“Prognostic And Health Management System For System Management And Method Thereof” in Patent Application Approval Process (USPTO 20230022100): Patent Application

2023 FEB 15 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors CHEN, MENG-JEN (Hsin-Chu, TW); CHEN, YEN-JEN (Hsin-Chu, TW); PAN, YING-HAO (Hsin-Chu, TW); WEN, YUAN-HAO (Hsin-Chu, TW), filed on July 12, 2022, was made available online on January 26, 2023, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors from the background information supplied by the inventors: “The core of a prognostic and health management mechanism is the use of various combinations of advanced sensors with various algorithms and artificial intelligence models to predict, monitor and manage the operating status of various systems to ensure the smooth operation of independent self-testing mechanisms and self-maintenance mechanisms. More specifically, a prognostic and health management mechanism monitors the environment parameters detected by the various systems in real time to monitor the operating status of systems or equipment, or even the range and period of frequent failure. A prognostic and health management mechanism can further monitor and analyze the data to predict possible failures occurring in the future to greatly improve the operation efficiency and stability of the system or equipment. Because of these characteristics, a prognostic and health management mechanism generally has the ability of independent failure detection and isolation, failure determination, failure prediction, health management, and life cycle tracking and management of individual components included in the system or equipment.

“However, when trying to introduce the mechanisms of prognostic and health management for system and equipment management, ordinary enterprises will find it quite time-consuming and labor-consuming to go from zero to implementation. More specifically, the implementation of the prognostic and health management mechanism will include the following stages: installing sensors to collect data to establish a database; data preprocessing and graph conversion; classifying the definition of normal and abnormal graphic features; convolution and pooling architecture processing of convolutional neural network (CNN); full connection layer architecture and deep learning of deep neural network (DNN); testing and diagnosis of new data; establishment of models; online detection of the transfer of equipment.

“During the establishment of these mechanisms mentioned above, at the same time, engineers need to be trained to deal with program writing and maintenance. Under the premise of ordinary recruitment and related training, the estimated time cost of personnel training and system establishment can include the following, which are not easy to pipeline: three months of Python language training with more than three years of practical experience in the field, eight hours of sensor arrangement, one week of sensor data acquisition, one month of data preprocessing and conversion, one month of feature extraction, one month of model establishment, two months of model training, one month of model prediction, one month of data analysis, one month planning of the architecture of convolutional neural network, three months of VC++ language training, three months of programmable logic controller arrangement and operation, and three months of artificial intelligence related courses. Among the above-mentioned costs of time consumption and result control, there are several parts that aim at training engineers, and the results of which are the most unpredictable and time-consuming. It is because artificial intelligence technologies have grown and advanced over the years, which makes training costs increase rapidly with time.

“Refer to FIG. 1. FIG. 1 is a schematic flow chart of the implementation of a prognostic and health management mechanism in the prior art. The main principle of FIG. 1 is performing diagnose and correction on the system/device (hereinafter referred to as “system”, but it still can be replaced by “device”) by confirming whether the actuation between the current data and the current system is normal. In the scenario of FIG. 1, it is assumed that the system monitored by the prognostic and health management mechanism will continually update new input data and continually be confirmed whether it operates normally.

“First, in step 102, a starting point will be determined to determine whether the currently received input data will trigger any actuation state of the system. If the currently received input data will not trigger any action state of the system, execute step 104 to delete the currently received input data to abandon the monitoring of the data, and go back to step 102 again to determine whether the next received input data will trigger any action state of the system. If the currently received input data triggers any actuation state of the system in step 102, proceed to step 106.

“In step 106, a detailed actuation classification will be made for the current input data. Step 108 will confirm whether the actuation classification corresponds to a complete actuation of the system. If step 108 confirms that it does not correspond to a complete actuation of the system, the current input data is temporarily stored in step 110, and it will wait in step 106 for the next input data related to triggering the system to complete the first cycle. In step 108, if the next input data is determined to not correspond to the complete actuation of the system either, the next input data will merge with the input data temporarily stored in the first cycle in step 110, and the process will further wait for another data in step 106 to form a second cycle. Finally, the data will continue to merge with new input data until it is determined in step 108 that it is related to a complete actuation of the system. Accordingly, a final merged actuation data will be generated, and the process will proceed to step 112.

“In step 112, the mechanism of the diagnostic and health management determines, by convolution neural network, whether the final merged actuation data will cause a complete abnormal actuation of the system. If the final merged actuation data is determined to not cause an abnormal actuation, a diagnostic data representing normal actuation will be outputted. If the final merged actuation data is determined to cause an abnormal actuation, a diagnostic data representing abnormal actuation will be outputted, and the system will be forced to perform further diagnosis or repair operations. Regardless of the diagnosis result in step 112, the prognostic and health management mechanism will temporarily store the non-actuating data accumulated so far, and merge them with the new input data and the derived diagnostic data as historical analysis and diagnosis record.

“However, the conventional diagnostic and health management mechanism mentioned above has its disadvantages. More specifically, in practice, the operation of the prognostic and health management mechanism will quickly accumulate a large amount of input data and massive diagnostic data derived from the large amount of input data. Therefore, it is usually impossible for the conventional mechanism to completely merge the input data and diagnostic data immediately. Instead, it will merge the large amount of data once a day on a daily basis and temporarily store the data in a temporary memory database before merging. However, if the amount of data accumulated per unit time (day) is too large, the complexity of data merging will also increase sharply to the degree that the data cannot be processed immediately. Ultimately, the conventional mechanism will not be able to correctly perform the function of early warning of anomaly because of insufficient time for real-time record.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “The disclosure provides a machine-learning-based prognostic and health management system and methods thereof to solve the shortcoming of the prior arts.

“In an embodiment, a machine-learning-based prognostic and health management method comprises: dynamically receiving data of a machine under test associated with operations of the machine under test; dynamically receiving a model-assigning command; dynamically applying a damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test; and dynamically generating, according to the anomaly probability, a damage possibility warning on the machine under test, and determining whether to keep the machine under test running or not, wherein the damage alert machine-learning model comprise a complete-life-cycle machine-training model, a failure-free machine-training model and a value-to-image machine-training model; wherein the complete-life-cycle machine-training model is based on a complete life cycle operation record of at least one machine, the failure-free machine-training model is based on an operation record of at least one failure-free machine, and the value-to-image machine-training model is based analysis on images converted from values stored in an operation record of at least one machine.

“In an embodiment, the deep neural network model comprises a low-rank factorization deep neural network model.

“In an embodiment, the method comprises: applying the logistic regression and logical model to perform a low-rank factorization to classify the data of the machine under test by applying a regression curve; applying deep neural network to establish a deep network model; and applying the deep network model to determine current status of the machine under test and the corresponding anomaly probability.

“In an embodiment, the failure-free machine-training model comprises support vector data description model.

“In an embodiment, the method comprises: performing frequency domain and time domain operations on the data of the machine under test based on a frequency feature and a temporal feature of the data of the machine under test; and applying support vector data description to the data of the machine under test with the frequency domain and time domain operations performed to establish an optimization model to classify the anomaly probability in different data points of the data of the machine under test.

“In an embodiment, the value-to-image machine-training model comprises convolutional neural network model.

“In an embodiment, the method comprises: performing processes of image data filtering and cutting and anomaly data generating on the data of the machine under test to convert the data of the machine under test to an image data; extracting eigenvalues of the image data according to an image feature of the image data to optimize parameters of the image data; and analyzing the image data having the optimized parameters by using convolutional neural network model to determine the current status of the machine under test and analyze total anomaly probability corresponding to the data of the machine under test.

“In an embodiment, the method comprises: when the model-assigning command dynamically assigns another damage alert machine-learning model that is different from the currently used damage alert machine-learning model, dynamically switching to the other damage alert machine-learning model to process the data of the machine under test to update the prediction of the anomaly probability.

“The machine-learning based prognostic and health management system of the disclosure comprises: a machine sensor configured to dynamically receive data of a machine under test associated with operations of the machine under test; an instruction receiver configured to dynamically receive a model-assigning command; a processor configured to dynamically apply the damage alert machine-learning model corresponding to the model-assigning command to process the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test, wherein the processor also dynamically generates, according to the anomaly probability, a damage possibility warning on the machine under test, and determine whether to keep the machine under test running or not; and an annunciator configured to inform, according to the damage possibility warning, the anomaly probability and a suggestion on whether to keep running; wherein the damage alert machine-learning model comprises a complete-life-cycle machine-training model, a failure-free machine-training model and a value-to-image machine-training model; wherein the complete-life-cycle machine-training model is based on the complete life cycle operation record of at least one machine, the failure-free machine-training model is based on the operation record of at least one failure-free machine, and the value-to-image machine-training model is based the analysis on images converted from values stored in an operation record of at least one machine.

“In an embodiment, the processor comprises: a complete-life-cycle machine-learning module applying the complete-life-cycle machine-training model; a failure-free machine-learning module applying the failure-free machine-training model; and a value-to-image machine-learning module applying the value-to-image machine-training model; wherein the processor further dynamically assigns to use one of the complete-life-cycle machine-learning module, the failure-free machine-learning module and the value-to-image machine-learning module to dynamically apply the corresponding damage alert machine-learning model to process the data of the machine under test.

“In an embodiment, the deep neural network model comprises a deep neural network model with low-rank decomposition.

“In an embodiment, the complete-life-cycle machine-learning module comprises: a logistic regression module; a logical model module configured to perform, with the logistic regression module, low-rank decomposition to classify the data of the machine under test by applying a regression curve; a deep neural network module configured to establish, according to the data of the machine under test, a deep network mode and determine, according to the deep network model, current status of the machine under test and the corresponding anomaly probability.

“In an embodiment, the failure-free machine-training model comprises a support vector data description model.

“In an embodiment, the failure-free machine-learning module comprises: a frequency feature module; a temporal feature module configured to perform, with the frequency feature module, frequency domain and time domain operations on the data of the machine under test based on the frequency feature and the temporal feature of the data of the machine under test; and a support vector data description module configured to apply support vector data description to the data of the machine under test with the frequency domain and time domain operations performed to establish an optimization model to classify the anomaly probability in different data points of the data of the machine under test.

“In an embodiment, the value-to-image machine-training model comprises a convolutional neural network model.

“In an embodiment, the value-to-image machine-learning module comprises: an image data filtering and cutting module; a virtual abnormal data generating module configured to perform, with the image data filtering and cutting module, image data filtering and cutting and anomaly data generating on the data of the machine under test to convert the data of the machine under test to an image data, and extract eigenvalues of the image data according to an image feature of the image data to optimize parameters of the image data; and a convolutional neural network model module configured to analyze the image data having the optimized parameters by using the convolutional neural network model to determine the current status of the machine under test and analyze total anomaly probability corresponding to the data of the machine under test.

“In an embodiment, when the model-assigning command dynamically assigns another damage alert machine-learning model different from the currently used damage alert machine-learning model, the processor is configured to dynamically switch to the other damage alert machine-learning model to process the data of the machine under test to update the prediction of the anomaly probability.”

The claims supplied by the inventors are:

“1. A machine-learning based prognostic and health management method, comprising: dynamically receiving data of a machine under test associated with operations of the machine under test; dynamically receiving a model-assigning command; dynamically applying a damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test; and dynamically generating, according to the anomaly probability, a damage possibility warning on the machine under test, and determining whether to keep the machine under test running or not, wherein the damage alert machine-learning model comprises a complete-life-cycle machine-training model, a failure-free machine-training model and a value-to-image machine-training model; wherein the complete-life-cycle machine-training model is based on a complete life cycle operation record of at least one machine, the failure-free machine-training model is based on an operation record of at least one failure-free machine, and the value-to-image machine-training model is based analysis on images converted from values stored in an operation record of at least one machine.

“2. The method of claim 1, wherein the complete-life-cycle machine-training model comprises a low-rank factorization deep neural network model.

“3. The method of claim 2, wherein dynamically applying the damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict the anomaly probability of the anomaly occurrence of the machine under test, comprises: applying logistic regression and logical model for performing low-rank factorization to classify the data of the machine under test by applying a regression curve; applying deep neural network to establish a deep network model; and applying the deep network model to determine current status of the machine under test and corresponding anomaly probability.

“4. The method of claim 1, wherein the failure-free machine-training model comprises support vector data description model.

“5. The method of claim 1, wherein dynamically applying the damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict the anomaly probability of the anomaly occurrence of the machine under test, comprises: performing frequency domain and time domain operations on the data of the machine under test based on a frequency feature and a temporal feature of the data of the machine under test; and applying support vector data description to the data of the machine under test with the frequency domain and time domain operations performed to establish an optimization model to classify the anomaly probability in different data points of the data of the machine under test.

“6. The method of claim 1, wherein the value-to-image machine-training model comprises convolutional neural network model.

“7. The method of claim 1, wherein dynamically applying the damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict the anomaly probability of the anomaly occurrence of the machine under test, comprises: performing processes of image data filtering and cutting, and anomaly data generating on the data of the machine under test to convert the data of the machine under test to an image data; extracting eigenvalues of the image data according to an image feature of the image data to optimize parameters of the image data; and analyzing the image data having the optimized parameters by using convolutional neural network model to determine current machine under test status and analyze total anomaly probability corresponding to the data of the machine under test.

“8. The method of claim 1, wherein dynamically applying the damage alert machine-learning model corresponding to the model-assigning command for processing the data of the machine under test to predict the anomaly probability of the anomaly occurrence of the machine under test, comprises: when the model-assigning command dynamically assigns another damage alert machine-learning model different from the currently used damage alert machine-learning model, dynamically switching to the another damage alert machine-learning model for processing the data of the machine under test to update the prediction of the anomaly probability.

“9. A machine-learning based prognostic and health management system, comprising: a machine sensor configured to dynamically receive data of a machine under test associated with operations of the machine under test; an instruction receiver configured to dynamically receive a model-assigning command; a processor configured to dynamically apply a damage alert machine-learning model corresponding to the model-assigning command for processing the data the machine under test to predict an anomaly probability of an anomaly occurrence of the machine under test, the processor also dynamically generates, according to the anomaly probability, a damage possibility warning on the machine under test, and determine whether to keep the machine under test running or not; and an annunciator configured to inform, according to the damage possibility warning, the anomaly probability and a suggestion on whether to keep running; wherein the damage alert machine-learning model comprises a complete-life-cycle machine-training model, a failure-free machine-training model and a value-to-image machine-training model; wherein the complete-life-cycle machine-training model is based on a complete life cycle operation record of at least one machine, the failure-free machine-training model is based on an operation record of at least one failure-free machine, and the value-to-image machine-training model is based analysis on images converted from values stored in an operation record of at least one machine.

“10. The prognostic and health management of claim 9, wherein the processor comprises: a complete-life-cycle machine-learning module applying the complete-life-cycle machine-training model; a failure-free machine-learning module applying the failure-free machine-training model; and a value-to-image machine-learning module applying the value-to-image machine-training model; wherein the processor further dynamically assigns to use one of the complete-life-cycle machine-learning module, the failure-free machine-learning module and the value-to-image machine-learning module to dynamically apply the corresponding damage alert machine-learning model for processing the data of the machine under test.

“11. The prognostic and health management of claim 10, wherein the deep neural network model comprises a low-rank factorization deep neural network model.

“12. The prognostic and health management of claim 11, wherein the complete-life-cycle machine-learning module comprises: a logistic regression module; a logical model module configured to perform, with the logistic regression module, low-rank factorization to classify the data of the machine under test by applying a regression curve; a deep neural network module configured to establish, according to the data of the machine under test, a deep network mode and determine, according to the deep network model, current status of the machine under test and corresponding anomaly probability.

“13. The prognostic and health management of claim 10, wherein the failure-free machine-training model comprises support vector data description model.

“14. The prognostic and health management of claim 13, wherein the failure-free machine-learning module comprises: a frequency feature module; a temporal feature module configured to perform, with the frequency feature module, frequency domain and time domain operations on the data of the machine under test based on a frequency feature and a temporal feature of the data of the machine under test; and a support vector data description module configured to apply support vector data description to the data of the machine under test with the frequency domain and time domain operations performed to establish an optimization model to classify the anomaly probability in different data points of the data of the machine under test.

“15. The prognostic and health management of claim 10, wherein the value-to-image machine-training model comprises a convolutional neural network model.

“16. The prognostic and health management of claim 15, wherein the value-to-image machine-learning module comprises: an image data filtering and cutting module; a virtual abnormal data generating module configured to perform, with the image data filtering and cutting module, process of image data filtering and cutting and anomaly data generating on the data of the machine under test to convert the data of the machine under test to an image data, and extract eigenvalues of the image data according to an image feature of the image data to optimize parameters of the image data; and a convolutional neural network model module configured to analyze the image data having the optimized parameters by using the convolutional neural network model to determine current machine under test status and analyze total anomaly probability corresponding to the data of the machine under test.

“17. The prognostic and health management of claim 10, when the model-assigning command dynamically assigns another damage alert machine-learning model different from the currently used damage alert machine-learning model, the processor is configured to dynamically switch to the another damage alert machine-learning model for processing the data of the machine under test to update the prediction of the anomaly probability.”

URL and more information on this patent application, see: CHEN, MENG-JEN; CHEN, YEN-JEN; PAN, YING-HAO; WEN, YUAN-HAO. Prognostic And Health Management System For System Management And Method Thereof. U.S. Patent Application Number 20230022100, filed July 12, 2022 and posted January 26, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230022100)&db=US-PGPUB&type=ids

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