary skill in the art would have recognized that applying the known technique of Wascat, namely, automatic fault diagnosis for a machine, with the known techniques of Dixit, namely, system fault resolution, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Wascat to analyze data to determine faults with the teachings of Dixit to analyze data to determine faults would have been recognized by those of ordinary skill in the art as resulting in an improved distributed fault determination system. In other words, the combination of references provides for a fault determination system that utilizes various fault analysis techniques based on the teachings of analyzing data for fault detection in Wascat and the teachings of analyzing data using various data techniques for fault detection in Dixit.
Claim 5:
Wascat does not explicitly describe subsystems as described below. However, Volponi teaches the subsystems as described below.
The cited prior art describes the method as claimed in claim 4 further comprising: determining a fault severity indicator for a fault associated with a subsystem from amongst one or more subsystems of the asset based on monitoring operating parameters of the subsystem, wherein the subsystem comprises a subset of components from amongst one or more components of the asset. (Dixit: “An example of output data from a model test is shown in FIG. 10 (PMD Viewer 122 FIG. 2). MBR Engine 106 (FIG. 2) has isolated a fault for the ECS Cooling component, using a fault in both the parameterized ECS Temperature sensor represented as ECS_Temperature node 238 and supporting evidence in other subsystem components including other temperature sensors (in some of these cases, for example, an LTA Laser Amplifier Driver Temperature (not shown), the only data available is a BIT test, hence a BIT test node is used for supporting evidence in this case). The logic of the interconnected subsystems' sub-models as similarly shown in FIGS. 2 and 10 dictates this result when the parameterized sensor ECS_Temperature node 238 measuring the ECS temperature is determined to be an anomaly with appropriate supporting evidence (from other sensor internal to subsystem or external sensors from other subsystem models). In addition, the BIT test BIT.ECS_TempFault node 242 measuring the ECS Temperature anomaly is separately indicating a fault; this sensor node is non-diagnostic and therefore not used to determine system faults, but it is used as a comparator for the non-diagnostic ECS_Temperature_ND parametric sensor node 224. Variations between the BIT and parametric nodes can indicate a faulty BIT test or sensor, and are one of the capabilities added by implementing parameterized sensors.” Col. 10, line 62 through col. 11, line 19; see the data fusion 1170 from the data 1165, 1125, 1145 from the detectors 1160, 1120, 1140 as illustrated in figure 11; “A ground-based computing system receives data of performance parameters for like components disposed on like aircraft, and determines corresponding levels of degradation and rates of change of degradation for the respective like components.” abstract)
Wascat and Dixit are combinable for the same rationale as set forth above with respect to claim 4.
Claim 11:
The cited prior art describes the system as claimed in claim 10, wherein the processor is to:
calculate, a fault severity indicator for each of one or more faults associated with the component or the subsystem based on the severity indexes of symptoms of the corresponding fault associated with the component or the subsystem; and (Wascat: “The addition of other, less common faults in some instances may lower the reliability of the confidence level analysis of the automatic fault diagnosis module 160. Accordingly, configuring the automatic process of fault diagnosis for common faults is preferable.” Paragraph 0069; see the fault severity 394 as illustrated in figure 11 and corresponding confidence level for the faults as described in paragraphs 0009, 0010; “Automatic fault diagnosis is performed on machine diagnostic data, including vibration data, sensed from a rotating machine. When a fault is detected a severity and confidence level are provided for such fault. The severity is based on a Bayesian probability calculated for the fault. The confidence level is based on a similarity between characteristic symptoms to be diagnosed for the fault and characteristic symptoms to be diagnosed for other faults of the machine. The greater the dis-similarity between sets of characteristic symptoms the more confidence that the diagnosed fault is actually present in the machine. Among the benefits of the severity and confidence level indications is that they result in warnings and recommendations advising a less experienced technician on when to seek assistance from more a experienced technician having expertise in vibration analysis.” Paragraph 0006)
Wascat does not explicitly describe data fusion as described below. However, Volponi teaches the data fusion as described below.
compute, a component level degradation indicator for the component, by integrating the fault severity indicators of each of the one or more faults associated with the component using data fusion operations. (Dixit: see the data fusion 1170 from the data 1165, 1125, 1145 from the detectors 1160, 1120, 1140 as illustrated in figure 11; “A ground-based computing system receives data of performance parameters for like components disposed on like aircraft, and determines corresponding levels of degradation and rates of change of degradation for the respective like components.” abstract) (Wascat: see the similarity among faults and the corresponding confidence 392 as illustrated in figure 11; “. Such similarity is weighted by the Bayesian probability (i.e., determined severity) of the second fault to determine the second fault's influence on the confidence level of the first fault. The similarity contributions regarding each other fault are similarly calculated. Because a high similarity is less indicative of confidence that the first fault is actually present, the value 1—the sum of the similarity contributions for each other fault (i.e., dis-similarity) is used to report the confidence level for a given fault.” Paragraph 0009)
Wascat and Dixit are combinable for the same rationale as set forth above with respect to claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent No. 10,565,046 describes fault detection using data distribution characteristics.
U.S. Patent No. 10,732,618 describes machine health monitoring and failure detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E EVERETT whose telephone number is (571)272-2851. The examiner can normally be reached Monday-Friday 8:00 am to 5:00 pm (Pacific).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Fennema can be reached at 571-272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117
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