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

Patent metadata

Field	Value
Publication number	US-10713864-B2
Application number	US-201816225654-A
Country	US
Kind code	B2
Filing date	Dec 19, 2018
Priority date	Feb 8, 2018
Publication date	Jul 14, 2020
Grant date	Jul 14, 2020

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.

First claim

What is claimed is:

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 operationa

Assignees

• Geotab Inc

Inventors

• Kyes John Robert Ford
• Davidson Mark Jeffrey
• Walli Thomas Arthur

Classifications

• B60N2230/20

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• B60N2/0024

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• Y02T10/70

  Energy storage systems for electromobility, e.g. batteries · CPC title

• G07C5/0816

  Indicating performance data, e.g. occurrence of a malfunction · CPC title

• G01S7/417

  involving the use of neural networks · CPC title