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 .
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(s) 1, 7- 9, 11, 16-18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over D'SOUZA; Prashanth et al. (US 20170097323 A1, hereafter referred to as D'SOUZA) and Gustafson, James R. et al. et al. (US 20130013138, hereafter referred to as Gustafson). Note: D'SOUZA teaches the use of an acoustic stimulus, which is highly effective under static conditions because there is no background electrical or mechanical noise to interfere with the “test chirp”.
Rejection of claims 1, 11 and 20:
D'SOUZA teaches stimulating the Stator with a Test Stimulus (Paragraph [0018] on page 2; and Figure 1 of D'SOUZA teaches an acoustic omission sensor 112 stimulating the stator with an acoustic signal. Paragraphs [0007] & [0026] on respective page 1-3 and Figures 3 and 7 of D'SOUZA teaches that in Step 704, the acoustic omission sensor 112 is configured to measure the acoustic signal to generate a transformed acoustic signal by measuring a first real and imaginary value representative of a first amplitude parameter indicative of a response of the stator to the acoustic signal stimulus. Steps 704 and 706 in Figure 7 of D'SOUZA teaches that the first real and imaginary values representative of a first amplitude parameter are used to generate a transformed acoustic signal and that the transformed acoustic signal is used to generate an acoustic test signature; hence, the acoustic test signature is also generated based on the first real and imaginary values representative of a first amplitude parameter.
Paragraph [0037] on pages 4-5 in D'SOUZA teaches that the acoustic test signature is compared to a threshold (note: the threshold substantially a reference signature indicative of a cracked defect in one or more of the stator vanes). Paragraph [0037] on pages 4-5 in D'SOUZA teaches that the acoustic signatures signal is compared with a predetermined threshold and, if the acoustic signatures signal is greater than the predetermined threshold a peak reference value in the acoustic signature is detected. The peak reference value in the acoustic signature is indicative of a cracked defect in one or more stator vane.
In addition, while the patent mentions “monitoring”, the use of a low energy acoustic transmitter to find structural “echoes” (real/imaginary parts” is most effective when the machine is static. In a running wind turbine, the “imaginary part” of the signature would be heavily contaminated by mechanical vibrations of the gearbox and blades. Doing this under static conditions makes it a high precision structural integrity test.
Paragraph [0020] on page 2 of Gustafson, in an analogous art, teaches comparing a measured signature with a reference signature indicative of a normal healthy condition to provide an indication of a failure when the measured signature deviates from the reference signature beyond a predetermined threshold.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine D'SOUZA with the teachings of Gustafson by including use of a decision-making step of determining a failure when a reference signature value matches a test signature value. This modification would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, because one of ordinary skill in the art would have recognized that use of a decision-making step of determining a failure when a reference signature value matches a test signature value would have provided an effective alert when mechanical parts are no longer functional (Paragraph [0020] on page 2 of Gustafson). .
Rejection of claims 7 and 16:
Paragraph [0017] in Figure 2 in Gustafson.
Rejection of claims 8 and 17:
Paragraphs [0016]-[0017] in Figures 1-2 in Gustafson.
Rejection of claims 9 and 18:
Paragraph [0018] and Figure 1 in D'SOUZA.
Allowable Subject Matter
Claims 2-6, 10, 12-15 and 19 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
These patents established the "Golden Rule" of comparing a measured signal to a reference:* US 4,965,513 A (1990): The pioneer of Motor Current Signature Analysis (MCSA). It teaches measuring raw current to identify "signatures" in the frequency domain that reveal mechanical and electrical faults.* US 5,594,175 A (1997): Focuses on Acoustic Monitoring. It teaches using vibration and sound signatures to detect irregularities. It is foundational for the idea that a "noise" can be a "signature" for a failure.Static and Impedance-Based TestingThese are the most relevant foir Static Testing (Standstill/Off-line).* US 2005/0237212 A1: Teaches an electrical Static Test. It uses low-voltage AC/DC stimulation to measure impedance/inductance and compares phase-to-phase "signatures" to fail a stator based on imbalance.* US 2013/0013231 A1: Focuses on Stator Winding Testing. It utilizes specific test signals to generate a signature based on the magnetic flux or winding response, primarily for identifying insulation degradation.Acoustic and Structural SignaturesThese use Real/Imaginary parts to test the stator's physical integrity:* US 2017/0097323 A1: The Acoustic Transfer patent. It teaches stimulating the stator with sound, measuring the response (Real/Imaginary), and comparing that "Acoustic Signature" to a reference. As we discussed, this is highly effective as a Static Test.* US 2019/0056441 A1: An evolution in Acoustic Fault Detection. It refines the way sound signatures are filtered, specifically to identify faults in the presence of external environmental noise.High-Frequency and Transient AnalysisThese identify faults that "Standard" tests miss by looking at fast electrical changes.* US 2017/0102425 A1: Teaches High-Frequency Admittance. It injects high-frequency "probing signals" to find signatures of turn-to-turn shorts. The signature is the symmetry of the admittance matrix.* US 2019/0265299 A1: The Transient State patent. It captures the signature during the "startup" or "shutdown" of the machine. It uses the "Negative Sequence" currents (mismatch) to fail the stator.* US 2020/0400745 A1: Focuses on Normalization. It allows signature comparison to work even if the wind turbine speed changes, by mapping the signature to a "Reference Map" of different speeds.Predictive Modeling and Modern MLThe latest era, moving toward automated "learning" of the reference signature.* US 2013/0013138 A1: Teaches a Predictive Model. It generates a signature based on a mathematical model of the motor and compares real-time data to that model to predict failures before they happen.* US 2013/0338898 A1: Focuses on Fault Diagnosis through signal processing. It refines the "Comparison" step by using advanced algorithms to separate fault signatures from normal operational data.* US 2022/0123680 A1: A modern update on Parameter Estimation. It uses the "Model-Based" approach to constantly update the "Reference Signature" so the system doesn't trigger a false "Fail" as the machine ages.* US 11,639,966 B2 (2023): The current state-of-the-art for Automated ESA. It teaches an "Enhanced" signature analysis where the system automatically learns the "Pass" criteria for a specific stator, making the match/mismatch logic more precise.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH D TORRES whose telephone number is (571)272-3829. The examiner can normally be reached Monday-Friday 10-7 PT.
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/JOSEPH D TORRES/Primary Examiner, Art Unit 2112