View source for 20240021024. METHODS AND SYSTEMS FOR GENERATING PREDICTIVE MAINTENANCE INDICATORS FOR A VEHICLE simplified abstract (Honeywell International Inc.)

=METHODS AND SYSTEMS FOR GENERATING PREDICTIVE MAINTENANCE INDICATORS FOR A VEHICLE=


==Organization Name==

[[:Category:Honeywell International Inc.|Honeywell International Inc.]]


[[Category:Honeywell International Inc.]]

==Inventor(s)==

[[:Category:David Kolbet of Phoenix AZ (US)|David Kolbet of Phoenix AZ (US)]][[Category:David Kolbet of Phoenix AZ (US)]]

[[:Category:Ramchandra Kankanala of Hyderabad TS (IN)|Ramchandra Kankanala of Hyderabad TS (IN)]][[Category:Ramchandra Kankanala of Hyderabad TS (IN)]]

[[:Category:Jeff Van Der Zweep of Phoenix AZ (US)|Jeff Van Der Zweep of Phoenix AZ (US)]][[Category:Jeff Van Der Zweep of Phoenix AZ (US)]]

[[:Category:Murali Kadeppagari of Bangalore KA (IN)|Murali Kadeppagari of Bangalore KA (IN)]][[Category:Murali Kadeppagari of Bangalore KA (IN)]]

[[:Category:Pradeep Kumar Mahalingaiah of Bangalore KA (IN)|Pradeep Kumar Mahalingaiah of Bangalore KA (IN)]][[Category:Pradeep Kumar Mahalingaiah of Bangalore KA (IN)]]

[[:Category:Rajavikrama Hanni of Bangalore KA (IN)|Rajavikrama Hanni of Bangalore KA (IN)]][[Category:Rajavikrama Hanni of Bangalore KA (IN)]]

==METHODS AND SYSTEMS FOR GENERATING PREDICTIVE MAINTENANCE INDICATORS FOR A VEHICLE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240021024 titled 'METHODS AND SYSTEMS FOR GENERATING PREDICTIVE MAINTENANCE INDICATORS FOR A VEHICLE

==Simplified Explanation==

The patent application describes a method for generating predictive maintenance indicators for a vehicle. Here is a simplified explanation of the abstract:

* The method involves creating a vehicle model that defines the different systems of the vehicle, each system having one or more functions.
* An anomaly algorithm is determined for each system function based on a signal set associated with that function.
* A signal anomaly detection model is generated using the vehicle system functions, associated signal sets, and anomaly algorithms.
* Signal data is monitored and evaluated to identify signal anomalies in the system functions.
* An anomaly score is determined and continuously updated until a maintenance action is identified.
* The vehicle model is updated in response to the maintenance action.
* Predictive indicators are generated based on the signal anomalies and changes in the signal data after the maintenance action.

Potential applications of this technology:

* Predictive maintenance for vehicles: This method can be used to predict maintenance needs in vehicles, allowing for proactive repairs and reducing the risk of breakdowns.
* Fleet management: The method can be applied to a fleet of vehicles, providing insights into the maintenance needs of each vehicle and optimizing maintenance schedules.
* Condition-based maintenance: By monitoring signal data and identifying anomalies, this method enables maintenance actions to be performed only when necessary, saving time and resources.

Problems solved by this technology:

* Preventive maintenance inefficiency: Traditional preventive maintenance schedules may result in unnecessary maintenance actions. This method allows for targeted maintenance based on actual system anomalies, reducing unnecessary repairs.
* Unplanned downtime: By predicting maintenance needs, this method helps prevent unexpected breakdowns and reduces the risk of unplanned downtime for vehicles.
* Costly repairs: Early detection of anomalies and proactive maintenance can help identify and address issues before they become major problems, potentially saving on costly repairs.

Benefits of this technology:

* Improved vehicle reliability: By identifying and addressing maintenance needs proactively, this method can improve the overall reliability of vehicles.
* Cost savings: Targeted maintenance actions based on actual system anomalies can help reduce unnecessary repairs and minimize downtime, resulting in cost savings.
* Optimal resource allocation: By generating predictive indicators, this method allows for better planning and allocation of maintenance resources, optimizing efficiency.


==Original Abstract Submitted==

as disclosed, a method for generating predictive maintenance indicators for a vehicle may include generating a vehicle model defining systems of the vehicle, each of the systems including one or more system functions; determining, for each system function, an anomaly algorithm based on a signal set associated with each system function; and generating a signal anomaly detection model based on the plurality of vehicle system functions, associated signal sets, and anomaly algorithms. the method may include monitoring signal data and evaluating the signal data to determine signal anomalies present in one or more system functions; determining an anomaly score; and continue updating the anomaly score until a maintenance action is identified to be performed. the method may then include updating the vehicle model in response to the maintenance action; generating predictive indicators based on the signal anomalies and changes in the signal data after performance of the identified maintenance action.



[[Category:G07C5/00]]
[[Category:G06F30/27]]