Claims:1. A system for hierarchical machine learning for an industrial plant machine learning system, comprising:
a memory device for storing a latent variable machine learning model and a joint probability model that are previously learned from the industrial plant;
an acquisition module configured to acquire an input data of an abnormality detection target in machine learning system;
a data encryption unit for inferring a latent variable machine learning model from the input data based on the latent variable machine learning model stored in the memory device;
a decryption unit for generating restored data from the latent variable machine learning model based on the joint probability model stored in the memory device; and
a hierarchical machine learning determination unit designed to determine whether the input data is normal or abnormal for the provided set of the latent variable machine learning model, which is based on a deviation between the input data and the restored data.
2. The system as claimed in claim 1, wherein the determination unit is implemented through a processing unit to determine an amount of the deviation between the input data and the restored data based on probability calculated for the provided set of the latent variable machine learning model according to parameters obtained in processes at the data encryption unit and decryption unit.
3. The system as claimed in claim 1, wherein the processing unit is configured to perform the inferring and generating by the data encryption unit and decryption unit, which includes, but not limited to, a Variational Auto Encoder, Adversarial Auto Encoder, Ladder Variational Auto Encoder, and Auxiliary Deep Generative Model.
4. The system as claimed in claim 1, wherein the processing unit is further configured to a discriminator that inputs he feature quantity generated by the data encryption unit of the self-encoder and generates an estimated label, the label corresponding to the input data and the estimated label are matched through a classifier for the industrial plant machine learning system.
5. The system as claimed in claim 1, wherein the classifier is designed to provide a computer perform the process which makes the data encryption unit to learn and trained so that the label corresponding to the input data and the estimation label can leave and separate using the discriminator.
6. The system as claimed in claim 1, wherein the processing unit is further connected with a plurality of means for learning an abnormality in the hierarchical machine leaning model from the operation data sets an initial abnormality hierarchical data detection model, and sequentially reconstructs the hierarchical machine leaning model with a small variation in the abnormality score from the abnormality detection model.
7. The system as claimed in claim 1, wherein the plurality of means for learning the abnormality detection model from the industrial plant machine learning operational data combines an abnormality score of the abnormality detection model, which is generated from the plurality of abnormality detection models by a weighted linear sum.
8. The system as claimed in claim 1, wherein the industrial plant machine learning operational data is evaluated by using sampling based on a random distribution, sampling using an interpolation value or an estimated value, or learning a generation model from the operational data and from the plurality of abnormality detection models.
9. The system as claimed in claim 1, wherein the processing unit with the memory unit can also be further resided in the computation server and communicatively coupled to the end-terminals, i.e., one or more computing devices through internet connectivity and using compatible interfaces for the industrial plant machine learning system.
, Description:[001] The present invention relates to the field of the hierarchical machine learning for an industrial plant machine learning system, an abnormality detection system, an abnormality detection method, an abnormality detection program, and a method for generating a learned model. The invention more particularly relates to a method of hierarchical machine learning for an industrial plant machine learning system.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Conventionally, identification of failure determination and failure cause of a machine in an industrial plant has been carried out by comparing various sensed data at the time of normal operation of the machine with sensor data at the time of failure in the past. Due to these kinds of issues, in order to accurately and efficiently compare sensor data, failure cause can be classified highly accurately and efficiently by accurately and efficiently selecting a sensor or through a machine learning which can transmit trained and learned data with the sensed data.
[004] Related background art citation: patent literature 1: JP 2012-098901 A
With regard to the technical problem being solved in cited-art, machines with advanced control today are controlled and managed by many sensors. For example, but not limited to, in automobiles, more than 100 types of sensors are installed per vehicle. In the case of new machines and unknown failures, even those with an advanced expertise and abundant experience could not deal with such case. Therefore, it is required to realize a method or a system that efficiently selects a sensor without requiring advanced expertise or abundant experience by using the hierarchical machine learning for an industrial plant machine learning system, an abnormality detection system.
[005] What is the need of this time a method which can eliminate the curse of dimensionality makes machine learning models very prone towards overfitting on high-dimensional input data. Further, one has to select between manual feature selection and engineering or the requirements of a very large number of training data. This invention provides a solution such a method, and a related system for implementing it.
[006] Considering the above drawbacks, accordingly, there remains a need in the prior art for a technical convergence to make an intelligent machine learning system, interfaces and method, it is in this context that the present invention provides a method of hierarchical machine learning for an industrial plant machine learning system, which provides an industrial automation and data processing system that can calibrate the conventional automation and manufacturing process by using the hierarchical machine learning interface. Therefore, it would be useful and desirable to have a system and interface to meet the above-mentioned needs.
SUMMARY OF THE PRESENT INVENTION
[007] In view of the foregoing disadvantages inherent in the known types of conventional industrial plant machine learning system, method and devices, are now present in the prior art, the present invention provides a system and method for hierarchical machine learning for an industrial plant machine learning system. The system is designed with, but not limited to, at least two set equipment implementation phase, in which the first set of equipment is the physical placement of the equipment, machines connected to the computing system / mainframes using a real-time communication module, which is further connected coupled with the second set of software implementation with the help of a processing unit provided a machine learning and an Artificial Intelligence trained embedded software and algorithm, which has all the advantages of the prior art and none of the disadvantages.
[008] The main aspect of the present invention is to provide a topology model, includes, but not limited to, structural information on hierarchical relations between components of the industrial plant is received by a machine learning system. The components comprise, but not limited to, data signals from a plurality of sensors of the industrial plant and hierarchical units, wherein the hierarchical units comprise, but not limited to, assets, plant sub-units, plant units and plant sections of the industrial plant.
[009] Another aspect of the present invention is to provide a system, in order to detect sensor data from the plurality of sensors of the industrial plant, it is necessary to design an abnormality score function for each sensor data and each application. Therefore, it is used to be necessary to evaluate through hierarchical machine learning an abnormality score function each time sensor data and the number of application and interface increased and eliminate the fear that information important for abnormality detection is missed in feature design of the industrial plant machine learning system.
[010] The proposed system and method is implemented on, but not limited to, the Field Programmable Gate Arrays (FPGAs) and the like, PC, Microcontroller and with other known processors to have computer algorithms and instruction up gradation for supporting many applications domain where the aforesaid problems to solution is required.
[011] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[014] FIG. 1 illustrates a schematic diagram of a method for hierarchical machine learning for an industrial plant machine learning system, in accordance with an embodiment of the present invention; and
[015] FIG. 2 illustrates a block diagram of the method for hierarchical machine learning for an industrial plant machine learning system, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[016] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[017] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[018] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[019] Referring now to the drawings, these are illustrated in FIG. 1-2, the present invention discloses a method for hierarchical machine learning for an industrial plant machine learning system. The method and system are comprised of, but not limited to, a memory device for storing a latent variable machine learning model and a joint probability model that are previously learned from the industrial plant; an acquisition module configured to acquire an input data of an abnormality detection target in machine learning system;
a data encryption unit for inferring a latent variable machine learning model from the input data based on the latent variable machine learning model stored in the memory device; a decryption unit for generating restored data from the latent variable machine learning model based on the joint probability model stored in the memory device; and a hierarchical machine learning determination unit designed to determine whether the input data is normal or abnormal for the provided set of the latent variable machine learning model, which is based on a deviation between the input data and the restored data.
[020] In accordance with another embodiment of the present invention, the determination unit is implemented through a processing unit to determine an amount of the deviation between the input data and the restored data based on probability calculated for the provided set of the latent variable machine learning model according to parameters obtained in processes at the data encryption unit and decryption unit.
[021] In accordance with another embodiment of the present invention, the processing unit is configured to perform the inferring and generating by the data encryption unit and decryption unit, which includes, but not limited to, a Variational Auto Encoder, Adversarial Auto Encoder, Ladder Variational Auto Encoder, and Auxiliary Deep Generative Model.
[022] In accordance with another embodiment of the present invention, the processing unit is further configured to a discriminator that inputs he feature quantity generated by the data encryption unit of the self-encoder and generates an estimated label, the label corresponding to the input data and the estimated label are matched through a classifier for the industrial plant machine learning system.
[023] In accordance with another embodiment of the present invention, the classifier is designed to provide a computer perform the process which makes the data encryption unit to learn and trained so that the label corresponding to the input data and the estimation label can leave and separate using the discriminator.
[024] In accordance with another embodiment of the present invention, the processing unit is further connected with a plurality of means for learning an abnormality in the hierarchical machine leaning model from the operation data sets an initial abnormality hierarchical data detection model, and sequentially reconstructs the hierarchical machine leaning model with a small variation in the abnormality score from the abnormality detection model.
[025] In accordance with another embodiment of the present invention, the plurality of means for learning the abnormality detection model from the industrial plant machine learning operational data combines an abnormality score of the abnormality detection model, which is generated from the plurality of abnormality detection models by a weighted linear sum.
[026] In accordance with another embodiment of the present invention, the industrial plant machine learning operational data is evaluated by using sampling based on a random distribution, sampling using an interpolation value or an estimated value, or learning a generation model from the operational data and from the plurality of abnormality detection models.
[027] Further, various exemplary computer system for implementing embodiments consistent with the present disclosure. Variations of computer system may be used for implementing the method of hierarchical machine learning for an industrial plant machine learning system. Computer system may comprise a central processing unit (“CPU” or “processor”). Processor may comprise at least one data processor for executing program components for executing user or system-generated requests. A user may include a person, a person using a device such as such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.
[028] Processor may be disposed in communication with one or more input/output (I/O) devices via I/O interfaces. The I/O interfaces may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.
[029] In some embodiments, the processor may be disposed in communication with one or more memory devices (e.g., RAM, ROM, etc.) via a storage interface. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. The memory devices may store a collection of program or database components, including, without limitation, an operating system, user interface application, web browser, mail server, mail client, user/application data (e.g., any data variables or data records discussed in this disclosure), etc. The operating system may facilitate resource management and operation of the computer system. Examples of operating systems include, without limitation, Apple Macintosh OS X, Unix, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.
[030] The word “module,” “model” “algorithms” and the like as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, Python or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of computer-readable medium or other computer storage device. Further, in various embodiments, the processor is one of, but not limited to, a general-purpose processor, an application specific integrated circuit (ASIC) and a field-programmable gate array (FPGA) processor. Furthermore, the data repository may be a cloud-based storage or a hard disk drive (HDD), Solid state drive (SSD), flash drive, ROM or any other data storage means.
[031] The above-mentioned system is having various novel aspects such as, but not limited to, the processing unit with the machine learning interface for providing a method for hierarchical machine learning for an industrial plant machine learning system by the present invention and which will be understood by reading and studying the aforesaid embodiments, and further, the system is described which can also be applied with at most slight modification to provide the hierarchical machine learning based data modelling with the same advantages described above.
[032] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[033] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[034] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.