# US 2022/0044151 A1 · Apparatus and method for electronic determination of system data integrity

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- Number: US 2022/0044151 A1
- Document ID: US20220044151A1
- Status: Published application
- Kind: A1
- Application number: 17/395,972
- Filed: 2021-08-06
- Published: 2022-02-10
- Earliest claimed: 2020-08-06
- Assignees: Front End Analytics
- Applicants: Front End Analytics LLC
- Inventors: Daniel Augusto BETTS; Juan Fernando BETTS; Sreekanth GONDIPALLE
- Web page: https://patentgrep.com/patent/US20220044151A1

## CPC

- G06N 20/00: Machine learning
- G06N 20/20: Ensemble learning
- G01D 3/08: with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
- G06F 16/2379: {Updates performed during online database operations; commit processing}
- G06N 3/09: Supervised learning
- G06N 3/04: Architecture, e.g. interconnection topology
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G07C 3/00: Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G01D: MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- 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

## Abstract

This application relates to apparatus and methods for determining the integrity of data, such as sensor data, in systems. In some examples, a computing device receives input data for the system, and executes a physics-based model to generate a first output. The physics-based model may include a plurality of surrogate models that simulate various portions of the system. The computing device may further execute a machine learning model that operates on the first output to generate a second output. The computing device may generate a predicted output for the system based on the first output and the second output. In some examples, the computing device determines an error for the system based on the predicted output and sensor data received from sensors for the system. Based on the error, the computing device determines if the sensor data is valid. The computing device may then provide an indication of the determination.

## Claims

### Claim 1

A computing device configured to:
receive sensor data from at least one sensor for a system;
determine a first value based on execution of a first model that operates on the sensor data and characterizes a relationship between inputs to the system and outputs from the system;
determine a second value based on execution of a second model that operates on the first value;
determine a sensor prediction value for the at least one sensor based on the first value and the second value; and
determine whether the sensor data is valid based on the sensor prediction value.

### Claim 2 (depends on 1)

The computing device of claim 1, wherein the first model is a physics-based model that operates on the sensor data and is based on at least one mathematical relationship between inputs to the system and outputs from the system, and the second model is a machine learning model that operates on the first value.

### Claim 3 (depends on 2)

The computing device of claim 2, wherein the physics-based model comprises a first weight and the machine learning model comprises a second weight, wherein the computing device is configured to train the first weight and the second weight based on the sensor prediction value and the sensor data.

### Claim 4 (depends on 2)

The computing device of claim 2, wherein the computing device is further configured to receive model input data, and wherein the physics-based model operates on the model input data.

### Claim 5 (depends on 4)

The computing device of claim 4, wherein the model input data comprises time-series data of prior sensor oil temperature readings of an engine, data identifying the engine's fuel consumption, data identifying the engine's coolant temperature, data identifying the mass flow rate of the oil in the engine, data identifying the mass flow rate of the coolant in the engine, and data identifying the speed of the engine's radiator fan.

### Claim 6 (depends on 1)

The computing device of claim 1, wherein the at least one sensor comprises a first sensor and a second sensor, the first model is a first classifier that operates on first sensor data from the first sensor, and the second model is a final classifier, the computing device further configured to:
determine a third value based on execution of a second classifier that operates on second sensor data from the second sensor; and
determine the second value based on execution of the final classifier that operates on the first value and the third value.

### Claim 7 (depends on 6)

The computing device of claim 6, wherein the computing device is further configured to:
train the first classifier with first system data corresponding to a first operating regime of the system;
train the second classifier with second system data corresponding to a second operating regime of the system;
apply the trained first classifier to the first sensor data to generate first output data;
apply the trained second classifier to the second sensor data to generate second output data; and
train the final classifier with the first output data and the second output data.

### Claim 8 (depends on 1)

The computing device of claim 1, wherein determining whether the sensor data is valid comprises:
determining whether the sensor prediction value is within a confidence interval;
determining that the sensor data is valid when the sensor prediction value is within the confidence interval; and
determining that the sensor data is invalid when the sensor prediction value is not within the confidence interval.

### Claim 9 (depends on 1)

The computing device of claim 1, wherein the computing device is further configured to:
receive current sensor data for the at least one sensor;
determine an error value based on the sensor prediction value and the current sensor data; and
determine at least one adjustment to a weight applied by the first model based on the error value.

### Claim 10 (depends on 1)

The computing device of claim 1, wherein the sensor data comprises an oil temperature of an engine.

### Claim 11 (depends on 1)

The computing device of claim 1, wherein the sensor prediction value is a predicted temperature and the sensor data is an actual temperature.

### Claim 12

A method comprising:
receiving sensor data from at least one sensor for a system;
determining a first value based on execution of a first model that operates on the sensor data and characterizes a relationship between inputs to the system and outputs from the system;
determining a second value based on execution of a second model that operates on the first output;
determining a sensor prediction value for the at least one sensor based on the first value and the second value; and
determining whether the sensor data is valid based on the sensor prediction value.

### Claim 13 (depends on 12)

The method of claim 12, wherein the first model is a physics-based model that operates on the sensor data and is based on at least one mathematical relationship between inputs to the system and outputs from the system, and the second model is a machine learning model that operates on the first value.

### Claim 14 (depends on 13)

The method of claim 13, wherein the physics-based model comprises a first weight and the machine learning model comprises a second weight, wherein the method comprises training the first weight and the second weight based on the sensor prediction value and the sensor data.

### Claim 15 (depends on 13)

The method of claim 13, comprising receiving model input data, and wherein the physics-based model operates on the model input data.

### Claim 16 (depends on 14)

The method of claim 14, wherein the model input data comprises time-series data of prior sensor oil temperature readings of an engine, data identifying the engine's fuel consumption, data identifying the engine's coolant temperature, data identifying the mass flow rate of the oil in the engine, data identifying the mass flow rate of the coolant in the engine, and data identifying the speed of the engine's radiator fan.

### Claim 17 (depends on 12)

The method of claim 12, wherein the at least one sensor comprises a first sensor and a second sensor, the first model is a first classifier that operates on first sensor data from the first sensor, and the second model is a final classifier, the method comprising:
determining a third value based on execution of a second classifier that operates on second sensor data from the second sensor; and
determining the second value based on execution of the final classifier that operates on the first value and the third value.

### Claim 18

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:
receiving sensor data from at least one sensor for a system;
determining a first value based on execution of a first model that operates on the sensor data and characterizes a relationship between inputs to the system and outputs from the system;
determining a second value based on execution of a second model that operates on the first output;
determining a sensor prediction value for the at least one sensor based on the first value and the second value; and
determining whether the sensor data is valid based on the sensor prediction value.

### Claim 19 (depends on 18)

The non-transitory computer readable medium of claim 18 wherein the first model is a physics-based model that operates on the sensor data and is based on at least one mathematical relationship between inputs to the system and outputs from the system, and the second model is a machine learning model that operates on the first value.

### Claim 20 (depends on 15)

The non-transitory computer readable medium of claim 15, wherein the at least one sensor comprises a first sensor and a second sensor, the first model is a first classifier that operates on first sensor data from the first sensor, and the second model is a final classifier, and further comprising instructions stored thereon that, when executed by at least one processor, further cause the device to perform operations comprising:
determining a third value based on execution of a second classifier that operates on second sensor data from the second sensor; and
determining the second value based on execution of the final classifier that operates on the first value and the third value.