Systems and methods for plant equipment condition monitoring

Patent metadata

Field	Value
Publication number	US-12498695-B2
Application number	US-202217975058-A
Country	US
Kind code	B2
Filing date	Oct 27, 2022
Priority date	Feb 22, 2022
Publication date	Dec 16, 2025
Grant date	Dec 16, 2025

Abstract

A method includes obtaining unprocessed data from a plurality of sensors mounted on one or more plant equipment, normalizing the unprocessed data to generate normalized data, converting the unprocessed data from a time domain to a frequency domain to generate a frequency domain signal, detecting a performance anomaly associated with the one or more plant equipment based on the normalized data and a change index routine, and determining a primary cause from among one or more causes of the performance anomaly based on the frequency domain signal and one or more rules.

First claim

What is claimed is:

1 . A method comprising:
obtaining unprocessed data from a plurality of sensors mounted on one or more plant equipment; normalizing the unprocessed data to generate normalized data; converting the unprocessed data from a time domain to a frequency domain to generate a frequency domain signal; detecting a performance anomaly associated with the one or more plant equipment based on the normalized data and a machine learning model; identifying one or more causes of the performance anomaly based on the frequency domain signal; determining a primary type of cause from among the one or more causes of the performance anomaly based on the frequency domain signal and a plurality of rules that correlate different frequency characteristics of the frequency domain signal to different types of the one or more causes; and performing a corrective action on the one or more plant equipment based on the primary cause.

2 . The method of claim 1 further comprising identifying one or more harmonic peaks of the frequency domain signal, wherein the plurality of rules are further based on the one or more harmonic peaks.

3 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to a fundamental frequency, the primary type of cause is associated with a looseness of a part of the one or more plant equipment.

4 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to a fundamental frequency and a second harmonic, the primary type of cause is associated with a misalignment of the one or more plant equipment.

5 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to only a fundamental frequency, the primary type of cause is associated with a structural looseness of a part of the one or more plant equipment.

6 . The method of claim 2 , wherein in response to (i) the one or more harmonic peaks corresponding to a third harmonic and a fourth harmonic and (ii) an amplitude of the fourth harmonic is greater than an amplitude of the third harmonic, the primary type of cause is associated with a misalignment of the one or more plant equipment.

7 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to a number of gear teeth, the primary type of cause is associated with one of a gear misalignment and a gear meshing of the one or more plant equipment.

8 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to a number of rotor bars, the primary type of cause is associated with a motor issue of the one or more plant equipment.

9 . The method of claim 2 , wherein in response to the one or more harmonic peaks corresponding to a harmonic associated with a predetermined frequency, the primary type of cause is associated with a bearing issue of the one or more plant equipment.

10 . The method of claim 1 , wherein the normalized data includes at least one of a root mean square (RMS) velocity value, an RMS acceleration value, a peak velocity value, and a peak acceleration value, or a combination thereof.

11 . The method of claim 1 further comprising performing a noise cancellation routine on the unprocessed data to generate the normalized data.

12 . The method of claim 1 , wherein the unprocessed data includes at least one of velocity data and, acceleration data.

13 . The method of claim 1 , further comprising detecting the performance anomaly based on statistical model.

14 . A system comprising:
one or more processors and one or more nontransitory computer-readable mediums storing instructions that are executable by the one or more processors, wherein the instructions comprise:
obtaining unprocessed data from a plurality of sensors mounted on one or more plant equipment;
normalizing the unprocessed data to generate normalized data;
converting the unprocessed data from a time domain to a frequency domain to generate a frequency domain signal;
identifying one or more harmonic peaks of the frequency domain signal;
detecting a performance anomaly associated with the one or more plant equipment based on the normalized data and a machine learning model;
determining a primary type of cause from among one or more causes of the performance anomaly based on the one or more harmonic peaks and a plurality of rules that correlate different frequency characteristics of the frequency domain signal to different types of the one or more causes; and
performing a corrective action on the one or more plant equipment based on the primary cause.

15 . The system of claim 14 , wherein the instructions further comprise:
in response to the one or more harmonic peaks corresponding to a fundamental frequency, the primary type of cause is associated with a looseness of a part of the one or more plant equipment.

16 . The system of claim 14 , wherein the instructions further comprise:
in response to the one or more harmonic peaks corresponding to a fundamental frequency and a second harmonic, the primary type of cause is associated with a misalignment of the one or more plant equipment.

17 . The system of claim 14 , wherein the instructions further comprise:
in response to the one or more harmonic peaks corresponding to only a fundamental frequency, the primary type of cause is associated with a structural looseness of a part of the one or more plant equipment.

18 . The system of claim 14 , wherein the instructions further comprise:
in response to (i) the one or more harmonic peaks corresponding to a third harmonic and a fourth harmonic and (ii) an amplitude of the fourth harmonic is greater than an amplitude of the third harmonic, the primary type of cause is associated with a misalignment of the one or more plant equipment.

19 . The system of claim 14 , wherein the instructions further comprise:
in response to the one or more harmonic peaks corresponding to a number of gear teeth, the primary type of cause is associated with one of a gear misalignment and a gear meshing of the one or more plant equipment; and in response to the one or more harmonic peaks corresponding to a number of rotor bars, the primary type of cause is associated with a motor issue of the one or more plant equipment.

20 . The system of claim 14 , wherein the instructions further comprise:
in response to the one or more harmonic peaks corresponding to a harmonic associated with a predetermined frequency, the primary type of cause is associated with a bearing issue of the one or more plant equipment.

Assignees

• Ford Motor Co

Inventors

• Rajendran Harishankar
• Jalluri Chandra Sekhar
• Sai Kishore B
• Blackful Jasper
• Gardner Loren
• Sundarakumar Nimalkumar
• Walker Mike
• Shaw Paul Christopher
• Accetta Dan
• Raghavan V
• Furness Richard James

Classifications

• G05B19/402

  characterised by control arrangements for positioning, e.g. centring a tool relative to a hole in the workpiece, additional detection means to correct position (G05B19/19 takes precedence) · CPC title

• G05B23/0275

  Fault isolation and identification, e.g. classify fault; estimate cause or root of failure · CPC title

• G05B23/024

  Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks · CPC title

• G05B2219/32252

  Scheduling production, machining, job shop · CPC title

• G05B19/4063 Primary

  Monitoring general control system (G05B19/4062 takes precedence) · CPC title