# US 2019/0244440 A1 · Telematically monitoring a condition of an operational vehicle component

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2019/0244440 A1
- Document ID: US20190244440A1
- Status: Published application
- Kind: A1
- Application number: 16/225,582
- Filed: 2018-12-19
- Published: 2019-08-08
- Earliest claimed: 2018-02-08
- Assignees: Geotab Inc.
- Applicants: Geotab Inc.
- Inventors: John Robert Ford Kyes; Mark Jeffrey Davidson; Thomas Arthur Walli
- Web page: https://patentgrep.com/patent/US20190244440A1

## CPC

- G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)}
- B60L 58/16: responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
- B60W 50/0205: {Diagnosing or detecting failures; Failure detection models}
- G06N 5/046: Forward inferencing; Production systems
- G07C 5/006: {Indicating maintenance}
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- 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
- G07C 5/0816: {Indicating performance data, e.g. occurrence of a malfunction}
- G07C 5/085: {using electronic data carriers}
- B60N 2/0024: {for identifying, categorising or investigation of the occupant or object on the seat}
- B60N 2230/20: Wireless data transmission
- B60W 2050/021: {Means for detecting failure or malfunction}
- G01S 7/417: {involving the use of neural networks}
- 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)}
- G06M 7/00: Counting of objects carried by a conveyor
- Y02T 10/70: Energy storage systems for electromobility, e.g. batteries
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- B60L: PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES (arrangements or mounting of electrical propulsion units or of plural diverse prime-movers for mutual or common propulsion in vehicles B60K1/00, B60K6/20; arrangements or mounting of electrical gearing in vehicles B60K17/12, B60K17/14; preventing wheel slip by reducing power in rail vehicles B61C15/08; dynamo-electric machines H02K; control or regulation of electric motors H02P); SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES (electric coupling devices combined with mechanical couplings of vehicles B60D1/64; electric heating for vehicles B60H1/00); ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL (control or regulation of electric motors H02P); MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- B60N: SEATS SPECIALLY ADAPTED FOR VEHICLES; VEHICLE PASSENGER ACCOMMODATION NOT OTHERWISE PROVIDED FOR
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06M: COUNTING MECHANISMS; COUNTING OF OBJECTS NOT OTHERWISE PROVIDED FOR
- Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION

## 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

### Claim 1

A method to monitor a condition of an operational vehicle component comprising:
access a sample of operational component data that includes an operational data from at least one vehicle component from at least one vehicle,
determine operational component parameters from said operational data, said operational component parameters indicative of a current operation of said at least one vehicle component,
compare said sample of operational component parameters with predictive indicator parameters, said predictive indicator parameters predetermined from historical operational life cycle use of at least one other vehicle component, and
indicate a condition of said operational vehicle component based upon a comparison of said operational component parameters and said predictive indicator parameters.

### Claim 2 (depends on 1)

A method as in claim 1, wherein said operational data is a series of at least eight consecutive operational data points from said at least one vehicle component.

### Claim 3 (depends on 1)

A method as in claim 1, wherein said operational component parameters include a moving average representative of said current operation.

### Claim 4 (depends on 1)

A method as in claim 1, wherein said operational component parameters include signals representative of said current operation.

### Claim at position 5

-6. (canceled)

### Claim 7 (depends on 1)

A method as in claim 1, wherein said predictive indicator parameters include at least one of: a moving average representative of said historical operational life cycle use a mean representative of said historical operational life cycle use, a lower control limit representative of said historical operational life cycle use, an upper control limit representative of said historical operational life cycle use, and a standard deviation representative of said historical operational life cycle use.

### Claim at position 7

-12. (canceled)

### Claim 13 (depends on 1)

A method as in claim 1, wherein said operational life cycle use is from a new component to a failed component.

### Claim 14 (depends on 1)

A method as in claim 1, wherein said condition is one of a new component a good component a fair component or a poor component.

### Claim 15 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include minus one standard deviation and a minus two standard deviation of said operational life cycle data, and
said moving average is increasing in value between said minus one standard deviation and said minus two standard deviation thereby indicating a new component for said condition of said operational vehicle component.

### Claim 16 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include a minus one standard deviation and a mean of said operational life cycle data, and
said moving average is increasing in value between said minus one standard deviation and said mean thereby indicating a new component for said condition of said operational vehicle component.

### Claim 17 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include a mean and plus one standard deviation of said operational life cycle data, and
said moving average is increasing in value between said mean and said plus one standard deviation thereby indicating a new component for said condition of said operational vehicle component.

### Claim 18 (depends on 15)

A method as in claim 15, wherein:
said operational component parameters further include signals of said operational component data,
said predictive indicator parameters include a mean and an upper control limit of said operational life cycle data, and
said signals are between said mean and said upper control limit.

### Claim 19 (depends on 16)

A method as in claim 16, wherein:
said operational component parameters further include signals of said operational component data,
said predictive indicator parameters include an upper control limit of said operational life cycle data, and
said signals are between said mean and said upper control limit.

### Claim 20 (depends on 17)

A method as in claim 17, wherein:
said operational component parameters further include signals of said operational component data,
said predictive indicator parameters include an upper control limit of said operational life cycle data, and
said signals are between said mean and said upper control limit.

### Claim 21 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include signals of said operational component data,
said predictive indicator parameters include a mean and an upper control limit of said operational life cycle data, and
said signals are between said plus two standard deviation and said upper control limit thereby indicating a new component for said condition of said operational vehicle component.

### Claim 22

(canceled)

### Claim 23 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include a mean and plus two standard deviation of said operational life cycle data and
said moving average is between said mean and said plus two standard deviation of said operational life cycle data and said moving average has a relatively constant slop thereby indicating a good component for said condition of said operational vehicle component.

### Claim 24 (depends on 23)

A method as in claim 23, wherein:
said operational component parameters include signals of said operational component data, and
said signals are between said mean and said plus two standard deviation of said operational life cycle data.

### Claim at position 20

-26. (canceled)

### Claim 27 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include a mean and minus one standard deviation of said operational life cycle data, and
said moving average is decreasing in value between said mean and said minus one standard deviation thereby indicating a fair component for said condition of said operational vehicle component.

### Claim 28 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include minus one standard deviation and minus two standard deviation of said operational life cycle data, and
said moving average is decreasing in value between said minus one standard deviation and minus two standard deviation thereby indicating a fair component for said condition of said operational vehicle component.

### Claim 29 (depends on 27)

A method as in claim 27, wherein:
said operational component parameters include signals of said operational component data
said predictive indicator parameters further include a lower control limit, and
said signals are between said mean and said lower control limit of said operational life cycle data.

### Claim at position 24

-31. (canceled)

### Claim 32 (depends on 38)

A method as in claim 38, wherein:
said operational component parameters include signals of said operational component data,
said predictive indicator parameters further include a mean and lower control limit, and
said signals are between said mean and said lower control limit of said operational life cycle data.

### Claim at position 26

-34. (canceled)

### Claim 35 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average and signals of operational component data,
said predictive indicator parameters include a mean, minus one standard deviation and a lower control limit, and
said moving average is decreasing between said mean and said one standard deviation and said signals are below said lower control limit thereby indicating a poor component for said condition of said operational vehicle component.

### Claim 36

(canceled)

### Claim 37 (depends on 1)

A method as in claim 1, wherein:
said operational component parameters include a moving average and signals of operational component data,
said predictive indicator parameters include a minus one standard deviation, minus two standard deviation and a lower control limit and
said moving average is decreasing between said one standard deviation and said two standard deviation and said signals are below said lower control thereby indicating a poor component for said condition of said operational vehicle component.

### Claim 38

(canceled)

### Claim 39

A system to monitor a condition of an operational vehicle component comprising:
a telematics hardware device including a processor, memory, firmware and communications capability, and
a remote device including a processor memory software and communications capability,
said telematics hardware device monitoring at least one vehicle component from at least one vehicle and logging operational component data of said at least one vehicle component, said telematics hardware device communicating a log of operational component data to said remote device
said remote device accessing a sample of operational component data that includes an operational data from at least one vehicle component from at least one vehicle,
said remote device determining operational component parameters from said operational data, said operational component parameters indicative of a current operation of said at least one vehicle component
said remote device comparing said sample of operational component parameters with predictive indicator parameters, said predictive indicator parameters predetermined from historical operational life cycle use of at least one other vehicle component, and
said remote device indicating a condition of said operational vehicle component based upon a comparison of said operational component parameters and said predictive indicator parameters.

### Claim at position 32

-43. (canceled)

### Claim 44 (depends on 39)

A system as in claim 39, wherein said at least one vehicle component is at least one of a thermostat or temperature sensors, oil sensors, fuel sensors, coolant sensors, transmission fluid sensors, electric motor coolant sensors, battery, pressure sensors, oil pressure sensors, fuel pressure sensors, crankcase sensors, hydraulic sensors, fuel volume, fuel shut off, camshaft position sensors, crankshaft position sensors, 02 sensors, turbocharger sensors, waste gate sensors, air injection sensors, mass air flow sensors, throttle body sensors, air metering sensors, emission sensors, throttle position sensors, fuel delivery, fuel timing, system lean, system rich, injectors, cylinder timing, engine speed conditions, charge air cooler bypass, fuel pump sensors, intake air flow control, misfire indications, accelerometer sensors, knock sensors, glow plug sensors, exhaust gas recirculation sensors, air injection sensors, catalytic convertor sensors evaporative emission sensors, brake sensors, idle speed control sensors, throttle position, air conditioning sensors, power steering sensors, system voltages, engine control module values, starter motor voltage, starter motor current, torque converter sensors, fluid sensors, output shaft speed values, gear position, transfer box, converter status, interlock, torque values, hybrid battery pack values, cooling fan values and inverter and battery voltages.

### Claim at position 34

-52. (canceled)

### Claim 53 (depends on 39)

A system as in claim 39, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include i) a minus one standard deviation and a minus two standard deviation of said operational life cycle data, ii) a minus one standard deviation and a mean of said operational life cycle data, or iii) a mean and plus one standard deviation of said operational life cycle data, and
said moving average is increasing in value between i) said minus one standard deviation and said minus two standard deviation, or ii) said minus one standard deviation and said mean, or iii) said mean and said plus one standard deviation, thereby indicating a new component for said condition of said operational vehicle component.

### Claim at position 36

-58. (canceled)

### Claim 59 (depends on 39)

A system as in claim 39, wherein:
said operational component parameters include signals of said operational component data,
said predictive indicator parameters include a mean and an upper control limit of said operational life cycle data, and
said signals are between said plus two standard deviation and said upper control limit thereby indicating a new component for said condition of said operational vehicle component.

### Claim 60

(canceled)

### Claim 61 (depends on 39)

A system as in claim 39, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include a mean and plus two standard deviation of said operational life cycle data and
said moving average is between said mean and said plus two standard deviation of said operational life cycle data and said moving average has a relatively constant slop thereby indicating a good component for said condition of said operational vehicle component.

### Claim at position 40

-64. (canceled)

### Claim 65 (depends on 39)

A system as in claim 39, wherein:
said operational component parameters include a moving average of said operational component data,
said predictive indicator parameters include i) a mean and minus one standard deviation of said operational life cycle data, or ii) a minus one standard deviation and minus two standard deviation of said operational life cycle data, and
said moving average is decreasing in value between i) said mean and said minus one standard deviation or ii) said minus one standard deviation and minus two standard deviation, thereby indicating a fair component for said condition of said operational vehicle component.

### Claim at position 42

-72. (canceled)

### Claim 73 (depends on 39)

A system as in claim 39, wherein:
said operational component parameters include a moving average and signals of operational component data,
said predictive indicator parameters include i) a mean, minus one standard deviation and a lower control limit, or ii) a minus one standard deviation, minus two standard deviation and a lower control limit, and
said moving average is decreasing between said i) mean and said one standard deviation or ii) one standard deviation and said two standard deviation, and said signals are below said lower control thereby indicating a poor component for said condition of said operational vehicle component.

### Claim at position 44

-76. (canceled)