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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN2023101930952, filed on 02/28/2023.
Status of Claims
Claims 1 – 7, 9 – 12 are pending and examined herein.
Claims 1 – 6, 11 – 12 are rejected under 35 U.S.C. 112(b).
Claims 1 – 7, 9 – 12 are rejected under 35 U.S.C. 101.
Claims 1 – 7, 9 – 12 are rejected under 35 U.S.C. 103.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it refers to purported merits and speculative applications of the invention and repeats information given in the title. The abstract should be revised to provide a concise technical summary of the disclosure. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 2, 5, 9, 12 are objected to because of the following informalities:
Claims 2, 9 recites
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which contains a singular/plural inconsistency that renders the claim language unclear. Either “a_k and b_k being parameters” or “a_k and b_k each being a parameter” would be more appropriate
Claims 5, 12 recites “Q being the number of the mechanical components I”. It is unclear relationship between Q and i as if i is being used as an index variable in the recited dataset expression or separate mechanical component i as currently written. Applicant should revise the claim to clearly identify them.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 6, 11 – 12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 4, 6, and 11 recites the limitation “the basis of first predicting time”. It is recited without antecedent introduction, rendering the scope. It is unclear whether the claims refer to a previously defined time, a newly introduced time, or a particular time in the degradation process. Applicant needs check limitations where “first predicting time” is used to resolve the indefiniteness. For examination purposes, all dependent claims after first introduction of “basis of first predicting time” would refer to it as “the basis of the first predicting time”.
Claims 2 – 3, 5, 12 are dependent on claims 1, 4, 11 respectively. They do not resolve the issue of indefiniteness and are rejected with the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 7, 9 – 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1 – 7, 9 – 12, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 – 5 are directed to a method, meaning that it is directed to the statutory category of process. Claim 6 is directed to a hybrid data and model driven system, which is the statutory category of machine. Claims 7, 9 – 12 are directed to an electronic device, which can be an article of machine.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
using an exponential random model to model a degradation process of the mechanical component and establish a system state space equation; (This is merely reciting mathematical equation, which is mathematical concept.)
estimating, on the basis of the system state space equation, parameters of the exponential random model by means of an extended Kalman filter, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Estimating parameter by the means of Kalman filter could recite mathematical calculation, which is mathematical concept.)
and using fast Fourier transform (FFT) to obtain frequency domain data corresponding to the state monitoring data in the degradation stage; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Using FFT to obtain frequency domain data merely recites mathematical calculation, which is mathematical concept.)
predict the remaining useful life of the mechanical component. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A hybrid data- and model-driven method for predicting remaining useful life of a mechanical component, comprising: (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
to obtain an optimal state estimate; (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
obtaining state monitoring data in a degradation stage of the mechanical component on the basis of first predicting time, (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
constructing a neural network training data set of all mechanical components according to the optimal state estimate and the frequency domain data; (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
building a hybrid driven prediction model comprising a fully connected layer, a one- dimensional convolutional long short-term memory network adaptive encoding layer, a multi-head attention mechanism module, a feedforward module and a fully connected regression layer; (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
using the neural network training data set to train and test the hybrid driven prediction model, (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
to obtain a trained hybrid driven prediction model; (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
and using the trained hybrid driven prediction model to (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
wherein the using an exponential random model to model a degradation process of the mechanical component and establish a system state space equation specifically comprises: using the exponential random model
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to model the degradation process of the mechanical component,
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being a parameter related to a health state of the mechanical component in the degradation process; and building the system state space equation
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on the basis of the exponential random model
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, a state vector at a moment k being
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f and h being nonlinear functions,
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being a state vector at a moment k-1,
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being a system input at the moment k-1,
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being a random zero mean error at the moment k-1,
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being a measured value at the moment k, and
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being a measurement error at the moment k. (These are merely reciting mathematical equations and relationships, which is mathematical concept.)
Claim 2 does not recite additional elements.
Regarding claim 3, the rejection of claim 2 is incorporated herein. Further, claim 3 recites the following abstract ideas:
locally linearizing, on the basis of the system state space equation, the nonlinear functions
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at the moment k about a state prior estimate
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, to obtain corresponding Jacobian metrices
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; building a prediction and update equation of an extended Kalman filter according to the Jacobian matrices
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; (These are merely reciting mathematical relationships, which is mathematical concept.)
Claim 3 further recites following additional element
and alternately executing, on the basis of the prediction and update equation, a prediction and update process of the extended Kalman filter to continuously update a predicted state vector, (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
so as to obtain the optimal state estimate. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 4, the rejection of claim 3 is incorporated herein. Further, claim 4 recites the following abstract ideas:
determining the first predicting time on the basis of original state monitoring data of the mechanical component collected by a sensor; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
and using the FFT to extract frequency domain information of the state monitoring data in the degradation stage, (Using the FFT to extract frequency domain information of the data is merely reciting mathematical calculation, which is mathematical concept.)
Claim 4 further recites following additional element
extracting the state monitoring data in the degradation stage of the mechanical component on the basis of the first predicting time; (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
to obtain the frequency domain data in the degradation stage. (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 5, the rejection of claim 4 is incorporated herein. Further, claim 5 recites the following abstract ideas:
constructing the neural network training data set
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of all the mechanical components according to the optimal state estimate
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and the frequency domain data
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in the degradation stage of the mechanical component, a moment
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being the first predicting time,
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being a length of a variance feature sequence, Q being the number of the mechanical components i, and
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being the remaining useful life of the mechanical component i at the moment k. (These are merely reciting mathematical equations and relationships, which is mathematical concept.)
Claim 5 does not recite additional elements.
Claim 6 recites substantially similar subject matter to claim 1 respectively and is rejected with the same rationale, mutatis mutandis.
Claims 7, 9 – 12 recite substantially similar subject matter to claim 1, 2 – 5 respectively and are rejected with the same rationale, mutatis mutandis.
Claim Rejections - 35 USC § 103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 7, 9 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Singleton et al. (NPL: “Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings”) in view of Ma et al. (U.S. Pub. 2025/0021089 A1), Wang et al. (foreign pub. CN109726524 B), further in view of Gang Wang et al. (NPL: “Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method”).
Regarding Claim 1, Singleton teaches
A hybrid data- and model-driven method for predicting remaining useful life of a mechanical component, comprising: using an exponential random model to model a degradation process of the mechanical component and establish a system state space equation; (Pg. 1785 C.EKF Parameter Learning section of Singleton states “For the two different types of features, i.e., vibration and entropy, time-dependent degradation models are obtained through curve fitting. For the variance feature, an exponential of the form aebt was found to be the most suitable, whereas for the TF entropy feature, a curve in the form of a − be−ct was more suitable” Pg. 1783 D. Extended Kalman Filtering section of Singleton states “The EKF equation in the presence of process noise and measurement noise is xk = f(xk−1, uk−1) +wk−1 (3) where xk is the state being estimated, f is a nonlinear function of states, uk is the input at time sample k, wk is random zero mean noise with covariance matrix Qk. In EKF, the relationship between system states (xk) and measurements (zk) can also be nonlinear, i.e.,zk = h(xk)+vk (4) where zk is the measurement, h is a measurement function that is a nonlinear function of states, and vk is a zero-mean random process”)
estimating, on the basis of the system state space equation, parameters of the exponential random model by means of an extended Kalman filter, to obtain an optimal state estimate; (Pg. 1785 C.EKF Parameter Learning section of Singleton states “The parameters of the degradation function are updated with each new measurement. To accomplish this, a state vector x containing the equation for the curve fit as well as the unknown parameters describing this degradation model at each time point are defined for each feature. For the variance feature, the parameters ak and bk of the exponential curve are used to define the state vector as [43], [53] xk = [akebkk ak bk]T (5)… With each time step, the parameters of the degradation model are updated to form a new model, i.e., fk, and an estimate of the next state, i.e., ˆxk, is calculated…. An overall view of the algorithm and all its steps are given below: 1) Initialize x0 and P0. 2) Predict the next state, ˆxk, and uncertainty matrix, Mk, i.e., ˆxk =f(ˆxk−1) + wk−1 (14) Mk =Fk−1Pk−1Fk−1 + Qk−1. (15) 3) Take in measurement zk. 4) Update the predictions and their uncertainties using the Kalman gain, Kk, ”)
according to the optimal state estimate and (Pg. 1785 C.EKF Parameter Learning section of Singleton states “With each time step, the parameters of the degradation model are updated to form a new model, i.e., fk, and an estimate of the next state, i.e., ˆxk, is calculated….”)
However, Singleton does not explicitly teach that
obtaining state monitoring data in a degradation stage of the mechanical component on the basis of first predicting time, and using fast Fourier transform (FFT) to obtain frequency domain data corresponding to the state monitoring data in the degradation stage;
constructing a neural network training data set of all mechanical components according … the frequency domain data;
building a hybrid driven prediction model comprising a fully connected layer, a one-dimensional convolutional long short-term memory network adaptive encoding layer, a multi-head attention mechanism module, a feedforward module and a fully connected regression layer;
using the neural network training data set to train and test the hybrid driven prediction model, to obtain a trained hybrid driven prediction model;
and using the trained hybrid driven prediction model to predict the remaining useful life of the mechanical component
Wang teaches that
obtaining state monitoring data in a degradation stage of the mechanical component…, and using fast Fourier transform (FFT) to obtain frequency domain data corresponding to the state monitoring data in the degradation stage; (Wang states “The method firstly performs FFT conversion to the original vibration signals the rolling bearing, then the frequency domain amplitude signal obtained by the pre-processing is normalized, and it is used as the input of CNN. using CNN with convolution operation, weight sharing and so characteristics, automatically extracting data local abstract information to excavate the deep features, avoiding the problem that the traditional feature extraction method is too dependent on expert experience.” Wang refers to the monitored data as the original vibration signal of the rolling bearing and a POSITA would be able to refer this to be monitored bearing condition data collected during bearing operation.)
constructing a neural network training data set of all mechanical components according … the frequency domain data; (Wang states “1) selecting the part data of each working condition of the rolling bearing vibration signals the training set, and performing FFT conversion to the original vibration signals of the training set to obtain the frequency domain amplitude value signal. 2) the frequency domain amplitude signal after performing normalization processing as characteristic input, life percentage as output training model, formula represents the N-dimensional characteristic at the time t, N=2048, yt belongs to [0, 1] represents the life degradation percentage of the bearing at time t, Dtra represents a certain working condition in the training vibration signals of a bearing of the data, R is frequency domain amplitude characteristic matrix; T is the running time of the bearing full service life. 3) setting number of CNN layers, filter number, convolution size and other parameters, the normalized frequency domain amplitude signal as input of CNN, using the convolution layer in CNN, the formula (1) to (3) of the pool layer traversal the whole input data sequence, extracting the local information of the vibration signals, mining deep features. 4) then inputting the deep characteristic into the LSTM network, using the advantages of the formula (4) to (9) and LSTM memory unit for long term memory to the time sequence data, constructing trend quantitative health index through LSTM network, establishing trend quantitative health index model. 5) performing FFT conversion to the non-full-life time domain vibration signals of different working conditions in the test set, obtaining the frequency domain amplitude signal, and performing normalization processing, step 3) digging the deep characteristic, combining the trend quantitative health index model of step 4), obtaining the trend quantitative health index of the test set. using the moving average method (average, MA) to smooth [17], reducing oscillation to improve the remaining life prediction precision. and 6) fitting the performance degradation trend the rolling bearing by the polynomial curve [18] to predict the RUL of the rolling bearing.” While Singleton teach the model based state estimate degradation parameter input, Wang teaches forming a training set using FFT to obtain frequency domain signal feature for neural network training.)
a one-dimensional convolutional long short-term memory network adaptive encoding layer, (Wang states “FIG. 1 is a one-dimensional CNN diagram, FIG. 2 is a LSTM memory unit structure diagram.. The invention claims a method for predicting rolling bearing RUL based on convolutional neural network (CNN) and long short-time memory neural network (LSTM) constructing trend quantitative health index, the fast Fourier transform (fast fourier transform, FFT) obtained frequency domain amplitude signal after normalization processing as input of CNN, digging deep features, avoiding the problem that traditional feature extraction method is too dependent on expert experience. then, using LSTM network with good processing time sequence advantage, constructing the trend quantization health index, so as to further predict RUL of rolling bearing, suitable for prediction of performance degradation gradual fault and burst fault two modes.” Wang teaches a CNN-LSTM encoding architecture in which convolutional processing extracts local sequential features form the frequency-domain signal and LSTM processes the resulting sequence information for RUL prediction.)
Gang Wang teaches that
on the basis of first predicting time (Pg. 1 Abstract of Gang Wang states “Remaining useful life (RUL) using exponential model (EM) prediction has been a hot research topic in the construction of prognostics health management (PHM) systems. However, in RUL prediction of rolling bearings, the EM 1) depends on the appropriate first prediction time (FPT), 2) requires reliable methods to optimize the model. Therefore, an improved EM is developed to predict the RUL of rolling bearings. Firstly, an adaptive method based on kurtosis and root mean square (RMS) of bearing vibration signals is used to determine the appropriate FPT. Secondly, gradient descent method is used to reliably optimize the EM. A commonly used bearing degradation datasets are analyzed to show the advantages of the present method. Compared with the traditional EM, the method can not only adaptively determine FPT, but also predict RUL more accurately.” Gang Wang teaches that rolling bearing RUL prediction using an exponential model depends on an appropriate first prediction time. )
Ma teaches that
building a hybrid driven prediction model comprising a fully connected layer, … , a multi-head attention mechanism module, a feedforward module and a fully connected regression layer; ()
using the neural network training data set to train and test the hybrid driven prediction model, to obtain a trained hybrid driven prediction model; ([0004] of Ma states “In this method, samples are constructed by sliding time windows on the historical sensor data of aero-engines, and then features are extracted by convolutional neural network. Finally, the remaining useful life is predicted through the full connection layer. Convolutional neural network is a kind of feedforward neural network through convolution calculation, which is inspired by the mechanism of receptive field in biology. It has translation invariance, uses convolution kernel, makes maximum use of local information, and retains plane structure information” [0016] of Ma states ”The network structure of the multi-scale hybrid attention mechanism model (the network structure diagram is shown in FIG. 3 a ) comprises a position encoding layer, a feature extraction layer and a regression prediction layer;” [0019] of Ma states “The feature extraction layer comprises two parts: a multi-head hybrid attention mechanism and a multi-scale convolutional neural network, and residual connection and layer normalization are added simultaneously at the end positions of the two parts to suppress overfitting; the multi-head hybrid attention mechanism is formed by mixing a multi-head self attention mechanism and a multi-head external attention mechanism.” [0030] of Ma states “(3.3) Regression Prediction Layer Firstly, expanding the result MultiScaleConv∈Rn*d obtained in step 3.2 as F∈R1*(n*d), and then, calculating the result through a two-layer fully connected neural network to obtain the predicted value RUL of the remaining useful life of an aero-engine:” [0016] of Ma states “The network structure of the multi-scale hybrid attention mechanism model (the network structure diagram is shown in FIG. 3 a ) comprises a position encoding layer, a feature extraction layer and a regression prediction layer;”)
and using the trained hybrid driven prediction model to predict the remaining useful life of the mechanical component ([0033] of Ma states “At the on-line testing stage, calculating the output value by preprocessing the data in step 1 and inputting the data into the multi-scale hybrid attention mechanism model trained in step 4 according to the real-time data collected by the aero-engine sensor, wherein the output value is the predicted value of the remaining useful life of the aero-engine.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Singleton, Ma, Gang Wang, and Wang because all references are directed to RUL prediction for mechanical assets. Singleton teaches an EKF based degradation modeling and state estimation framework for RUL prediction with equations. Wang teaches obtaining frequency domain information from monitored bearing signals using FFT and using those signals as input for neural network-based RUL prediction. Gang Wang teaches determining first prediction time as a key for implementing RUL prediction model. Ma teaches constructing training samples from historical sensor data and training RUL prediction model including attention based and fully connected prediction components. One with ordinary skill in the art would be motivated to incorporate the teachings of Singleton, Ma, Gang Wang, and Wang because they solve different but complementary parts of the same RUL prediction pipeline. Combining them would have predictably improved the overall pipeline by using degradation stage data that are more relevant, extracting signal features that better reflect degradation, and training hybrid models on those more useful inputs to produce accurate RUL predictions.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Singleton, Wang, Gang Wang, and Ma teaches
wherein the using an exponential random model to model a degradation process of the mechanical component and establish a system state space equation specifically comprises: using the exponential random model
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to model the degradation process of the mechanical component,
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being a parameter related to a health state of the mechanical component in the degradation process; and building the system state space equation
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on the basis of the exponential random model
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, a state vector at a moment k being
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f and h being nonlinear functions,
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being a state vector at a moment k-1,
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being a system input at the moment k-1,
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being a random zero mean error at the moment k-1,
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being a measured value at the moment k, and
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being a measurement error at the moment k. (Pg. 1785 C.EKF Parameter Learning section of Singleton states “For the two different types of features, i.e., vibration and entropy, time-dependent degradation models are obtained through curve fitting. For the variance feature, an exponential of the form aebt was found to be the most suitable, whereas for the TF entropy feature, a curve in the form of a − be−ct was more suitable… The parameters of the degradation function are updated with each new measurement. To accomplish this, a state vector x containing the equation for the curve fit as well as the unknown parameters describing this degradation model at each time point are defined for each feature. For the variance feature, the parameters ak and bk of the exponential curve are used to define the state vector as [43], [53] xk = [akebkk ak bk]T (5) and for the entropy feature, we have xk = [ak – bke−ckk ak bk ck]T (6) both with the measurement equation given by zk = h(xk) = xk(1). (7)” It is also noted that there is no input to this system; hence, in our case, uk defined in (3) is equal to zero. With each time step, the parameters of the degradation model are updated to form a new model, i.e., fk, and an estimate of the next state, i.e., ˆxk, is calculated. Functions f and h are then locally linearized about that estimate to produce Fk and Hk” Pg. 1783 D. Extended Kalman Filtering section of Singleton states “The EKF equation in the presence of process noise and measurement noise is xk = f(xk−1, uk−1) +wk−1 (3) where xk is the state being estimated, f is a nonlinear function of states, uk is the input at time sample k, wk is random zero mean noise with covariance matrix Qk. In EKF, the relationship between system states (xk) and measurements (zk) can also be nonlinear, i.e., zk = h(xk)+vk (4) where zk is the measurement, h is a measurement function that is a nonlinear function of states, and vk is a zero-mean random process described by the measurement noise covariance matrix”)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Singleton, Wang, Gang Wang, and Ma teaches
locally linearizing, on the basis of the system state space equation, the nonlinear functions
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at the moment k about a state prior estimate
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, to obtain corresponding Jacobian metrices
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; building a prediction and update equation of an extended Kalman filter according to the Jacobian matrices
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; (Pg. 1785 C.EKF Parameter Learning section of Singleton states “With each time step, the parameters of the degradation model are updated to form a new model, i.e., fk, and an estimate of the next state, i.e., ˆxk, is calculated. Functions f and h are then locally linearized about that estimate to produce Fk and Hk by
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.” Pg. 1783 D. Extended Kalman Filtering section of Singleton states “To carry out the normal KF operations, the nonlinear functions, i.e., f and h, must be locally linearized around the estimated state by calculating their respective Jacobian, producing matrices F and H, respectively. One important point in the implementation of EKF is the choice of the initial parameters, as the speed of convergence depends on the initial estimate ˆx0 and the uncertainty matrix P0 [52].”)
and alternately executing, on the basis of the prediction and update equation, a prediction and update process of the extended Kalman filter to continuously update a predicted state vector, so as to obtain the optimal state estimate. (Pg. 1785 C. EKF Parameter Learning section of Singleton states “The parameters of the degradation function are updated with each new measurement. To accomplish this, a state vector x containing the equation for the curve fit as well as the unknown parameters describing this degradation model at each time point are defined for each feature. For the variance feature, the parameters ak and bk of the exponential curve are used to define the state vector” Pg. 1785-86 D. RUL Prediction section of Singleton states “An overall view of the algorithm and all its steps are given below: 1) Initialize x0 and P0. 2) Predict the next state, ˆxk, and uncertainty matrix, Mk, i.e., ˆxk =f(ˆxk−1) + wk−1 (14) Mk =Fk−1Pk−1Fk−1 + Qk−1. (15) 3) Take in measurement zk. 4) Update the predictions and their uncertainties using the Kalman gain, Kk, i.e., Kk =MkHTkHkMkHTk+ Rk−1 (16) ˆxk ←− ˆxk +Kk(zk −Hkˆxk) (17) Pk =(1 −KkHk)Mk. (18) 5) The current value of the feature state is extrapolated out to failure threshold. The total number of time steps, i.e., n, required to reach the failure threshold is taken as the RUL at time k, i.e., ¯γo = ˆxk+n = fk(ˆxk+n−1) + wk+n. (19) 6) Calculate the confidence intervals of RUL predictions (see Section III-E). 7) Repeat process starting at step 3.” Singleton teaches alternatively executing a prediction and update process of the extended Kalman filter to continuously update a predicted state vector and update state estimate. )
Regarding claim 4, the rejection of claim 3 is incorporated herein. Furthermore, the combination of Singleton, Wang, Gang Wang, and Ma teaches
determining the first predicting time on the basis of original state monitoring data of the mechanical component collected by a sensor; (Pg. 3 3.1 FPT selection section of Gang Wang states “In order to use EM to predict the RUL of rolling bearings, FPT must be determined first. The rolling bearings degradation state after FPT is the fault stage, in which the prediction of RUL is implemented. In the normal operation stage of rolling bearings, the bearing signal is irregular vibration caused by environmental noise, and the RMS hardly changes with time. However, in the fault stage, the signal-to-noise ratio (SNR) of the fault signal increases with time, resulting in an exponential rise of RMS with time.” Pg. 5 4. Experimental evaluation section of Gang Wang states “The contents observed on the rolling bearings components before and after the experiment are shown in Fig. 5. During the experiment, the vibration signals of rolling bearings are recorded by acceleration sensor. The acceleration sensor performs sampling once every 10 s, and the sampling frequency is 25.6 kHz. Therefore, each sample contains 2560 data points, that is, 0.1 s. All the rolling bearings signal process and EM construction and RUL prediction are completed by python 3.6 program: https://github.com/famer3riots/PHM-RUL-Prediction-by-EM. In addition, TensorFlow 2.3 is used to construct and train the EM.” Gang Wang teaches that bearing RUL prediction requires determining an appropriate first prediction time.)
extracting the state monitoring data in the degradation stage of the mechanical component on the basis of the first predicting time; (Wang states “The method firstly performs FFT conversion to the original vibration signals the rolling bearing, then the frequency domain amplitude signal obtained by the pre-processing is normalized, and it is used as the input of CNN. using CNN with convolution operation, weight sharing and so characteristics, automatically extracting data local abstract information to excavate the deep features, avoiding the problem that the traditional feature extraction method is too dependent on expert experience.” Pg. 3 3.1 FPT selection section of Gang Wang states “In order to use EM to predict the RUL of rolling bearings, FPT must be determined first. The rolling bearings degradation state after FPT is the fault stage, in which the prediction of RUL is implemented. In the normal operation stage of rolling bearings, the bearing signal is irregular vibration caused by environmental noise, and the RMS hardly changes with time. However, in the fault stage, the signal-to-noise ratio (SNR) of the fault signal increases with time, resulting in an exponential rise of RMS with time.” )
and using the FFT to extract frequency domain information of the state monitoring data in the degradation stage, to obtain the frequency domain data in the degradation stage. (Wang states “The method firstly performs FFT conversion to the original vibration signals the rolling bearing, then the frequency domain amplitude signal obtained by the pre-processing is normalized, and it is used as the input of CNN. using CNN with convolution operation, weight sharing and so characteristics, automatically extracting data local abstract information to excavate the deep features, avoiding the problem that the traditional feature extraction method is too dependent on expert experience.” Wang applies FFT to extract frequency domain amplitude signal of the monitored original vibration signal. )
Regarding claim 5, the rejection of claim 4 is incorporated herein. Furthermore, the combination of Singleton, Wang, Gang Wang, and Ma teaches
constructing the neural network training data set
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of all the mechanical components according to the optimal state estimate
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and the frequency domain data
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in the degradation stage of the mechanical component, a moment
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being the first predicting time,
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being a length of a variance feature sequence, Q being the number of the mechanical components i, and
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being the remaining useful life of the mechanical component i at the moment k. (Wang states “
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1) selecting part data of rolling bearing vibration signals different working conditions as the training set, and performing FFT conversion on the original vibration signals of the training set to obtain the frequency domain amplitude signal; 2) the frequency domain amplitude signal after performing normalization processing as characteristic input, life percentage as output training model, formula wherein xt belongs to RN * 1 represents the N-dimensional feature input of a bearing at time t, N=2048, yt belongs to [0, 1] represents the bearing at time t of life degradation percentage output; Dtra vibration signals the data of a bearing of a certain working condition in training set, R is frequency domain amplitude characteristic matrix; T is the running time of the bearing full service life;” Wang shows the constructing the neural network training dataset and obtaining the frequency domain data in the degradation stage. Pg. 1785 C.EKF Parameter Learning section of Singleton states “For the two different types of features, i.e., vibration and entropy, time-dependent degradation models are obtained through curve fitting. For the variance feature, an exponential of the form aebt was found to be the most suitable, whereas for the TF entropy feature, a curve in the form of a − be−ct was more suitable… With each time step, the parameters of the degradation model are updated to form a new model, i.e., fk, and an estimate of the next state, i.e., ˆxk, is calculated.” Singletone teaches generating and updating the EKF state estimate which corresponds to the optimal state estimate for each time given. Singletone also teaches a variance feature tracked over time as part of the degradation feature sequence, which length of it could be denoted as ni . Pg. 3 3.1 FPT selection section of Gang Wang states “In order to use EM to predict the RUL of rolling bearings, FPT must be determined first. The rolling bearings degradation state after FPT is the fault stage, in which the prediction of RUL is implemented.” Gang Wang directly states that FPT should be determined so it could be accounted for constructing data set. The notation I = 1,…,Q merely indexes multiple mechanical components and would have been obvious when constructing a training dataset for multiple mechanical components. Regarding y being the remaining useful life of the mechanical component at the moment k, Pg. 1786 of Singletone algorithm states “The current value of the feature state is extrapolated out to failure threshold. The total number of time steps, i.e., n, required to reach the failure threshold is taken as the RUL at time k, i.e.,” It gives an explicit RUL estimate at time K before meeting the failure threshold.)
Claims 6 and 7 recite substantially similar subject matter to claim 1 respectively and are rejected with the same rationale, mutatis mutandis.
Claims 9 – 12 recite substantially similar subject matter to claim 2 – 5 respectively and are rejected with the same rationale, mutatis mutandis.
Conclusion
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/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121