Assessing historical telematic vehicle component maintenance records to identify predictive indicators of maintenance events

• US 10,713,864 B2
• Filed: 12/19/2018
• Issued: 07/14/2020
• Est. Priority Date: 02/08/2018
• Status: Active Grant

• Current Assignee: GeoTab Incorporated
• Original Assignee: GeoTab Incorporated
• Inventors: Kyes, John Robert Ford, Davidson, Mark Jeffrey, Walli, Thomas Arthur
• Primary Examiner(s): Badii, Behrang
• Application Number:
  US16/225,654
• Publication Number:
  US 20190244442A1
• Time in Patent Office: 573 Days
• CPC Class Codes: B60L 58/16 responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: B60W 2050/021 Means for detecting failure or malfunction
• CPC Class Codes: B60W 50/0205 Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: G01S 7/417 involving the use of neural networks
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06M 7/00 Counting of objects carried by a conveyor
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G07C 5/006 Indicating maintenance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries

Abstract

Apparatus, device, methods and system relating to a vehicular telemetry environment for monitoring vehicle components and providing indications towards the condition of the vehicle components and providing optimal indications towards replacement or maintenance of vehicle components before vehicle component failure.

Claims

1. A method of assessing historical vehicle component maintenance and identifying predictive indicators of maintenance events, the method comprising:

accessing a record of operational component data, said operational component data including operational values from a vehicle component from a vehicle, said operational values representative of an operational life cycle use of said vehicle component, said operational values further based upon a measured component event,

accessing a record of management event data, said management event data containing a vehicle component event data point for said vehicle, said vehicle component event data point including a date and a maintenance event indication,

associating said record of operational component data with said record of management event data,

filtering said operational component data, said filtering including determining a moving average of said operational component data, an upper control limit of said operational component data, plus two standard deviation of said operational component data, plus one standard deviation of said operational component data, a mean of said operational component data, minus one standard deviation of said operational component data, minus two standard deviation of said operational component data and a lower control limit of said operational component data,

deriving from said operational component data at least one signal representative of operational use of said vehicle component for said measured component event, and

comparing filtered operational component data with said at least one signal prior to said vehicle component event data point thereby identifying indicators associated with said maintenance event indication.

2. The method as in claim 1, wherein accessing a record of management event data containing a vehicle component event data point including a maintenance event indication comprises accessing a record of management event data containing a vehicle component event data point including a component failure and wherein comparing comprises identifying failure indicators, wherein identifying failure indicators comprises at least one of:

identifying a first failure indicator, said first failure indicator having a decreasing moving average from said mean of said operational component data to said minus one standard deviation of said operational component data,

identifying a second failure indicator, said second failure indicator having first signals above and below said decreasing moving average, and

identifying a third failure indicator, said third failure indicator having second signals below said lower control limit of said operational component data.

3. The method as in claim 2, wherein said first signals include “

O”

signal values.

4. The method as in claim 2, wherein said first signals include “

Y”

signal values.

5. The method as in claim 2, wherein said first signals include “

B”

signal values.

6. The method as in claim 2, wherein said second signals include “

R”

signal values.

7. The method as in claim 2, wherein said second signals are a plurality of second signals decreasing in value away from said lower control limit of said operational component data.

8. The method as in claim 1, wherein said comparing filtered operational component data with said at least one signal includes comparing the filtered operational component data with said at least one signal post said vehicle component event data point and said maintenance event indication is a component replacement and said component replacement was premature, said comparing identifies premature component replacement indicators, said premature component replacement indicators include at least one of the following indicators:

a first indicator, said first indicator having a moving average relatively constant between said mean and said plus one standard deviation prior to said vehicle component data point, said first indicator having a moving average increasing in value from said mean to said plus one standard deviation followed by a relatively constant moving average at said plus one standard deviation,

a second indicator, said second indicator having signals above said lower control limit, and

a third indicator, said third indicator having signals above said mean.

9. The method as in claim 1, wherein said comparing filtered operational component data with said at least one signal includes comparing the filtered operational component data with said at least one signal post said vehicle component event data point and said maintenance event indication is component maintenance and said comparing identifies component maintenance indicators, said component maintenance indicators include at least one of the following indicators:

a first indicator, said first indicator having a moving average relatively constant and above said minus two standard deviation prior to said vehicle component data point and a moving average increasing in value post said vehicle component data point, and

a second indicator, said second indicator having signals above said lower control limit.

10. The method as in claim 1, wherein said maintenance event indication is component failure and said comparing identifies indicators for an incorrect date for said maintenance event indication, said indicators include at least one of the following indicators:

a first failure indicator, said first failure indicator where said moving average is not decreasing from said mean of said operational component data to said minus one standard deviation of said operational component data,

a second failure indicator, said second failure indicator where first signals are not above and below a decreasing moving average, and

a third failure indicator, said third failure indicator where second signals are not below said lower control limit of said operational component data.

11. A system configured to assess historical vehicle component maintenance and identify predictive indicators of maintenance events, the system comprising:

a telematics hardware device including a processor, memory, firmware and communications capability,

a remote device including a processor, memory, software and communications capability,

said telematics hardware device configured to monitor at least one vehicle component from at least one vehicle and log operational component data of said at least one vehicle component, said telematics hardware device configured to communicate a log of operational component data to said remote device,

said remote device configured to access a record of operational component data, said operational component data including operational values from a vehicle component from a vehicle, said operational values representative of an operational life cycle use of said vehicle component, said operational values further based upon a measured component event,

said remote device configured to access a record of management event data, said management event data containing a vehicle component event data point for said vehicle, said vehicle component event data point including a date and a maintenance event indication,

said remote device configured to associate said record of operational component data with said record of management event data,

said remote device configured to filter said operational component data, wherein the filtering includes determining a moving average of said operational component data, an upper control limit of said operational component data, plus two standard deviation of said operational component data, plus one standard deviation of said operational component data, a mean of said operational component data, minus one standard deviation of said operational component data, minus two standard deviation of said operational component data and a lower control limit of said operational component data,

said remote device configured to derive from said operational component data at least one signal representative of operational use of said vehicle component for said measured component event,

said remote device configured to compare filtered operational component data with said at least one signal prior to said vehicle component event data point, thereby identifying indicators associated with said maintenance event indication.

12. The system as in claim 11, wherein said maintenance event indication is a component failure and said remote device configured to compare comprises said remote device configured to identify failure indicators, said failure indicators including at least one of the following indicators:

a first failure indicator, said first failure indicator having a decreasing moving average from said mean of said operational component data to said minus one standard deviation of said operational component data,

a second failure indicator, said second failure indicator having first signals above and below said decreasing moving average, and

a third failure indicator, said third failure indicator having second signals below said lower control limit of said operational component data.

13. The system as in claim 12, wherein said first signals include “

O”

signal values.

14. The system as in claim 12, wherein said first signals include “

Y”

signal values.

15. The system as in claim 12, wherein said first signals include “

B”

signal values.

16. The system as in claim 12, wherein said second signals include “

R”

signal values.

17. The system as in claim 12, wherein said second signals are a plurality of second signals decreasing in value away from said lower control limit of said operational component data.

18. The system as in claim 11, wherein said comparing filtered operational component data with said at least one signal includes comparing the filtered operational component data with said at least one signal post said vehicle component event data point and said maintenance event indication is a component replacement and said component replacement was premature, said comparing identifies premature component replacement indicators, said premature component replacement indicators include at least one of the following indicators:

a first indicator, said first indicator having a moving average relatively constant between said mean and said plus one standard deviation prior to said vehicle component data point, said first indicator having a moving average increasing in value from said mean to said plus one standard deviation followed by a relatively constant moving average at said plus one standard deviation,

a second indicator, said second indicator having signals above said lower control limit, and

a third indicator, said third indicator having signals above said mean.

19. The system as in claim 11, wherein said comparing filtered operational component data with said at least one signal includes comparing the filtered operational component data with said at least one signal post said vehicle component event data point and said maintenance event indication is component maintenance and said comparing identifies component maintenance indicators, said component maintenance indicators include at least one of the following indicators:

a first indicator, said first indicator having a moving average relatively constant and above said minus two standard deviation prior to said vehicle component data point and a moving average increasing in value post said vehicle component data point, and

a second indicator, said second indicator having signals above said lower control limit.

20. The system as in claim 11, wherein said maintenance event indication is component failure and said comparing identifies indicators for an incorrect date for said maintenance event indication, said indicators include at least one of the following indicators:

a first failure indicator, said first failure indicator where said moving average is not decreasing from said mean of said operational component data to said minus one standard deviation of said operational component data,

a second failure indicator, said second failure indicator where first signals are not above and below a decreasing moving average, and

a third failure indicator, said third failure indicator where second signals are not below said lower control limit of said operational component data.