MULTI-MODAL FLUID CONDITION SENSOR PLATFORM AND SYSTEM THEREFOR

• US 20140188407A1
• Filed: 12/31/2012
• Published: 07/03/2014
• Est. Priority Date: 12/31/2012
• Status: Active Grant

• Current Assignee: Mast Inc.
• Original Assignee: Mast Inc.
• Inventors: VON HERZEN, Brian, Van Fleet, Steven
• Granted Patent: US 9,568,461 B2
• Time in Patent Office: Days
• US Class Current: 702/50
• CPC Class Codes: F01M 11/10 Indicating devices; Other safety devices
• CPC Class Codes: F01M 2011/14 for indicating the necessity to change the oil
• CPC Class Codes: F16N 2250/00 Measuring
• CPC Class Codes: F16N 2250/04 Pressure
• CPC Class Codes: F16N 2250/08 Temperature
• CPC Class Codes: F16N 2250/32 Inductive
• CPC Class Codes: F16N 2250/34 Transparency; Light; Photo sensor
• CPC Class Codes: F16N 29/00 Special means in lubricating arrangements or systems providing for the indication or detection of undesired conditions; Use of devices responsive to conditions in lubricating arrangements or systems
• CPC Class Codes: G01D 21/02 Measuring two or more variables by means not covered by a single other subclass
• CPC Class Codes: G01N 33/28 Oils , i.e. hydrocarbon liquids edible oils or edible fats G01N33/03
• CPC Class Codes: G01N 33/2888 Lubricating oil characteristics, e.g. deterioration lubricating properties G01N33/30
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 17/142 Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm

Abstract

This invention encompasses embodiments for multi-modal integrated simultaneous measurement of various aspects of fluids contained in circulating systems such as automotive reciprocating engines and vehicle transmissions. These circulating systems perform constant internal lubrication, and heat and contaminant removal to protect the internal moving parts from the inherent friction and damage in normal operation. Most commonly this is achieved with fluids based on hydrocarbon and/or related synthetics, which, over time, can lose their protective properties, and vary in their performance or breakdown/decay due to internal and external events. Several components within the lubricant fluid can be measured and can provide insight into the efficacy of the system to perform its designed mission. Described herein is a real-time, simultaneous, integrated, multi-modal sensor system for early warning notification.

Claims

1. An integrated system for continuous monitoring of multiple properties of a fluid derived from measurements from a plurality of sensor modalities within a fluid-based closed-system environment.

2. The integrated system of claim 1, wherein said system is an in-motor lubrication monitoring system.

3. The integrated system of claim 1, wherein said monitoring is real-time.

4. The integrated system of claim 1, built into the form factor of a standard size and shaped oil plug element found within a reciprocating engine oil drain plug, wherein said system is remotely located from a display and processing unit by wired or wireless data telemetry.

5. The integrated system of claim 1, additionally comprising a remotely located processing and display unit.

6. The integrated system of claim 1, wherein the sensor modalities comprise at least two of electrical, temperature, magnetic, optical and pressure sensors.

7. The integrated system of claim 1, wherein at least one of the sensor modalities comprises an inductor.

8. The integrated system of claim 1, wherein the sensor modalities comprise at least magnetic and optical sensors.

9. The integrated system of claim 1, wherein the sensor modalities comprise at least electrical, magnetic and optical sensors.

10. The integrated system of claim 1, contained within an epoxy encapsulation that can support high temperature and high pressure environments.

11. The integrated system of claim 1, further comprising multiple digital signal processor modules for detection of both single and multiple related fluid characteristics.

12. The integrated system of claim 1, further comprising multi-stage output signal generation selected from the group consisting of error indication, specific data signature detection signal, specific data signature signal detection strength level, and Fast Fourier Transform. (FFT) data output.

13. The integrated system of claim 1, wherein the sensor modality measurements are analyzed using Kalman Filtering techniques.

14. The integrated system of claim 1, wherein the sensor modality measurements are analyzed using Baysian analytic techniques.

15. The integrated system of claim 1, wherein the sensor modality measurements are analyzed using hidden-Markov Filtering techniques.

16. The integrated system of claim 1, wherein the sensor modality measurements are analyzed using fuzzy logic analysis techniques.

17. The integrated system of claim 1, wherein the sensor modality measurements are analyzed using neural network analysis techniques.

18. The integrated system of claim 1, wherein the sensor modality measurements comprise at least one of the following:

a) Differential temperature comparison

b) Differential magnetic sensor comparison

c) Differential inductive sensor comparison

d) Differential electrical impedance comparison

e) Differential optical absorption comparison

f) Any combination and integrated comparison consisting of at least a set of two sensors

g) Data comparison of each sensor vector versus time and temperature

h) Data comparison of an integrated vector consisting of a set of at least two sensors combined

i) Inductive data comparison versus time and temperature

j) Optical data comparison versus time and temperature

k) Optical data comparison versus temperature and pressure

l) Temperature data comparison versus time and pressure to detect peak heat, and

m) Other sensor combinations.

19. A method of continuously monitoring an operating fluid of a machine comprising:

measuring a first condition of the fluid using a first sensor modality, measuring a second condition of the fluid using a second sensor modality, filtering data from the sensors, integrating the data from the sensors, analyzing the data from the sensors, deriving a property of the fluid from the data, transmitting the derived property of the fluid condition to a receiver, and repeating the process so as to accumulate a time-series of a fluid property that tracks changes in the operating condition of the fluid.

20. The method of claim 19, further comprising tracking the condition of the fluid by calculating the time series expected rates of change versus observed rates of change of any single or multiple conditions.

21. The method of claim 19, farther comprising calculating the expected divergence or convergence across multiple sensor time series data of anticipated and expected measured value changes versus unexpected changes.