Systems and methods for predicting and managing tool assets
A scalable digital fleet management system may be leveraged by organizations with high-volume high-value assets. In such scenarios, predictive analytics for tool health enables decision-making in terms of planning, maintenance, end-of-life replacement, tool selection, etc. An end-to-end solution spans all the way from gathering live tool data to visual representations of tool health. Long-term fleet management can be accomplished through a consistent evaluation of the fleet performance profile. Predictive analysis can anticipate maintenance needs and resultant downtimes and help improve scheduling of procurement and distribution of the fleet. Framework deployment focuses on topics related to developing an end-to-end solution architecture, scaling, and deploying it on demand, and maintaining it going forward. Sustenance and algorithm development focuses on defining a risk index, developing machine learning algorithms for different tools, defining a single comparison metric, and deciding when to trigger automated re-training for each tool.
1 . A method, comprising:
receiving, via one or more computing devices, tool data comprising a maintenance history and a failure history for each tool of a plurality of tools;
iteratively generating, via the one or more computing devices, a tool risk index for each tool of the plurality of tools based on tool modeling using a tool model, the maintenance history, and the failure history, wherein the tool model includes Arrhenius equivalent hours as a function of an activation energy, a Boltzmann's constant, a reference temperature, and an operating temperature, wherein the tool risk index excludes a probability of failure for each tool of the plurality of tools, and the maintenance history and the failure history are combined to form a weighted tool data source;
comparing, via the one or more computing devices, tool risk indices of the plurality of tools to obtain a risk comparison based on a common metric, wherein the risk comparison comprises ranking a real-time tool health of the plurality of tools from high risk to low risk according to the tool risk indices;
determining, via the one or more computing devices, the probability of failure for each tool of the plurality of tools based only on the risk comparison of the tool risk indices;
outputting, via the one or more computing devices, a real-time visualization of the real-time tool health based on the probability of failure for each tool of the plurality of tools via a visualization tool;
predicting, via the one or more computing devices, a tool failure of at least one tool of the plurality of tools based on a trend over time of the probability of failure for the at least one tool;
identifying, via the one or more computing devices, the at least one tool of the plurality of tools having the predicted tool failure as needing a risk mitigation action;
selecting, via the one or more computing devices, the risk mitigation action comprising an overhaul or a retirement of the at least one tool of the plurality of tools;
automatically, via the one or more computing devices, taking the at least one tool of the plurality of tools offline to avoid an unscheduled downtime of oil and gas production caused by an actual tool failure; and
executing, via the one or more computing devices, at least a portion of the risk mitigation action comprising the overhaul or the retirement of the at least one tool of the plurality of tools based on the real-time tool health.
2 . The method of claim 1 , comprising receiving the tool data from a database comprising a data lake, and continuously receiving the tool data from the data lake through an ingestion/subscription service.
3 . The method of claim 1 , comprising generating optimized asset-to-operation mappings based on the tool risk indices to map the plurality of tools to corresponding field operations to maximize productivity and minimize the probability of failure.
4 . The method of claim 1 , wherein the tool model is a digital representation of a corresponding tool of the plurality of tools, wherein the Arrhenius equivalent hours is based on a first equation:
t spent =t operation ·AF
where AF is an acceleration factor based on a second equation:
AF=e Ea/h (1/ T ref −1/ T o )
where E a is the activation energy, h is the Boltzmann's constant, T ref is the reference temperature, and T o is the operating temperature.
5 . The method of claim 1 , comprising using tool data outside of a testing window to train the tool model using machine learning.
6 . The method of claim 1 , comprising validating the tool model through the tool modeling based on a model validation metric, wherein the model validation metric comprises a swap ratio, the plurality of tools is arranged in a plurality of pairs, and the swap ratio is a ratio of the tool risk index for each pair of the plurality of pairs relative to a total number of pairs.
7 . The method of claim 1 , comprising:
analyzing tool sensor data of the plurality of tools in real-time, wherein the tool data comprises the tool sensor data; and
determining the probability of failure comprises real-time predictions of the probability of failure for each tool of the plurality of tools.
8 . The method of claim 6 , comprising triggering tool model re-training based on a lower bound on a mean value and an upper bound on a variance value of the model validation metric.
9 . The method of claim 1 , comprising:
producing, by a tool failure prediction, an average failure trend or a peak failure trend; and
determining the probability of failure for each tool of the plurality of tools based on the average failure trend or the peak failure trend produced by the tool failure prediction.
10 . The method of claim 9 , comprising interpolating the probability of failure for each tool of the plurality of tools using linear or cubic spline interpolation to smooth the probability of failure and eliminate outliers.
11 . The method of claim 1 , wherein the plurality of tools comprises at least a thousand tools.
12 . The method of claim 1 , comprising:
generating a cloud framework comprising the visualization tool and a plurality of failure prediction models for the plurality of tools, wherein the plurality of failure prediction models comprise the tool model;
comparing prediction accuracies of the plurality of failure prediction models for a selected tool of the plurality of tools; and
selecting a specific model of the plurality of failure prediction models for the selected tool of the plurality of tools based on the comparison of prediction accuracies.
13 . The method of claim 12 , comprising:
comparing predictions and model training across the plurality of failure prediction models in relation to the selected tool of the plurality of tools; and
training the specific model of the plurality of failure prediction models for the selected tool of the plurality of tools based on the comparison of predictions and model training.
14 . The method of claim 1 , comprising:
iteratively predicting failure trends using the Arrhenius equivalent hours at a time interval;
predicting an impending tool failure; and
initiating the overhaul or the retirement based on the impending tool failure.
15 . A system for managing a plurality of tools of an organization in a cloud computing environment, comprising:
one or more computing devices comprising a processor, a memory, and instructions configured to operate:
an organizational data lake storing tool data associated with the plurality of tools, wherein the tool data comprises a maintenance history and a failure history for each tool of the plurality of tools;
a project data pond associated with ongoing projects related to the plurality of tools, wherein the project data pond continuously receives the tool data from the organizational data lake;
an on-demand data extract, transform, and load (ETL) component communicating the tool data with the project data pond;
a project code repository storing a plurality of templates corresponding to a plurality of tool models digitally and mathematically representative of the plurality of tools; and
a framework orchestration and monitoring platform configured to manage the ongoing projects, wherein the system is configured to:
iteratively generate a tool risk index for each tool of the plurality of tools based on tool modeling using a tool model of the plurality of tool models, the maintenance history, and the failure history, wherein the tool model includes Arrhenius equivalent hours as a function of an activation energy, a Boltzmann's constant, a reference temperature, and an operating temperature, wherein the tool risk index excludes a probability of failure for each tool of the plurality of tools, and the maintenance history and the failure history are combined to form a weighted tool data source;
compare tool risk indices of the plurality of tools to obtain a risk comparison based on a common metric, wherein the risk comparison comprises ranking a real-time tool health of the plurality of tools from high risk to low risk according to the tool risk indices;
determine the probability of failure for each tool of the plurality of tools based only on the risk comparison of the tool risk indices;
output a real-time visualization of the real-time tool health based on the probability of failure for each tool of the plurality of tools via a visualization tool;
predict a tool failure of at least one tool of the plurality of tools based on a trend over time of the probability of failure for the at least one tool;
identify the at least one tool of the plurality of tools having the predicted tool failure as needing a risk mitigation action;
select the risk mitigation action from a plurality of options comprising an overhaul or a retirement of the at least one tool of the plurality of tools;
automatically take the at least one tool of the plurality of tools offline to avoid an unscheduled downtime of oil and gas production caused by an actual tool failure; and
execute at least a portion of the overhaul or the retirement of the at least one tool of the plurality of tools based on the real-time tool health.
16 . The system of claim 15 , wherein the ongoing projects comprises training, testing, and validation of the plurality of tool models, and the plurality of tools models comprises a plurality of machine learning models.
17 . The system of claim 15 , wherein the ETL component is configured to:
extract the tool data from the project data pond;
transform the extracted tool data into a pre-defined data format specific to the organization; and
load the transformed data back into the project data pond.
18 . The system of claim 15 , comprising a code change tracking component configured to track code changes of code stored in the project code repository, wherein the code changes comprise template check-out, customization, check-in, and deletion.
19 . The system of claim 18 , comprising a tool level machine learning module configured to:
enable a user to add one or more machine learning models to the project code repository via the code change tracking component; and
enable the user to submit tool model training, validation, and testing jobs for the plurality of tools to run in parallel in the cloud computing environment.
20 . The system of claim 15 , comprising a continuous integration and continuous delivery (CI/CD) pipeline component configured to enable code automations for a user to facilitate tool model training, validation, and testing jobs for the plurality of tools to run in parallel in the cloud computing environment.
21 . A non-transitory, computer-readable medium storing instructions that, when executed by one or more processors, are configured to cause the one or more processors to:
receive tool data comprising a maintenance history and a failure history for each tool of a plurality of tools;
iteratively generate a tool risk index for each tool of the plurality of tools based on tool modeling using a tool model, the maintenance history and the failure history, wherein the tool model includes Arrhenius equivalent hours as a function of an activation energy, a Boltzmann's constant, a reference temperature, and an operating temperature, wherein the tool risk index excludes a probability of failure for each tool of the plurality of tools, and the maintenance history and the failure history are combined to form a weighted tool data source;
compare tool risk indices of the plurality of tools to obtain a risk comparison based on a common metric, wherein the risk comparison comprises ranking a real-time tool health of the plurality of tools from high risk to low risk according to the tool risk indices;
determine the probability of failure for each tool of the plurality of tools based only on the risk comparison of the tool risk indices;
output a real-time visualization of the real-time tool health based on the probability of failure for each tool of the plurality of tools via a visualization tool;
predict a tool failure of at least one tool of the plurality of tools based on a trend over time of the probability of failure for the at least one tool;
identify the at least one tool of the plurality of tools having the predicted tool failure as needing a risk mitigation action;
select the risk mitigation action from a plurality of options comprising an overhaul or a retirement of the at least one tool of the plurality of tools;
automatically take the at least one tool of the plurality of tools offline to avoid an unscheduled downtime of oil and gas production caused by an actual tool failure; and
execute at least a portion of the overhaul or the retirement of the at least one tool of the plurality of tools based on the real-time tool health.