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 .
DETAILED ACTION
Claims 1-20 are presented for examination.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Natsumeda et al (US Pub. 2022/0004182; hereinafter Natsumeda) in view of Maeda et al (US Pub. 2012/0316835; hereinafter Maeda).
As per claim 1, Natsumeda discloses a method for predicting a system fault in a monitored system based on a predefined global automaton that includes a plurality of distinct degradation stages, a transition from a healthy stage to at least one of the degradation stages, and a transition from at least one of the degradation stages to a faulty stage [Abstract; para 0028; claim 1; determination of a point in time that a health index (HI) of a system changes from a healthy stage to a degradation stage and collecting time series measurements of different attributes of the system by a plurality of sensors of the system, wherein time series data may include a time period to an end-of-life for the system, which may include details regarding system failures and system maintenance].
Natsumeda does not specifically disclose regarding wherein each of the plurality of degradation stages corresponds to a different signal feature trajectory class. However, Maeda (in the same field of endeavor, i.e., detecting an anomaly or a fault in equipment such as a plant) discloses if the trajectories of past anomalous cases A, B, and so on are already known and present in a database, the types of the anomalies can be identified (or diagnosed) by doing collation [Abstract; para 0105]. 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 the cited references as both are directed to for early detection of an anomaly or a fault in a plant, equipment or like.
As per claim 2, Maeda discloses wherein each trajectory class is defined at least in part by a monitorable signal feature of the system and a state equation that is a function of the signal feature, the state equation being different for each of the feature trajectory classes [para 0072; “… as a learning method adopted in a case where anomaly data or abnormality data can be used, metric learning that learns vector quantization for updating a dictionary pattern or a distance function can be employed.”].
As per claim 3, Maeda discloses wherein each trajectory class is defined at least in part by a monitorable signal feature of the system and a state equation that is a function of the signal feature and a variable parameter, the variable parameter being different for each of the feature trajectory classes [Fig. 5B; para 0073; “A local subspace classifier is also a type of subspace classifier. k is a parameter. In anomaly detection, the distance from the unknown pattern q (the newest observation pattern) to the normal class is found and taken as a deviation (residual).”].
As per claim 4, Maeda discloses wherein each trajectory class is defined at least in part by a monitorable signal feature, a state equation that is a function of the signal feature and a parameter, and a constraint on the parameter [Fig. 5B; para 0073; “A local subspace classifier is also a type of subspace classifier. k is a parameter. In anomaly detection, the distance from the unknown pattern q (the newest observation pattern) to the normal class is found and taken as a deviation (residual).”].
As per claim 5, Maeda discloses wherein each trajectory class corresponding to one of the degradation stages having a transition to the faulty stage is a trending trajectory class [para 0146; “ In order to carry out the state maintenance, it is necessary to collect data indicating whether the equipment is normal or faulty.”].
As per claim 6, Maeda discloses wherein the global automaton includes a plurality of degradation paths from the healthy stage to the faulty stage [para 0105; “If the trajectories of past anomalous cases A, B, and so on are already known and present in a database, the types of the anomalies can be identified (or diagnosed) by doing collation against them.”].
As per claim 7, Maeda discloses wherein at least one of the plurality of degradation paths includes more than one of the plurality of degradation stages [para 0105; “If the trajectories of past anomalous cases A, B, and so on are already known and present in a database, the types of the anomalies can be identified (or diagnosed) by doing collation against them.”].
As per claim 8, Natsumeda discloses further comprising an iterative fault prediction step based on extrapolation of a signal feature trajectory of one of the degradations stages having a transition to the faulty stage [para 0088; “RULENet simultaneously optimizes its Dual-estimator for RUL estimation and the change point estimate from unpredictable health stage to predictable health stage and health stage classification.” And Maeda further adds: “… the trajectories of the past anomalous cases A, B, and so on described in FIG. 11 are stored in the database DB 121 and collated against it to identify (diagnose) the type of the anomaly.”; para 0121 and 0146; “In order to carry out the state maintenance, it is necessary to collect data indicating whether the equipment is normal or faulty. The amount and quality of the data determine the quality of the state maintenance.”].
As per claim 9, Natsumeda discloses further comprising an iterative fault prediction step based on a signal feature history and health stage history of the monitored system and independent from external degradation models [para 0028; “The stored time series data can form a historical record of the performance of the system 110 based on the monitored attributes. The data and record(s) can include a time period to the end-of-life for the system 110, which can include details regarding system failures and system maintenance.”].
As per claim 10, Maeda discloses wherein one of the plurality of degradation stages is an unknown degradation stage used to capture signal feature behavior that does not fit within any of the other degradation stages [para 0069; “If an unknown pattern q (the newest observation pattern) is applied, the length of an orthogonal projection onto a subspace or the projection distance to the subspace is found. For a multidimensional time-series signal, a normal portion is handled in a fundamental manner. Therefore, the distance from the unknown pattern q (the newest observation pattern) to a normal class is found and taken as a deviation (residual). If the deviation is great, it is determined that it is an outlier.”].
As per claim 11, Natsumeda discloses further comprising repeated system monitoring, including: observing a new instance of a signal feature upon which each trajectory class is based [Fig. 4; para 0044; “RUL linearly decreases over time, but the health index (HI) does not always due to nonlinearity in degradation process. It can be severe especially when the degradation process has multi-stages.”]; and making a determination pertinent to a current health stage of the monitored system using the observed new instance of the signal feature, wherein the current health stage is selected from the healthy stage or one of the degradation stages of the global automaton [Maeda – para 0059; “The subject is a multidimensional time-series sensor signal. It is a generated voltage, the temperature of exhaust gas, the temperature of cooling water, the pressure of cooling water, the running time, or the like. The installation environment or the like is also monitored. The sampling timing of the sensor similarly varies greatly, for example, from tens of ms to tens of seconds.”].
As per claim 12, Natsumeda discloses wherein the step of making the determination includes using a stage estimation process that determines an estimated probability that the monitored system has transitioned from the current health stage to a next health stage of the global automaton based in part on the observed new instance of the signal feature [para 0023; “… Predictive Maintenance can be used to monitor an equipment's and/or component's condition and identify maintenance program(s). Predictive Maintenance can include: 1) a remaining useful life estimation, which estimates the remaining time to the end of the equipment's useful life, and 2) failure prediction, which predicts the probability that the equipment will fail within a predetermined time frame.”].
As per claim 13, Maeda discloses wherein the step of making the determination includes using a trajectory updating process that determines a value for a variable parameter of a state equation of the trajectory class corresponding to the current health stage based in part on the observed new instance of the signal feature [para 0072; “… as a learning method adopted in a case where anomaly data or abnormality data can be used, metric learning that learns vector quantization for updating a dictionary pattern or a distance function can be employed.”].
As per claim 14, Maeda discloses wherein the step of making the determination includes using a fault prediction process that determines a predicted time to reach the system fault by extrapolating a trajectory of the signal feature based on previously observed instances of the signal feature, including the observed new instance of the signal feature [Fig. 11; para 0169; “… the motion of the ending point of an anomaly measurement vector is represented. The time taken to reach the anomalous case A can be estimated if the velocity of motion of this vector is calculated. Alternatively, if the past motion of the ending point of the anomaly measurement vector leading to the anomalous case A is stored, the current state can be grasped during the course reaching the anomalous case A by collation with them. Hence, the time at which the anomaly occurred can be estimated.”].
As per claim 15, Maeda discloses wherein the step of making the determination includes using an anomaly detection process that determines whether the observed new instance of the signal feature is anomalous relative to previously observed instances of the signal feature [para 0069; “If an unknown pattern q (the newest observation pattern) is applied, the length of an orthogonal projection onto a subspace or the projection distance to the subspace is found. For a multidimensional time-series signal, a normal portion is handled in a fundamental manner. Therefore, the distance from the unknown pattern q (the newest observation pattern) to a normal class is found and taken as a deviation (residual). If the deviation is great, it is determined that it is an outlier.”].
As per claim 16, Natsumeda discloses further comprising defining a local automaton indicative of a health stage history of the monitored system, the local automaton including a current health stage of the monitored system selected from the healthy stage or one of the degradation stages, wherein the health stage history includes only health stages that are part of the global automaton [Fig. 1-7; para 0095; “Part or all of processing system 1000 may be implemented in one or more of the elements of FIGS. 1-7. Further, it is to be appreciated that processing system 800 may perform at least part of the methods described herein including, for example, at least part of the method of FIGS. 1-7.”].
As per claim 17, Natsumeda discloses further comprising expanding the local automaton to include an additional degradation stage of the global automaton [Fig. 1-7; para 0095; “Part or all of processing system 1000 may be implemented in one or more of the elements of FIGS. 1-7. Further, it is to be appreciated that processing system 800 may perform at least part of the methods described herein including, for example, at least part of the method of FIGS. 1-7.”].
As per claim 18, Maeda discloses further comprising modifying the global automaton to include a new degradation stage that corresponds to a new signal feature trajectory class [Fig. 5B; para 0073; “Multidimensional time-series signals which are k in number and close to an unknown pattern q (the newest observation pattern) are found. A linear manifold in which the nearest pattern of classes gives the origin is created. Unknown patterns are classified into classes at a minimum projection distance to the linear manifold. A local subspace classifier is also a type of subspace classifier.”].
As per claim 19, Maeda discloses wherein the new signal feature class is based at least in part on one or more anomalous signal feature observations [Fig. 5B; para 0073; “Multidimensional time-series signals which are k in number and close to an unknown pattern q (the newest observation pattern) are found. A linear manifold in which the nearest pattern of classes gives the origin is created. Unknown patterns are classified into classes at a minimum projection distance to the linear manifold. A local subspace classifier is also a type of subspace classifier.”].
As per claim 20, Maeda discloses wherein the monitored system is an industrial process [para 0172; “The present invention can be used as anomaly detection in plants and equipment.”].
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
C. US-12505262 discloses systems and methods for detecting and predicting faults in an industrial process automation system.
D. US-20190278648 discloses methods and systems for adaptive fault prediction analysis.
N. CN-114298164 discloses a self-adaptive fault prediction method based on KLMS algorithm and filter trend.
O. CN-101859128 discloses a knowledge-based fault prediction expert system for a complex milling machine tool, which comprises a man-machine interface module, a data acquisition and preprocessing module, a knowledge acquisition module based on data mining, a self-adaptive fault prediction module, a fault diagnosis expert system, an interpreter, an inference machine, a prediction method library, an integrated knowledge library, a model library and a diagnosis module.
P. CN-11291918 discloses a prediction method for rotating machinery degradation trend of exogenous vector autoregression in stationary subspace.
Conclusion
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/SURESH SURYAWANSHI/Primary Examiner, Art Unit 2116
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