CN113496332B - Industrial Internet fault prediction method and system - Google Patents
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Abstract
The invention discloses an industrial Internet fault prediction method and system, and relates to the technical field of networks. The method comprises the following steps: collecting industrial Internet data; extracting industrial Internet data, identifying an entity in the industrial Internet data, and taking the entity as a node in a knowledge graph; and calculating the association strength index between the nodes, so as to generate an industrial Internet security risk knowledge graph, and performing fault prediction according to the knowledge graph. According to the method and the device, the industrial Internet security risk knowledge graph is constructed, a security decision basis can be provided for predictive maintenance of the industrial Internet, and further the accuracy of fault prediction can be improved, and fault maintenance can be timely carried out, so that stable operation of an industrial production system is ensured.
Description
Technical Field
The disclosure relates to the field of network technology, and in particular relates to an industrial internet fault prediction method and system.
Background
The production equipment of the traditional industry is mainly mechanical equipment, and physical and functional safety is focused. However, the level of digitization, informatization, networking and intellectualization of production equipment in the industrial Internet is continuously improved; the man-machine interaction process in the production link gradually reduces or even disappears, and the production environment often causes potential safety hazards to be difficult to detect, so that safety accidents in the industrial production process are caused. The quality defect of production equipment, the state such as material ageing are all continuous change, and the chain reaction that these faults arouse can lead to the whole production process to go on normally, therefore, can predict the system trouble in time, reduce the security risk of system shutdown and be very important.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to provide an industrial internet fault prediction method and system, which can improve the accuracy of fault prediction.
According to an aspect of the present disclosure, an industrial internet fault prediction method is provided, including: collecting industrial Internet data; extracting industrial Internet data, identifying an entity in the industrial Internet data, and taking the entity as a node in a knowledge graph; and calculating the association strength index between the nodes, so as to generate an industrial Internet security risk knowledge graph, and performing fault prediction according to the knowledge graph.
In some embodiments, performing association algorithm calculation on the entities, mining frequently occurring entity sets in the entities, and taking the entity sets as nodes in the knowledge graph.
In some embodiments, the entity comprises: fault equipment, core production systems, and fault checkpoints.
In some embodiments, calculating the association strength index between nodes includes: calculating an association strength index between the fault equipment and the core production system; and calculating an association strength index between the core production system and the fault checkpoint.
In some embodiments, the greater the strength of association index between the failed device and the core production system, the greater the impact of the device failure on the core production system; the greater the correlation strength index between the core production system and the fault checkpoint, the greater the probability that the core production system fails and requires fault checking.
In some embodiments, calculating the association strength indicator between the malfunctioning device and the core production system includes: calculating the probability of failure equipment causing the failure of the core production system; and calculating an association strength indicator between the core production system and the fault checkpoint comprises: the probability of performing a single point failure check when the core production system is down is calculated.
In some embodiments, the formula is based onCalculating probability P (d) of failure equipment causing failure of core production system i /s j ) Wherein Σd i s j Number of records indicating equipment failure or core production system failure caused by different operation states, sigma D indicating total number of equipment, sigma s j The method is characterized in that the method comprises the steps of representing the number of faults of a core production system, f (x, y) represents the time of the previous fault repair of equipment and the adjustment parameter of the repair condition on the fault probability, x represents the time of the previous fault occurrence of the equipment, and y represents the previous fault repair condition of the equipment.
In some embodiments, the formula is based onComputing core production systemProbability of performing a single point failure check when a system fails P (s j /c k ) Wherein, sigma s j c k Representing the number of single point failure checks by failure of the core production system, Σc representing the total number of single point failure checks, Σs j The method is characterized in that the method comprises the steps of representing the number of faults of a core production system, f (m, n) represents the time of the previous fault repair of the core production system and the adjustment parameter of the repair success rate to the inspection probability, m represents the time of the previous fault repair of the core production system, and n represents the repair success rate of a fault point.
In some embodiments, the industrial internet data includes one or more of machine operation data, production environment data, information system data, manufacturing execution system data, and control system data.
According to another aspect of the present disclosure, there is also provided an industrial internet fault prediction system, including: the data acquisition unit is configured to acquire industrial Internet data; the entity extraction unit is configured to extract the industrial Internet data, identify the entity in the industrial Internet data and take the entity as a node in the knowledge graph; a correlation calculation unit configured to calculate a correlation strength index between nodes; and a knowledge graph generation unit configured to generate an industrial Internet security risk knowledge graph according to the nodes and the correlation strength indexes among the nodes so as to predict faults according to the knowledge graph.
According to another aspect of the present disclosure, there is also provided an industrial internet fault prediction system, including: a memory; and a processor coupled to the memory, the processor configured to perform the industrial internet fault prediction method as described above based on instructions stored in the memory.
According to another aspect of the disclosure, a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the industrial internet fault prediction method described above is also presented.
In the embodiment of the disclosure, the industrial internet data is extracted, the entity in the industrial internet data is identified, the entity is used as the node in the knowledge graph, the correlation strength index between the nodes is calculated, so that the industrial internet security risk knowledge graph is generated, a security decision basis can be provided for predictive maintenance of the industrial internet, further the accuracy of fault prediction can be improved, and the fault maintenance can be performed in time, so that the stable operation of an industrial production system is ensured.
Other features of the present disclosure and its advantages will become apparent from the following detailed description of exemplary embodiments of the disclosure, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow diagram of some embodiments of an industrial Internet failure prediction method of the present disclosure.
FIG. 2 is a flow diagram of further embodiments of an industrial Internet failure prediction method of the present disclosure.
FIG. 3 is a schematic diagram of the architecture of some embodiments of the industrial Internet failure prediction system of the present disclosure.
FIG. 4 is a schematic diagram of other embodiments of an industrial Internet failure prediction system of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
FIG. 1 is a flow diagram of some embodiments of an industrial Internet failure prediction method of the present disclosure.
At step 110, industrial Internet data is collected.
In some embodiments, machine operation data, production environment data, information system data, manufacturing execution system data, control system data, and the like in the industrial internet are collected and consolidated.
In step 120, the industrial internet data is extracted, the entity in the industrial internet data is identified, and the entity is used as a node in the knowledge graph.
In some embodiments, statistical machine learning methods may be employed to identify entities from industrial Internet data using trained models. Entities include, for example, faulty devices, core production systems, and fault checkpoints, i.e., devices, core production systems, and fault checkpoints that have failed are extracted from industrial internet data.
At step 130, an association strength index between nodes is calculated.
In some embodiments, when an industrial internet device fails, resulting in a shutdown of the core production system, a related failure check needs to be performed, and therefore, a correlation strength index between the failed device and the core production system needs to be calculated, and a correlation strength index between the core production system and a failure checkpoint needs to be calculated. The larger the correlation strength index between the fault equipment and the core production system is, the larger the influence of the equipment fault on the core production system is; the greater the correlation strength index between the core production system and the fault checkpoint, the greater the probability that the core production system will fail requiring a fault check.
In step 140, an industrial internet security risk knowledge graph is generated according to the nodes and the correlation strength indexes between the nodes, so as to predict faults according to the knowledge graph.
In some embodiments, a knowledge graph describes the association between two entities by describing the various entities and concepts that exist in the real world, as well as the strong relationships between them. The entity, the entity relation and the entity form the triples in the knowledge graph, wherein the entity can be used as a node, and the entity relation is regarded as an edge, so that the knowledge base containing a large number of triples can be formed into the knowledge graph.
In some embodiments, the constructed industrial Internet security risk knowledge graph is stored in a graph database, so that visual display is provided for industrial Internet security risk, and a security decision basis is provided for predictive maintenance of the industrial Internet.
In the embodiment, the industrial internet data are extracted, the entity in the industrial internet data is identified, the entity is used as the node in the knowledge graph, and the correlation strength index between the nodes is calculated, so that the industrial internet security risk knowledge graph is generated, a security decision basis can be provided for predictive maintenance of the industrial internet, further the accuracy of fault prediction can be improved, and the fault maintenance can be performed in time, so that the stable operation of an industrial production system is ensured.
FIG. 2 is a flow diagram of further embodiments of an industrial Internet failure prediction method of the present disclosure.
At step 210, machine operation data, production environment data, information system data, manufacturing execution system data, and control system data in the industrial Internet are collected.
In some embodiments, after the industrial internet data is collected, the data is pre-processed, e.g., cleaned, converted, correlated, missing value processed, etc.
At step 220, the industrial Internet data is extracted, and entities in the industrial Internet data are identified.
The running state of the equipment comprises vibration, deformation, displacement, temperature, rotating speed, pressure, current and the like, and the fault degree, the fault property and the fault type of the core production system are identified and diagnosed according to the change of the state. For example, when the fault equipment is identified, the collected industrial Internet signals are analyzed, and the positions, types, reasons and degrees of the fault of the identification equipment can be judged by combining the data sources, so that the development trend of the performance of the equipment can be predicted. In some embodiments, by a machine learning method, when training a model, fault threshold values of vibration, deformation, displacement, temperature, rotation speed, pressure, current and the like are set, so that a fault device is identified in the industrial internet.
In some embodiments, core production systems, fault checkpoints, etc. may be extracted from the industrial internet data by way of statistical analysis.
At step 230, an associative algorithm is performed on the entities, mining the entity sets that frequently occur in the entities.
In some embodiments, taking a failed device as an example, some faults occur on a single device, some faults may be able to occur on an associated plurality of devices at the same time. A single device is an entity and multiple associated devices are a group or collection of entities. The association algorithm calculation is to perform frequent set mining on entities such as fault equipment, a core production system, a fault check point and the like by using the association algorithm to find out frequently occurring entity sets of the entities, such as: a multi-point failure check group.
In some embodiments, the association algorithm is, for example, an Apriori association analysis algorithm, and searches the data set for frequent item sets and association rules between data, so that the entity set can be identified.
In step 240, the entity and the entity set are used as nodes of the knowledge-graph.
At step 250, an association strength indicator between the failed device and the core production system, and an association strength indicator between the core production system and the failure checkpoint is calculated.
In some embodiments, calculating the association strength indicator between the malfunctioning device and the core production system includes: the probability that the failed device caused the core production system to fail is calculated.
For example, according to the formulaCalculating probability P (d) of failure equipment causing failure of core production system i /s j ) Wherein Σd i s j Number of records indicating equipment failure or core production system failure caused by different operation states, sigma D indicating total number of equipment, sigma s j The method is characterized in that the method comprises the steps of representing the number of faults of a core production system, f (x, y) represents the time of the previous fault repair of equipment and the adjustment parameter of the repair condition on the fault probability, x represents the time of the previous fault occurrence of the equipment, and y represents the previous fault repair condition of the equipment.
In some embodiments, calculating the correlation strength indicator between the core production system and the fault checkpoint includes: the probability of performing a single point failure check when the core production system fails is calculated.
For example, according to the formulaCalculating probability P(s) of performing a single point failure check when a core production system fails j /c k ) Wherein, sigma s j c k Representing the number of single point failure checks by failure of the core production system, Σc representing the total number of single point failure checks, Σs j The number of faults of the core production system is represented, f (m, n) represents the time of the previous fault repair of the core production system and the adjustment parameter of the repair success rate to the inspection probability, and m represents the time of the previous fault repair of the core production systemAnd n represents the success rate of fault point repair.
In some embodiments, when a core production system fails, a single point check is performed more often, indicating that this failure checkpoint is more relevant to the core production system failure. In addition, if the success rate of repairing the system fault through the fault check point is higher, the correlation between the fault check point and the core production system fault can be also indicated to be higher.
In some embodiments, a high compactness between the failed device and the core production system indicates that the failure of the device has a large impact on the core production system; a high tightness between the core production system and the fault check point means that the probability of fault check is high after the core production system fails.
In step 260, an industrial internet security risk knowledge graph is generated according to the nodes and the association strength index between the nodes.
In step 270, the generated industrial internet security risk knowledge graph is stored in a graph database.
In step 280, a fault is predicted from the knowledge-graph.
Related fault checks are required when the core production system is shut down due to the failure of industrial internet equipment. Therefore, according to the constructed knowledge graph, the correlation analysis is carried out on the knowledge nodes such as the operation equipment, the core production system, the fault check points and the like, so that the safety risk analysis, the fault discovery, the equipment detection maintenance and the like can be carried out on the industrial Internet in time.
In the embodiment, knowledge acquired from the industrial Internet is utilized to construct a knowledge graph, and knowledge node association analysis such as equipment, an operation system and targeted safety detection is carried out to judge which relevant fault detection is needed when the system faults occur, so that the efficiency of predictive maintenance of the industrial Internet, the timeliness of fault discovery and the success rate of fault maintenance are improved, and the stable operation of an industrial production system is ensured.
FIG. 3 is a schematic diagram of the architecture of some embodiments of the industrial Internet failure prediction system of the present disclosure. The system comprises a data acquisition unit 310, an entity extraction unit 320, a correlation calculation unit 330 and a knowledge-graph generation unit 340.
The data acquisition unit 310 is configured to acquire industrial internet data.
Industrial internet data includes, for example, machine operation data, production environment data, information system data, manufacturing execution system data, control system data, and the like.
The entity extraction unit 320 is configured to extract the industrial internet data, identify an entity in the industrial internet data, and use the entity as a node in the knowledge graph.
In some embodiments, statistical machine learning methods may be employed to identify entities from industrial Internet data using trained models. The entities include, for example, faulty equipment, core production systems, and fault checkpoints.
In some embodiments, the entity extraction unit 320 is further configured to perform association algorithm calculation on the entities, mine a set of frequently occurring entities in the entities, and use the set of entities as nodes in the knowledge graph. The set of entities is, for example, a plurality of associated devices, a multi-point failure check, or the like.
The association calculation unit 330 is configured to calculate an association strength index between nodes.
In some embodiments, the association calculation unit 330 is configured to calculate an association strength indicator between the failed device and the core production system, and to calculate an association strength indicator between the core production system and the failure checkpoint. The larger the correlation strength index between the fault equipment and the core production system is, the larger the influence of the equipment fault on the core production system is; the greater the correlation strength index between the core production system and the fault checkpoint, the greater the probability that the core production system fails and requires fault checking.
In some embodiments, the association calculation unit 330 is configured to calculate a probability that a failed device results in a failure of the core production system.
For example, according to the formulaCalculating probability P (d) of failure equipment causing failure of core production system i /s j ) Wherein Σd i s j Number of records indicating equipment failure or core production system failure caused by different operation states, sigma D indicating total number of equipment, sigma s j The method is characterized in that the method comprises the steps of representing the number of faults of a core production system, f (x, y) represents the time of the previous fault repair of equipment and the adjustment parameter of the repair condition on the fault probability, x represents the time of the previous fault occurrence of the equipment, and y represents the previous fault repair condition of the equipment.
The running state of the equipment comprises vibration, deformation, displacement, temperature, rotating speed, pressure, current and the like, and the fault degree, the fault property and the fault type of the core production system are identified and diagnosed according to the change of the state. The running state of the device can be used as an attribute of an entity in the knowledge graph.
In some embodiments, the association computation unit 330 is configured to compute the probability of performing a single point failure check when the core production system fails.
For example, according to the formulaCalculating probability P(s) of performing a single point failure check when a core production system fails j /c k ) Wherein, sigma s j c k Representing the number of single point failure checks by failure of the core production system, Σc representing the total number of single point failure checks, Σs j The method is characterized in that the method comprises the steps of representing the number of faults of a core production system, f (m, n) represents the time of the previous fault repair of the core production system and the adjustment parameter of the repair success rate to the inspection probability, m represents the time of the previous fault repair of the core production system, and n represents the failure point repair success rate.
The knowledge-graph generation unit 340 is configured to generate an industrial internet security risk knowledge graph according to the nodes and the correlation strength index between the nodes, so as to perform fault prediction according to the knowledge graph.
In some embodiments, the constructed industrial Internet security risk knowledge graph is stored in a graph database, so that visual display is provided for industrial Internet security risk, and a security decision basis is provided for predictive maintenance of the industrial Internet.
In the embodiment, the industrial internet data is extracted, the entity in the industrial internet data is identified, the entity is used as the node in the knowledge graph, and the correlation strength index between the nodes is calculated, so that the industrial internet security risk knowledge graph is generated, a security decision basis can be provided for predictive maintenance of the industrial internet, and stable operation of an industrial production system is ensured.
FIG. 4 is a schematic diagram of other embodiments of an industrial Internet failure prediction system of the present disclosure. The system 400 includes a memory 410 and a processor 420. Wherein: memory 410 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is used to store instructions in the corresponding embodiments of fig. 1-2. Processor 420, coupled to memory 410, may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 420 is configured to execute instructions stored in the memory.
In some embodiments, processor 420 is coupled to memory 410 through BUS 430. The system 400 may also be connected to an external storage system 450 via a storage interface 440 for invoking external data, and may also be connected to a network or another computer system (not shown) via a network interface 460. And will not be described in detail herein.
In this embodiment, the data instruction is stored in the memory, and then the processor processes the instruction, so that the accuracy of fault prediction can be improved, and stable operation of the industrial production system can be ensured.
In other embodiments, a computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of the corresponding embodiment of fig. 1-2. It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (7)
1. An industrial internet fault prediction method, comprising:
collecting industrial Internet data;
extracting the industrial Internet data, identifying an entity in the industrial Internet data, and taking the entity as a node in a knowledge graph, wherein the entity comprises fault equipment, a core production system and a fault check point; and
calculating the association strength index between the nodes so as to generate an industrial Internet security risk knowledge graph, so as to predict faults according to the knowledge graph, wherein calculating the association strength index between the nodes comprises the following steps:
according to the formulaCalculating a probability P (d) that the failed device causes the core production system to fail i /s j ) According to the formula->Calculating probability P(s) of performing a single point failure check when the core production system fails j /c k ) Wherein, the method comprises the steps of, wherein,
∑d i s j a record number representing the failure of the equipment or the failure of the core production system caused by different operation states, sigma D representing the total number of the equipment, sigma s j Representing the number of faults of the core production system, and f (x, y) represents the time and time of the previous fault repair of the equipmentThe condition is to the parameter of adjustment of the fault probability, x represents the time of occurrence of the previous fault of the equipment, y represents the condition of restoration of the previous fault of the equipment, and sigma s j c k The method comprises the steps of representing the number of times that the core production system breaks down to perform single-point fault detection, wherein Sigma C represents the total number of single-point fault detection, f (m, n) represents the previous fault repair time of the core production system and the adjustment parameter of the repair success rate to the detection probability, m represents the previous fault repair time of the core production system, and n represents the fault point repair success rate.
2. The industrial internet fault prediction method of claim 1, further comprising:
and carrying out association algorithm calculation on the entities, mining frequently-occurring entity sets in the entities, and taking the entity sets as nodes in the knowledge graph.
3. The industrial Internet failure prediction method according to claim 1, wherein,
the larger the correlation strength index between the fault equipment and the core production system is, the larger the influence of equipment faults on the core production system is; and
the greater the correlation strength index between the core production system and the fault check point, the greater the probability that the core production system fails and needs to be subjected to fault check.
4. The industrial Internet failure prediction method according to any one of claim 1 to 3, wherein,
the industrial internet data includes one or more of machine operation data, production environment data, information system data, manufacturing execution system data, and control system data.
5. An industrial internet fault prediction system, comprising:
the data acquisition unit is configured to acquire industrial Internet data;
an entity extraction unit configured to extract the industrial internet data, identify an entity in the industrial internet data, and take the entity as a node in a knowledge graph, wherein the entity comprises fault equipment, a core production system and a fault check point;
an association calculation unit configured to calculate an association strength index between nodes, comprising: according to the formulaCalculating a probability P (d) that the failed device causes the core production system to fail i /s j ) According to the formula->Calculating probability P(s) of performing a single point failure check when the core production system fails j /c k ) Wherein Σd i s j A record number representing the failure of the equipment or the failure of the core production system caused by different operation states, sigma D representing the total number of the equipment, sigma s j Representing the number of faults of the core production system, f (x, y) represents the time of the previous fault repair of the equipment and the adjustment parameter of the repair condition on the fault probability, x represents the time of the previous fault occurrence of the equipment, y represents the condition of the previous fault repair of the equipment, and sigma s j c k The method comprises the steps of representing the number of times that single-point fault detection is carried out when a core production system breaks down, wherein Sigma C represents the total number of single-point fault detection, f (m, n) represents the previous fault repair time of the core production system and the adjustment parameter of the repair success rate to the detection probability, m represents the previous fault repair time of the core production system, and n represents the fault point repair success rate; and
and the knowledge graph generation unit is configured to generate an industrial Internet security risk knowledge graph according to the nodes and the correlation strength indexes among the nodes so as to predict faults according to the knowledge graph.
6. An industrial internet fault prediction system, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the industrial internet fault prediction method of any of claims 1-4 based on instructions stored in the memory.
7. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the industrial internet fault prediction method of any one of claims 1 to 4.
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