# US 2019/0244445 A1 · Predictive indicators for operational status of vehicle components

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- Number: US 2019/0244445 A1
- Document ID: US20190244445A1
- Status: Published application
- Kind: A1
- Application number: 16/225,550
- 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/US20190244445A1

## 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 identify real time predictive indicators of operational vehicle component status comprising:
accessing at least one record of operational component data, said operational component data including operational values from at least one vehicle component from at least one vehicle, said operational values representative of an operational life cycle use of said at least one vehicle component, said operational values further based upon a measured component event,
accessing at least one record of management event data, said management event data containing at least one vehicle component event data point for at least one vehicle,
associating said at least one record of operational component data with said at least one record of management event data,
filtering said operational component data,
deriving from said operational component data at least one signal representative of said measured component event, and
comparing filtered operational component data and at least one signal prior to said vehicle component event data point with filtered operational component data and at least one signal post said vehicle component event data point thereby identifying real time predictive indicators of operational vehicle component status for real time use in fleet management.

### Claim 2 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status includes life cycle status representative of at least one of a new component state, a good component state, a fair component state, a poor component state, a replace component state or a failure component state.

### Claim 3 (depends on 1)

A method as in claim 1, wherein said operational component data includes data representative of at least one category of fuel and air metering, emission control, ignition system control, vehicle idle speed control, transmission control, hybrid propulsion, or battery, or data based upon at least one of on-board diagnostic fault codes, trouble codes, manufacturer codes, generic codes or vehicle specific codes.

### Claim at position 4

-6. (canceled)

### Claim 7 (depends on 1)

A method as in claim 1, wherein said operational life cycle includes a portion of operational values from a new component to a failed component.

### Claim 8 (depends on 1)

A method as in claim 1, wherein said measured component event is an event that provides a high operational load within the limits of said at least one vehicle component.

### Claim at position 7

-13. (canceled)

### Claim 14 (depends on 1)

A method as in claim 1, wherein said management event data is at least one of component maintenance indication, component repair indication, component failure indication or component replacement indication.

### Claim 15 (depends on 1)

A method as in claim 1, wherein said filtering determines a moving average or a running average of said operational values.

### Claim 16

(canceled)

### Claim 17 (depends on 1)

A method as in claim 1, wherein said at least one signal is determined when:
a single data point from said operational values is above an upper control limit or below a lower control limit,
a series of eight consecutive data points from said operational values are between a mean value and an upper control limit or between said mean value and a lower control limit,
a series of four out of five consecutive data points from said operational values are between a mean value and greater than plus one sigma or between said mean value and greater than minus one standard deviation, or
a series of two out of three consecutive data points from said operational values are between a mean value and greater than plus two sigma or said mean value and greater than minus two standard deviation.

### Claim at position 12

-22. (canceled)

### Claim 22 (depends on 1)

A method as in claim 1, further comprising a monitoring indicator framework including a lower control limit, an upper control limit, plus one standard deviation, plus two standard deviation, a mean value, minus one standard deviation and minus two standard deviation derived from said operational component data.

### Claim at position 14

-40. (canceled)

### Claim 41 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is replace vehicle component when said real time predictive indicators are at least one of: a moving average of said operational component data decreasing from a mean value to minus one standard deviation, a moving average of said operational component data decreasing from minus one standard deviation to minus two standard deviation, a moving average that reaches minus two standard deviation, and a moving average rising from minus one standard deviation to above plus one standard deviation followed by a moving average between plus one standard deviation and plus two standard deviation.

### Claim at position 16

-52. (canceled)

### Claim 53 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is a new vehicle component when said real time predictive indicators are a moving average between plus one standard deviation and plus two standard deviation.

### Claim 54 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is a good vehicle component when said real time predictive indicators are a moving average between plus one standard deviation and minus two standard deviation.

### Claim 55 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is a fair vehicle component when said real time predictive indicators are a moving average at or below minus two standard deviation.

### Claim 56 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is a poor vehicle component when said real time predictive indicators are at least one signal at or below a lower control limit.

### Claim at position 21

-58. (canceled)

### Claim 59 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is new and said real time predictive indicators are a plurality of signals above and below a moving average, said plurality of signals are further above a mean value and below an upper control limit.

### Claim 60

(canceled)

### Claim 61 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is good and said real time predictive indicators are a plurality of signals above a moving average and below plus one standard deviation.

### Claim 62 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is fair and said real time predictive indicators are a plurality of signals on either side of decreasing moving average.

### Claim at position 26

-64. (canceled)

### Claim 65 (depends on 1)

A method as in claim 1, wherein said operational vehicle component status is poor and said real time predictive indicators are a plurality of signals below a lower control limit.

### Claim 66

(canceled)

### Claim 67

A system to identify parameters indicative of a vehicle component status 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 on 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 the remote device,
said remote device accessing at least one record of operational component data, said operational component data including operational values from at least one vehicle component from at least one vehicle, said operational values representative of an operational life cycle use of said at least one vehicle component, said operational values further based upon a measured component event,
said remote device further accessing at least one record of management event data, said management event data containing at least one vehicle component event data point for at least one vehicle,
said remote device associating said at least one record of operational component data with said at least one record of management event data,
said remote device filtering said operational component data and deriving from said operational component data at least one signal representative of said measured component event, and
said remote device comparing filtered operational component data and at least one signal prior to said vehicle component event data point with filtered operational component data and at least one signal post said vehicle component event data point thereby identifying real time predictive indicators of operational vehicle component status for real time use in fleet management.

### Claim at position 30

-73. (canceled)

### Claim 74 (depends on 67)

A system as in claim 67, wherein said measured component event is an event that provides a high operational load within the limits of said at least one vehicle component.

### Claim 75 (depends on 67)

A system as in claim 67, wherein said measured component event is a cranking event for said at least one vehicle.

### Claim 76 (depends on 75)

A system as in claim 75, wherein said cranking event is detected by sensing a voltage decrease over time followed by an indication of engine RPM or vehicle speed.

### Claim 77

(canceled)

### Claim 78 (depends on 76)

A system as in claim 76, wherein a detected cranking event creates at least one record of operational component data in the form of a series of battery voltages.

### Claim 79 (depends on 78)

A system as in claim 78, wherein said series of battery voltages include values indicative of ignition on, starter motor cranking, battery charging and battery recovery.

### Claim at position 37

-86. (canceled)

### Claim 87 (depends on 76)

A system as in claim 76, wherein said cranking event produces lower voltages thereby increasing a number of signals for said measured component event.

### Claim 88 (depends on 67)

A system as in claim 67, further comprising a monitoring indicator framework providing a gauge for identifying said real time predictive indicators of operational vehicle component status.

### Claim 89 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework comprises a lower control limit, an upper control limit, plus one standard deviation, plus two standard deviation, a mean value, minus one standard deviation and minus two standard deviation derived from said operational component data.

### Claim 90 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a first zone, said first zone representative of a new component state wherein said at least one signal includes a mix of signal values that are disposed between a mean value and an upper control limit value.

### Claim 91 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a second zone, said second zone representative of a good component state wherein said at least one signal includes a mix of signal values that are disposed between a mean value and an upper control limit value.

### Claim 92 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a third zone, said third zone representative of a fair component state wherein said at least one signal includes a first signal values disposed above and below a mean value, second signal values disposed below said mean value and above a lower control limit, and third signal values disposed below the lower control limit.

### Claim 93 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a fourth zone, said fourth zone representative of a poor component state wherein said at least one signal includes a mix of signal values disposed between a mean value and a lower control limit.

### Claim 94 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a fifth zone, said fifth zone representative of a failed component state wherein said at least one signal include signal values disposed below a lower control limit.

### Claim 95 (depends on 88)

A system as in claim 88, wherein said monitoring indicator framework includes a sixth zone representative of a new component state wherein said at least one signal includes a mix of signal values disposed between a mean value and upper control limit.

### Claim at position 47

-133. (canceled)