CN111062625A - Method and device for establishing residual oil hydrogenation device failure prediction model - Google Patents
Method and device for establishing residual oil hydrogenation device failure prediction model Download PDFInfo
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Abstract
The invention discloses a method and a device for establishing a residual oil hydrogenation device failure prediction model, wherein the establishing method comprises the following steps: obtaining a failure database corresponding to a residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device; acquiring a data mining model and a failure factor analysis model corresponding to a residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device; and establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model. The invention can effectively establish a corresponding failure prediction model of the residual oil hydrogenation device.
Description
Technical Field
The invention relates to the technical field of big data prediction application, in particular to a method and a device for establishing a residual oil hydrogenation device failure prediction model.
Background
The data is a key factor for realizing long-period safe operation of the refining enterprise static equipment, and the safe operation of the static equipment can be effectively ensured by analyzing the full-life data set of the refining enterprise static equipment. At present, the static equipment of the refining and chemical enterprises is really a big data aggregate, the big data era has come, the mass data generated in the design, construction and operation processes of the static equipment is processed and analyzed by applying big data technology becomes necessary trend, and the big data management platform has wide application prospect in the petrochemical enterprises. In order to solve fragmentation and islanding of static equipment data and form uniform static equipment big data, a failure risk early warning model of the static equipment needs to be established so as to prompt the risk condition of the static equipment at the present stage by combining the failure risk early warning model of the static equipment. However, in the prior art, no mature method exists for how to analyze failure and corrosion factors of the static equipment and establish a static equipment failure risk early warning model, and how to establish the static equipment failure risk early warning model becomes a problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides a method and a device for establishing a residual oil hydrogenation device failure prediction model, which aim to solve the problem that no mature method exists for analyzing failure and corrosion factors of static equipment and establishing a static equipment failure risk early warning model in the prior art.
In order to solve the above technical problem, a first technical solution adopted in the embodiments of the present invention is as follows:
a method for establishing a residual oil hydrogenation device failure prediction model comprises the following steps: obtaining a failure database corresponding to the residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device; acquiring a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device; and establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
Optionally, the establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model includes: acquiring failure factors for establishing the failure prediction model; inputting the failure factor into a preset artificial neural network for training, and outputting a training failure mode corresponding to the failure factor.
Optionally, after outputting the training failure mode corresponding to the failure factor, the method includes: and judging whether the similarity between the training failure mode and the specified failure mode reaches a preset similarity value, wherein the specified failure mode is the failure mode corresponding to the historical corresponding relation.
Optionally, after determining whether the similarity between the training failure mode and the designated failure mode reaches a preset similarity value, the method includes: and if the similarity between the training failure mode and the designated failure mode reaches a preset similarity value, stopping training the preset artificial neural network, storing the network structure and parameter value of the preset artificial neural network at the moment, and taking the network structure and parameter value as the network structure and parameter value of the residual oil hydrogenation device failure prediction model.
Optionally, after determining whether the similarity between the training failure mode and the designated failure mode reaches a preset similarity value, the method includes: if the similarity between the training failure mode and the designated failure mode does not reach a preset similarity value, repeating the steps, continuously inputting the acquired failure factors into the preset artificial neural network for training, and outputting the training failure mode corresponding to the failure factors until the similarity between the training failure mode and the designated failure mode reaches the preset similarity value.
Optionally, the obtaining failure factors for establishing the failure prediction model includes: and acquiring failure factors for establishing the failure prediction model according to the failure database and the data mining model.
Optionally, the preset artificial neural network has completed setting before acquiring failure factors for establishing the failure prediction model.
In order to solve the above technical problem, a second technical solution adopted in the embodiments of the present invention is as follows:
a device for establishing a residual oil hydrogenation device failure prediction model comprises: the data acquisition module is used for acquiring a failure database corresponding to the residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device; the model acquisition module is used for acquiring a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device; and the model establishing module is used for establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
In order to solve the above technical problem, a third technical solution adopted in the embodiments of the present invention is as follows:
a computer readable storage medium having stored thereon a computer program which, when executed, implements the method of establishing a residual oil hydrogenation unit failure prediction model as described above.
In order to solve the above technical problem, a fourth technical solution adopted in the embodiments of the present invention is as follows:
a computer apparatus comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the method for establishing a residual oil hydrogenation unit failure prediction model as described above.
The embodiment of the invention has the beneficial effects that: different from the situation of the prior art, the embodiment of the invention establishes the failure prediction model corresponding to the residual oil hydrogenation device by acquiring the failure database corresponding to the residual oil hydrogenation device, acquiring the data mining model and the failure factor analysis model corresponding to the residual oil hydrogenation device and finally according to the failure database, the data mining model and the failure factor analysis model, thereby achieving the purpose of establishing the failure prediction model corresponding to the residual oil hydrogenation device by analyzing failure and corrosion factors of static equipment.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for establishing a residual oil hydrogenation unit failure prediction model according to a first embodiment of the present invention;
FIG. 2 is a partial structural framework diagram of an embodiment of a residual oil hydrogenation unit failure prediction model building device in example two of the present invention;
FIG. 3 is a partial structural framework diagram of an embodiment of a computer-readable storage medium according to a third embodiment of the present invention;
fig. 4 is a partial structural framework diagram of an embodiment of a computer device according to a fourth embodiment of the present invention.
Detailed Description
Example one
Referring to fig. 1, fig. 1 is a flowchart illustrating an implementation of a method for building a residual oil hydrogenation device failure prediction model according to an embodiment of the present invention, which can be obtained by combining fig. 1, and the method for building a residual oil hydrogenation device failure prediction model according to the present invention includes:
step S101: and acquiring a failure database corresponding to the residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device.
Step S102: and acquiring a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device.
Step S103: and establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
In this embodiment, optionally, the establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model includes:
acquiring failure factors for establishing the failure prediction model; inputting the failure factor into a preset artificial neural network for training, and outputting a training failure mode corresponding to the failure factor.
An Artificial Neural Network (ANNs) is an algorithmic mathematical model that simulates behavioral characteristics of animal Neural Networks and performs distributed parallel information processing. The network achieves the purpose of processing information by adjusting the mutual connection relationship among a large number of internal nodes depending on the complexity of the system, and has self-learning and self-adapting capabilities. A neural network is formed by a large number of nodes (or neurons) and their interconnections, each node representing a specific output function called an activation function. Every connection between two nodes represents a weighted value, called weight, for the signal passing through the connection, which is equivalent to the memory of the artificial neural network. The output of the network is different according to the connection mode of the network, the weight value and the excitation function, and the network itself is usually an approximation to a certain algorithm or function in nature, and may also be an expression of a logic strategy.
In this embodiment, optionally, after outputting the training failure mode corresponding to the failure factor, the method includes:
and judging whether the similarity between the training failure mode and the specified failure mode reaches a preset similarity value, wherein the specified failure mode is the failure mode corresponding to the historical corresponding relation.
In this embodiment, optionally, after determining whether the similarity between the training failure mode and the designated failure mode reaches the preset similarity value, the method includes:
and if the similarity between the training failure mode and the designated failure mode reaches a preset similarity value, stopping training the preset artificial neural network, storing the network structure and parameter value of the preset artificial neural network at the moment, and taking the network structure and parameter value as the network structure and parameter value of the residual oil hydrogenation device failure prediction model.
In this embodiment, optionally, after determining whether the similarity between the training failure mode and the designated failure mode reaches the preset similarity value, the method includes:
if the similarity between the training failure mode and the designated failure mode does not reach a preset similarity value, repeating the steps, continuously inputting the acquired failure factors into the preset artificial neural network for training, and outputting the training failure mode corresponding to the failure factors until the similarity between the training failure mode and the designated failure mode reaches the preset similarity value.
In this embodiment, optionally, the obtaining the failure factor for establishing the failure prediction model includes: and acquiring failure factors for establishing the failure prediction model according to the failure database and the data mining model.
In this embodiment, optionally, the preset artificial neural network is already set before acquiring the failure factor for establishing the failure prediction model.
According to the embodiment of the invention, the failure prediction model corresponding to the residual oil hydrogenation device is established by acquiring the failure database corresponding to the residual oil hydrogenation device, acquiring the data mining model and the failure factor analysis model corresponding to the residual oil hydrogenation device and finally according to the failure database, the data mining model and the failure factor analysis model, so that the purpose of establishing the failure prediction model corresponding to the residual oil hydrogenation device by analyzing failure and corrosion factors of static equipment is realized.
Example two
Referring to fig. 2, fig. 2 is a partial structural framework diagram of an apparatus for building a residual oil hydrogenation device failure prediction model according to an embodiment of the present invention, and as can be obtained by referring to fig. 2, an apparatus 100 for building a residual oil hydrogenation device failure prediction model according to an embodiment of the present invention includes:
a data obtaining module 110, configured to obtain a failure database corresponding to the residual oil hydrogenation device, where the failure database includes all failure data corresponding to the residual oil hydrogenation device.
The model obtaining module 120 is configured to obtain a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, where the data mining model includes historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model includes a historical correspondence between a failure mode and a failure factor corresponding to the residual oil hydrogenation device.
And the model establishing module 130 is configured to establish a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
According to the embodiment of the invention, the failure prediction model corresponding to the residual oil hydrogenation device is established by acquiring the failure database corresponding to the residual oil hydrogenation device, acquiring the data mining model and the failure factor analysis model corresponding to the residual oil hydrogenation device and finally according to the failure database, the data mining model and the failure factor analysis model, so that the purpose of establishing the failure prediction model corresponding to the residual oil hydrogenation device by analyzing failure and corrosion factors of static equipment is realized.
EXAMPLE III
Referring to fig. 3, a computer-readable storage medium 10 according to an embodiment of the present invention can be seen, where the computer-readable storage medium 10 includes: ROM/RAM, magnetic disk, optical disk, etc., on which a computer program 11 is stored, the computer program 11, when executed, implementing the method for establishing a residual oil hydrogenation apparatus failure prediction model according to the first embodiment. Since the method for establishing the residual oil hydrogenation unit failure prediction model is described in detail in the first embodiment, the description is not repeated here.
According to the method for establishing the failure prediction model of the residual oil hydrogenation device, which is realized by the embodiment of the invention, the failure prediction model corresponding to the residual oil hydrogenation device is established by acquiring the failure database corresponding to the residual oil hydrogenation device, acquiring the data mining model and the failure factor analysis model corresponding to the residual oil hydrogenation device and finally according to the failure database, the data mining model and the failure factor analysis model, so that the purpose of establishing the failure prediction model corresponding to the residual oil hydrogenation device by analyzing failure and corrosion factors of static equipment is realized.
Example four
Referring to fig. 4, a computer apparatus 20 according to an embodiment of the present invention includes a processor 21, a memory 22, and a computer program 221 stored in the memory 22 and executable on the processor 21, wherein the processor 21 executes the computer program 221 to implement the method for establishing the residual oil hydrogenation unit failure prediction model according to an embodiment. Since the method for establishing the residual oil hydrogenation unit failure prediction model is described in detail in the first embodiment, the description is not repeated here.
According to the method for establishing the failure prediction model of the residual oil hydrogenation device, which is realized by the embodiment of the invention, the failure prediction model corresponding to the residual oil hydrogenation device is established by acquiring the failure database corresponding to the residual oil hydrogenation device, acquiring the data mining model and the failure factor analysis model corresponding to the residual oil hydrogenation device and finally according to the failure database, the data mining model and the failure factor analysis model, so that the purpose of establishing the failure prediction model corresponding to the residual oil hydrogenation device by analyzing failure and corrosion factors of static equipment is realized.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for establishing a residual oil hydrogenation device failure prediction model is characterized by comprising the following steps:
obtaining a failure database corresponding to the residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device;
acquiring a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device;
and establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
2. The method for building a residual oil hydrogenation device failure prediction model according to claim 1, wherein the building a corresponding failure prediction model of the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model comprises:
acquiring failure factors for establishing the failure prediction model;
inputting the failure factor into a preset artificial neural network for training, and outputting a training failure mode corresponding to the failure factor.
3. The method for building a residue hydrogenation unit failure prediction model according to claim 2, wherein outputting a training failure mode corresponding to the failure factor comprises:
and judging whether the similarity between the training failure mode and the specified failure mode reaches a preset similarity value, wherein the specified failure mode is the failure mode corresponding to the historical corresponding relation.
4. The method for establishing a residue hydrogenation unit failure prediction model according to claim 3, wherein the step of determining whether the similarity between the training failure mode and the designated failure mode reaches a preset similarity value comprises the following steps:
and if the similarity between the training failure mode and the designated failure mode reaches a preset similarity value, stopping training the preset artificial neural network, storing the network structure and parameter value of the preset artificial neural network at the moment, and taking the network structure and parameter value as the network structure and parameter value of the residual oil hydrogenation device failure prediction model.
5. The method for establishing a residue hydrogenation unit failure prediction model according to claim 3, wherein the step of determining whether the similarity between the training failure mode and the designated failure mode reaches a preset similarity value comprises the following steps:
if the similarity between the training failure mode and the designated failure mode does not reach a preset similarity value, repeating the steps, continuously inputting the acquired failure factors into the preset artificial neural network for training, and outputting the training failure mode corresponding to the failure factors until the similarity between the training failure mode and the designated failure mode reaches the preset similarity value.
6. The method for building a residual oil hydrogenation unit failure prediction model according to claim 2, wherein the obtaining failure factors for building the failure prediction model comprises:
and acquiring failure factors for establishing the failure prediction model according to the failure database and the data mining model.
7. The method for building a residue hydrogenation unit failure prediction model according to claim 2, wherein the preset artificial neural network has been set before obtaining failure factors for building the failure prediction model.
8. A device for establishing a residual oil hydrogenation device failure prediction model is characterized by comprising:
the data acquisition module is used for acquiring a failure database corresponding to the residual oil hydrogenation device, wherein the failure database comprises all failure data corresponding to the residual oil hydrogenation device;
the model acquisition module is used for acquiring a data mining model and a failure factor analysis model corresponding to the residual oil hydrogenation device, wherein the data mining model comprises historical classification data corresponding to the residual oil hydrogenation device, and the failure factor analysis model comprises historical corresponding relations between failure modes and failure factors corresponding to the residual oil hydrogenation device;
and the model establishing module is used for establishing a failure prediction model corresponding to the residual oil hydrogenation device according to the failure database, the data mining model and the failure factor analysis model.
9. A computer-readable storage medium having stored thereon a computer program which, when executed, implements the method of establishing a residual oil hydrogenation apparatus failure prediction model according to any one of claims 1 to 7.
10. A computer device, comprising a processor, a memory, and a computer program stored in the memory and operable on the processor, wherein the processor executes the computer program to implement the method for establishing a residual oil hydrogenation device failure prediction model according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20140281713A1 (en) * | 2013-03-14 | 2014-09-18 | International Business Machines Corporation | Multi-stage failure analysis and prediction |
| CN105653791A (en) * | 2015-12-29 | 2016-06-08 | 中国石油天然气集团公司 | Data mining based corrosion failure prediction system for on-service oil pipe column |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US20140281713A1 (en) * | 2013-03-14 | 2014-09-18 | International Business Machines Corporation | Multi-stage failure analysis and prediction |
| CN105653791A (en) * | 2015-12-29 | 2016-06-08 | 中国石油天然气集团公司 | Data mining based corrosion failure prediction system for on-service oil pipe column |
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