Patent Description:
Instrument management systems can also diagnose faults to prevent an unmanaged problem. An instrument management system can monitor a number of measuring devices to ensure their values are within an expected range for the device whose operation it is taking measurements of, for example. The measuring devices can obtain real-time values during operation of the process it is controlling. This can include, for example, temperature readings, pressure readings, gas flow readings, and the like. Instrument management systems can also report a status of a device or process it controls. This can include, for example, that a device is: out of fiscal reading; out of range; has a device error; and others.

Determining the root cause of an error in a device is a complex and time-consuming task that requires deep knowledge and experience. Highly-skilled and trained technicians are usually required and are typically expensive and in short supply. What is needed is an instrument management that is capable of handling some portion of the faults in its controlled system without human intervention. This will not only make the system more cost-effective, but also reduce the downtime and increase the reliability of the entire system.

[<NUM>] An example of a currently used system can be found in <CIT>, which discloses an apparatus and method for alert management in instrumentation, that includes generating a set of alerts within a process control instrument, processing the set of alerts to compare the set of alerts to known combinations of alerts, determining if one of the known combinations of alerts matches the set of alerts based on the comparison of the set of alerts to the known combinations of alerts, and identifying a recommended action instruction based on the determination.

One embodiment is an instrument management device, which includes a monitor module for a first device, wherein the first device is configured to obtain measurement data from a second device, to compare the measurement data to a reference value, and to send a signal when the measurement data in comparison to the reference indicates an error condition, a recommendation module to receive the signal from the monitoring module, to analyze the signal, and to generate one or more recommendations associated with the error condition by using a cause and effect module, the cause and effect module comprising a data structure having at least one row and at least one column, the recommendation module accessing a data item from one of the rows or one of the columns of the cause and effect module, a ticket generation module to receive the one or more recommendations including the data item, to obtain a resolution of the error condition by altering a state of the second device, and to generate a data package associated with the resolution of the error condition, and a machine learning module configured to use the package to determine whether to update the cause and effect module, and if so altering the data item in one of the rows or one of the columns.

The invention provides for a system, which includes a monitoring system for a first device, wherein the first device is configured to obtain measurement data from a second device, to compare the measurement data to a reference value, and to send a signal when the measurement data in comparison to the reference indicates an error condition, a recommendation system to receive the signal from the monitoring system, to analyze the signal, and to generate one or more recommendations associated with the error condition by using a cause and effect table, the cause and effect table comprising a data structure having at least one row and at least one column, the recommendation system accessing a data item from one of the rows or one of the columns of the cause and effect table, a ticket generation system to receive the one or more recommendations including the data item, to obtain a resolution of the error condition by altering a state of the second device, and to generate a data package associated with the resolution of the error condition, and a machine learning system configured to use the package to determine whether to update the cause and effect table, and if so altering the data item in one of the rows or one of the columns.

A method according to the invention comprises obtaining measurement data from a device, comparing the measurement data to a reference value, receiving a signal when the measurement data in comparison to the reference value indicates an error condition, using a cause and effect table to generate one or more recommendations associated with the error condition by, the cause and effect table comprising a data structure having at least one row and at least one column, the step of using further comprising accessing a data item from one of the rows or one of the columns of the cause and effect table, obtaining a resolution of the error condition by altering a state of the device, generating a data package associated with the resolution of the error condition, and using the package to determine whether to update the cause and effect table, and if so altering the data item in one of the rows or one of the columns.

<FIG> illustrates an example autonomous instrument management system <NUM> according to this disclosure. As shown in <FIG>, the system <NUM> includes various components that facilitate production or processing of at least one product or other material. For instance, the system <NUM> is used here to facilitate control over components in one or multiple plants, shown in <FIG> as 101a, 101b, and 101n (referred to hereinafter as 101a-101n). Each plant 101a-101n represents one or more processing facilities (or one or more portions thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant 101a-101n may implement one or more processes and can individually or collectively be referred to as a process system. A process system generally represents any system or portion thereof configured to process one or more products or other materials in some manner.

In <FIG>, the system <NUM> is implemented using the Purdue model of process control. In the Purdue model, "Level <NUM>" may include one or more sensors 102a and one or more actuators 102b. The sensors 102a and actuators 102b represent components in a process system that may perform any of a wide variety of functions. For example, the sensors 102a could measure a wide variety of characteristics in the process system, such as temperature, pressure, volume, or flow rate and could include such instruments as ultrasonic flow meters, turbines, orifices, Coriolis, gas chromatographs, P&T transmitters, flow computers, and the like. Also, the actuators 102b could alter a wide variety of characteristics in the process system. The sensors 102a and actuators 102b could represent any other or additional components in any suitable process system. Each of the sensors 102a includes any suitable structure for measuring one or more characteristics in a process system. Each of the actuators 102b includes any suitable structure for operating on or affecting one or more conditions in a process system. The sensors and actuators may be generally referred to as field devices.

At least one network <NUM> is coupled to the sensors 102a and actuators 102b. The network <NUM> facilitates interaction with the sensors 102a and actuators 102b. For example, the network <NUM> could transport measurement data from the sensors 102a and provide control signals to the actuators 102b. The network <NUM> could represent any suitable network or combination of networks. As particular examples, the network <NUM> could represent an Ethernet network, an electrical signal network (such as a HART or FOUNDATION FIELDBUS network), a pneumatic control signal network, or any other or additional type(s) of network(s).

In the Purdue model, "Level <NUM>" may include one or more controllers <NUM>, which are coupled to the network <NUM>. Among other things, each controller <NUM> may use the measurements from one or more sensors 102a to control the operation of one or more actuators 102b. For example, a controller <NUM> could receive measurement data from one or more sensors 102a and use the measurement data to generate control signals for one or more actuators 102b. Multiple controllers <NUM> could also operate in redundant configurations, such as when one controller <NUM> operates as a primary controller while another controller <NUM> operates as a backup controller (which synchronizes with the primary controller and can take over for the primary controller in the event of a fault with the primary controller). Each controller <NUM> includes any suitable structure for interacting with one or more sensors 102a and controlling one or more actuators 102b. Each controller <NUM> could, for example, represent a multivariable controller, such as a Robust Multivariable Predictive Control Technology (RMPCT) controller or other type of controller implementing model predictive control (MPC) or other advanced predictive control (APC). As a particular example, each controller <NUM> could represent a computing device running a real-time operating system.

Two networks <NUM> are coupled to the controllers <NUM>. The networks <NUM> facilitate interaction with the controllers <NUM>, such as by transporting data to and from the controllers <NUM>. The networks <NUM> could represent any suitable networks or combination of networks. As particular examples, the networks <NUM> could represent a pair of Ethernet networks or a redundant pair of Ethernet networks, such as a FAULT TOLERANT ETHERNET (FTE) network from HONEYWELL INTERNATIONAL INC.

At least one switch/firewall <NUM> couples the networks <NUM> to two networks <NUM>. The switch/firewall <NUM> may transport traffic from one network to another. The switch/firewall <NUM> may also block traffic on one network from reaching another network. The switch/firewall <NUM> includes any suitable structure for providing communication between networks, such as a HONEYWELL CONTROL FIREWALL (CF9) device. The networks <NUM> could represent any suitable networks, such as a pair of Ethernet networks or an FTE network.

In the Purdue model, "Level <NUM>" may include one or more machine-level controllers <NUM> coupled to the networks <NUM>. The machine-level controllers <NUM> perform various functions to support the operation and control of the controllers <NUM>, sensors 102a, and actuators 102b, which could be associated with a particular piece of industrial equipment (such as a boiler or other machine). For example, the machine-level controllers <NUM> could log information collected or generated by the controllers <NUM>, such as measurement data from the sensors 102a or control signals for the actuators 102b. The machine-level controllers <NUM> could also execute applications that control the operation of the controllers <NUM>, thereby controlling the operation of the actuators 102b. In addition, the machine-level controllers <NUM> could provide secure access to the controllers <NUM>. Each of the machine-level controllers <NUM> includes any suitable structure for providing access to, control of, or operations related to a machine or other individual piece of equipment. Each of the machine-level controllers <NUM> could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different machine-level controllers <NUM> could be used to control different pieces of equipment in a process system (where each piece of equipment is associated with one or more controllers <NUM>, sensors 102a, and actuators 102b).

One or more operator stations <NUM> are coupled to the networks <NUM>. The operator stations <NUM> represent computing or communication devices providing user access to the machine-level controllers <NUM>, which could then provide user access to the controllers <NUM> (and possibly the sensors 102a and actuators 102b). As particular examples, the operator stations <NUM> could allow users to review the operational history of the sensors 102a and actuators 102b using information collected by the controllers <NUM> and/or the machine-level controllers <NUM>. The operator stations <NUM> could also allow the users to adjust the operation of the sensors 102a, actuators 102b, controllers <NUM>, or machine-level controllers <NUM>. In addition, the operator stations <NUM> could receive and display warnings, alerts, or other messages or displays generated by the controllers <NUM> or the machine-level controllers <NUM>. Each of the operator stations <NUM> includes any suitable structure for supporting user access and control of one or more components in the system <NUM>. Each of the operator stations <NUM> could, for example, represent a computing device running a MICROSOFT WINDOWS operating system.

At least one router/firewall <NUM> couples the networks <NUM> to two networks <NUM>. The router/firewall <NUM> includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The networks <NUM> could represent any suitable networks, such as a pair of Ethernet networks or an FTE network.

In the Purdue model, "Level <NUM>" may include one or more unit-level controllers <NUM> coupled to the networks <NUM>. Each unit-level controller <NUM> is typically associated with a unit in a process system, which represents a collection of different machines operating together to implement at least part of a process. The unit-level controllers <NUM> perform various functions to support the operation and control of components in the lower levels. For example, the unit-level controllers <NUM> could log information collected or generated by the components in the lower levels, execute applications that control the components in the lower levels, and provide secure access to the components in the lower levels. Each of the unit-level controllers <NUM> includes any suitable structure for providing access to, control of, or operations related to one or more machines or other pieces of equipment in a process unit. Each of the unit-level controllers <NUM> could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different unit-level controllers <NUM> could be used to control different units in a process system (where each unit is associated with one or more machine-level controllers <NUM>, controllers <NUM>, sensors 102a, and actuators 102b).

Access to the unit-level controllers <NUM> may be provided by one or more operator stations <NUM>. Each of the operator stations <NUM> includes any suitable structure for supporting user access and control of one or more components in the system <NUM>. Each of the operator stations <NUM> could, for example, represent a computing device running a MICROSOFT WINDOWS operating system.

In the Purdue model, "Level <NUM>" may include one or more plant-level controllers <NUM> coupled to the networks <NUM>. Each plant-level controller <NUM> is typically associated with one of the plants 101a-101n, which may include one or more process units that implement the same, similar, or different processes. The plant-level controllers <NUM> perform various functions to support the operation and control of components in the lower levels. As particular examples, the plant-level controller <NUM> could execute one or more manufacturing execution system (MES) applications, scheduling applications, or other or additional plant or process control applications. Each of the plant-level controllers <NUM> includes any suitable structure for providing access to, control of, or operations related to one or more process units in a process plant. Each of the plant-level controllers <NUM> could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system.

Access to the plant-level controllers <NUM> may be provided by one or more operator stations <NUM>. Each of the operator stations <NUM> includes any suitable structure for supporting user access and control of one or more components in the system <NUM>. Each of the operator stations <NUM> could, for example, represent a computing device running a MICROSOFT WINDOWS operating system.

At least one router/firewall <NUM> couples the networks <NUM> to one or more networks <NUM>. The router/firewall <NUM> includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The network <NUM> could represent any suitable network, such as an enterprise-wide Ethernet or other network or all or a portion of a larger network (such as the Internet).

In the Purdue model, "Level <NUM>" may include one or more enterprise-level controllers <NUM> coupled to the network <NUM>. Each enterprise-level controller <NUM> is typically able to perform planning operations for multiple plants 101a-101n and to control various aspects of the plants 101a-101n. The enterprise-level controllers <NUM> can also perform various functions to support the operation and control of components in the plants 101a-101n. As particular examples, the enterprise-level controller <NUM> could execute one or more order processing applications, enterprise resource planning (ERP) applications, advanced planning and scheduling (APS) applications, or any other or additional enterprise control applications. Each of the enterprise-level controllers <NUM> includes any suitable structure for providing access to, control of, or operations related to the control of one or more plants. Each of the enterprise-level controllers <NUM> could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. In this document, the term "enterprise" refers to an organization having one or more plants or other processing facilities to be managed. Note that if a single plant 101a is to be managed, the functionality of the enterprise-level controller <NUM> could be incorporated into the plant-level controller <NUM>.

Access to the enterprise-level controllers <NUM> may be provided by one or more operator stations <NUM>. Each of the operator stations <NUM> includes any suitable structure for supporting user access and control of one or more components in the system <NUM>. Each of the operator stations <NUM> could, for example, represent a computing device running a MICROSOFT WINDOWS operating system.

Various levels of the Purdue model can include other components, such as one or more databases. The database(s) associated with each level could store any suitable information associated with that level or one or more other levels of the system <NUM>. For example, a historian <NUM> can be coupled to the network <NUM>. The historian <NUM> could represent a component that stores various information about the system <NUM>. The historian <NUM> could, for instance, store information used during production scheduling and optimization. The historian <NUM> represents any suitable structure for storing and facilitating retrieval of information. Although shown as a single centralized component coupled to the network <NUM>, the historian <NUM> could be located elsewhere in the system <NUM>, or multiple historians could be distributed in different locations in the system <NUM>.

In particular embodiments, the various controllers and operator stations in <FIG> may represent computing devices. For example, each of the controllers could include one or more processing devices <NUM> and one or more memories <NUM> for storing instructions and data used, generated, or collected by the processing device(s) <NUM>. Each of the controllers could also include at least one network interface <NUM>, such as one or more Ethernet interfaces or wireless transceivers. Also, each of the operator stations could include one or more processing devices <NUM> and one or more memories <NUM> for storing instructions and data used, generated, or collected by the processing device(s) <NUM>. Each of the operator stations could also include at least one network interface <NUM>, such as one or more Ethernet interfaces or wireless transceivers.

<FIG> illustrates another example of an autonomous instrument management system. <FIG> includes a controller <NUM> which can be any suitable controller, such as those described with respect to <FIG> or others. In one example, the controller <NUM> is configured to monitor the state, condition, or other aspect of a device <NUM>. The device <NUM> can be any suitable device that can facilitate production or processing of at least one product or other material. In one example, this includes a gas-flow meter or an ultrasonic transducer. In another example, the controller <NUM> can use a head-end <NUM> to provide automated error-correction, service recommendations, and other functionality based on analytics provided by the head-end <NUM>. The device <NUM> can be any suitable device that has a microprocessor and is capable of storing bulk data and/or performing analytics on the bulk data. In one example, the device <NUM> is a measuring device, such as a volume and/or mass flow measuring device or meter.

In operation, the controller <NUM> monitors the device <NUM> using a monitor module <NUM>. The monitor module <NUM> continually receives measurement data from the device <NUM> and compares the measurement data to a reference value. This could be, for example, a volume flow meter reporting a volume measurement to the monitor module <NUM> and the monitor module comparing the volume measurement to measurement data that represents a normal operating range. The measurement data can be stored in a database <NUM>, or other suitable storage mechanism such as hardware, software, firmware, or any suitable combination of these. When the measurement data and the reference values indicate that the device <NUM> is operating out of range, or otherwise is in a state that requires some action, a signal <NUM> is sent to a recommendation module <NUM>. This could happen when an ultrasonic transducer of the device <NUM> has a reference value indicative of a reduced accuracy. Alternatively, this situation can occur when any generic sensor, instrument, or device is operating out of range when compared to an expected reference value.

The recommendation module <NUM> can access solution data <NUM>, where it is configured to provide a solution to the monitor module <NUM> and is capable of enabling the controller <NUM> to bring the device <NUM> back into range or otherwise restore its normal operating characteristics. A ticket generation module <NUM> can also be used, which is configured to perform a process that resolves the error condition that caused the origination of the ticket. The ticket generation module <NUM> can be configured to receive the signal <NUM> from the recommendation module <NUM>. In one example, the controller <NUM> encaplsulates the data associated with the error condition into a package and sends it to the head-end <NUM>. A package can be included on a per device basis and can also be sent with a pre-defined delay, which can allow the system to stabilize on the error situation before sending it to the ticket generation module <NUM>. For example, in the case of a short-lived disturbance that simply disappears, no package may need to be sent. It is also possible that multiple errors and warnings occur based on one error situation. (e.g., a broken sensor cable will result in many different errors), in which case the package could include the multiple errors, or multiple packages can be sent. In another example, the package includes detailed device information collected by the controller <NUM>, like name and serial number of the device, location, log (raw data) files, recommendation text, and more.

The head-end <NUM> is configured to receive the package and to perform machine learning using the package with a machine learning module <NUM>. A cause and effect module <NUM> can also be included in the head-end <NUM>. The cause and effect module <NUM> can have a table of other data structure that pairs a cause of a problem with a solution to the problem. By utilizing the machine learning module <NUM> continually over time, the cause and effect module <NUM> can be updated, for example as better solutions are learned to existing problems. In one example, the cause and effect module <NUM> is configured to have one or more of any active status, alarm, and warning messages for the device <NUM>. In this manner, the machine learning module <NUM> improves the cause and effect table <NUM> over time and this will in turn improve the performance of the controller <NUM>, the recommendation module <NUM> and can be used to improve the solution data <NUM>.

<FIG> illustrates another example of an autonomous instrument management system. In <FIG>, the operation of the head-end <NUM> is described in more detail. <FIG> includes the controller <NUM>, which is configured to monitor the state, condition, or other aspect of a device that can facilitate production or processing of at least one product or other material. The head-end <NUM> is configured to provide automated error-correction, service recommendations, and other functionality based on analytics provided by the head-end <NUM>. In operation, the controller <NUM> monitors the device by continually receiving measurement data from the device and comparing the measurement data to a reference value. The measurement data is not limited to a specific device this. Instead, it can be any industrial apparatus (measurement device), like a gas-flow meter, a gas chromatograph, a temperature sensor, a pressure sensor, am ultrasonic transducer, or any suitable general purpose measuring device.

The measurement data can be stored in a database <NUM>, or other suitable storage mechanism such as hardware, software, firmware, or any suitable combination of these.

When the measurement data and the reference values indicate that the device is operating out of range, or otherwise is in a state that requires some action, a ticket generation module <NUM> communicates with the head-end <NUM>. The head-end <NUM> is configured to receive the communication from the ticket generation module <NUM> and to generate a ticket, which can be processed and utilize a recommendation engine <NUM>. In one example, a new ticket number can be generated at the head-end <NUM>, which can include with the ticket the details that were encapsulated in the package sent from the controller <NUM>. The ticket number can be shared and stored at the head-end <NUM>, for example it can be used to reference the ticket as it is processed through a traditional ticket processing system and until the problem is solved and the ticket is closed.

The recommendation engine <NUM> is configured to provide autonomous advice on solving the error that is happening in the device using an analysis module <NUM>, shown here as device <NUM>. The recommendation engine <NUM> is also configured to return a detailed report to the ticket generation module <NUM> using a notification module <NUM>. The report can include, for example, all of the information relevant to the device like data log files, historical data, and recommendations. In one embodiment, the recommendation engine <NUM> can provide a most likely root cause and a most likely recommendations to solve this error situation back to the device. Both the most likely root cause and the most likely recommendations can be provided in clear text or other form to a user, device screen, or both. In another example, each recommendation has a calculated likelihood indicator between <NUM>% and <NUM>%. The recommendation with the highest likelihood, can be used by the recommendation engine <NUM> for the representation to the user, device screen, or elsewhere.

The communication from the head-end <NUM> can also be used by a machine learning module <NUM>. The machine learning module <NUM> can perform machine learning using a cause and effect matrix <NUM>, which can also be included in the head-end <NUM>. The cause and effect matrix <NUM> can have a table of other data structure that pairs a cause of a problem with a solution to the problem. By utilizing the machine learning module <NUM> continually over time, the cause and effect matrix <NUM> can be updated, for example as better solutions are learned to existing problems. In this manner, the machine learning module <NUM> improves the cause and effect matrix <NUM> over time and this will in turn improve the performance of the controller <NUM>, the recommendation module <NUM> and can be used to improve the solution data. In one example, automated feedback can be provided to the machine learning module <NUM> either from the ticket generation module <NUM> or another source. The automated feedback can include data associated with the actual field solutions for the device that can be used to update the cause and effect matrix <NUM>, the recommendation engine <NUM>, or both.

<FIG> is a flowchart that illustrates the operation of an autonomous instrument management system according to one embodiment. At step <NUM>, the system waits until there is an active error. When there is an active error, then at step <NUM>, recommendations are generated. This could use a cause and effect module, for example. Thereafter, at step <NUM>, a package is sent to a ticketing system. The package could include, for example, raw and/or historical data associated with the device that triggered the error state in step <NUM>. In one example, the ticketing system will resolve the device problem associated with the package in one of two manners: <NUM>. Autonomously adjusting of the device parameters; or <NUM>. Human interaction is required. The system will seek to resolve the problem at step <NUM> by either sending the error- and root cause description to the user for manual intervention or it will autonomously solve the problem by adjusting the device remotely.

At step <NUM>, the system determines if the error has been fixed. If not, there error resolution step repeats at step <NUM>. When the error condition is fixed, the ticketing system is updated at step <NUM> and a data package is sent to a recommendation module at step <NUM>. At step <NUM>, the recommendation engine analyzes the data package. At step <NUM>, the recommendation engine determines whether or not the cause and effect module needs to be updated. If not, the process ends. Otherwise, the cause and effect module is updated at step <NUM> and the process ends.

<FIG> is a flowchart that illustrates the operation of an autonomous instrument management system according to another embodiment. At step <NUM>, the system waits until there is a signal will be received. At step <NUM>, the signal is analyzed. This signal can be sent, for example, when an ultrasonic transducer of a device is detected to be operating with a reduced accuracy. At step <NUM>, the system determines whether there is an active error. If not, the system continues to wait at step <NUM>. When the error occurs at step <NUM>, the system determines whether a parameter change is possible at step <NUM>. When a parameter change is not possible, the system generates recommendations at step <NUM> (e.g., using a cause and effect table) and a data package is created and sent to a ticketing system, for example, at step <NUM>. Thereafter, the process ends.

When a parameter change is possible at step <NUM> a new parameter value can be calculated at step <NUM>. In one example the parameter is changed gradually in a step-wise manner as described in further detail with respect to Table <NUM>.

At step <NUM>, the system determines whether the current parameter value of the device is at a threshold where the system cannot increase it anymore. This could occur, for example, by comparing the max value in Table <NUM> for a given device and determining whether the current value is equal or greater to the max value. If the current parameter value at step <NUM> is not at a threshold where the system cannot increase it anymore, then the system updates the connected device at step <NUM>. At step <NUM>, the system determines whether the error is solved. If so, the process ends. Otherwise, the process repeats at step <NUM> and the system thereafter waits for an additional signal at step <NUM>.

Table <NUM>, and others, can be used to instruct the system on what values to use when doing a step-wise adjustment for a device. In another example, the cause and effect module includes weighting factors associated with each error or warning that originates from a device being monitored. Each factor can be rated on a scale from <NUM> to <NUM>, for example, with <NUM> being of no influence and <NUM> indicating highly important for the outcome of the cause and effect module. Each possible error cause can also have a weighing factor on every possible error and warning. Each error cause can be rated, for example, on a scale from -<NUM> to <NUM>, with <NUM> being of no influence, -<NUM> indicating high reduction of the impact and <NUM> indicating highly important for the outcome of the cause and effect module.

Claim 1:
A system comprising:
a monitoring system (<NUM>) configured, for a first device, wherein the first device is configured to obtain measurement data from a second device, to compare the measurement data to a reference value, and to send a signal when the measurement data in comparison to the reference indicates an error condition;
a recommendation system (<NUM>) configured to receive the signal from the monitoring system (<NUM>), to analyze the signal, and to generate one or more recommendations associated with the error condition by using a cause and effect table (<NUM>), the cause and effect table (<NUM>) comprising a data structure having at least one row and at least one column, the recommendation system (<NUM>) configured to access a data item from one of the rows or one of the columns of the cause and effect table (<NUM>);
a ticket generation system (<NUM>) configured to receive the one or more recommendations including the data item, to obtain a resolution of the error condition by altering a state of the second device, and to generate a data package associated with the resolution of the error condition; and
a machine learning system (<NUM>) configured to use the package to determine whether to update the cause and effect table (<NUM>), and if so altering the data item in one of the rows or one of the columns.