# US 9,752,960 B2 · System and method for anomaly detection

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- Number: US 9,752,960 B2
- Document ID: US9752960B2
- Status: Granted
- Kind: B2
- Application number: 14/359,539
- Filed: 2011-11-22
- Published: 2015-02-19
- Granted: 2017-09-05
- Earliest claimed: 2011-11-22
- Assignees: Electric Power Research Institute, Inc.
- Applicants: Leonard Angello; Tim Lieuwen; David Robert Noble; Brian Poole
- Inventors: Leonard Angello; Tim Lieuwen; David Robert Noble; Brian Poole
- Web page: https://patentgrep.com/patent/US9752960B2

## CPC

- G01M 15/14: Testing gas-turbine engines or jet-propulsion engines
- F01D 21/003: {Arrangements for testing or measuring (for measuring vibrations G01H)}
- F23N 5/242: {using electronic means}
- F05D 2260/80: Diagnostics
- F05D 2270/304: Spool rotational speed
- F05D 2270/334: Vibration measurements
- F05D 2270/708: with comparison tables
- F23N 2241/20: Gas turbines
- F23R 2900/00013: Reducing thermo-acoustic vibrations by active means
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- F01D: NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES (machines or engines for liquids F03; non-positive displacement pumps F04D)
- F23N: REGULATING OR CONTROLLING COMBUSTION (control devices specially adapted for combustion apparatus in which combustion takes place in a fluidised bed of fuel or other particles F23C10/28; condition responsive controls for regulating combustion in domestic stoves with open fires for solid fuel F24B1/187)
- F05D: INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F23R: GENERATING COMBUSTION PRODUCTS OF HIGH PRESSURE OR HIGH VELOCITY, e.g. GAS-TURBINE COMBUSTION CHAMBERS (fluidised bed combustion apparatus specially adapted for operation at superatmospheric pressures F23C10/16)

## Abstract

A system and method for anomaly detection is provided. The system (100) and method (200) include utilizing one or more processors, such as in a server, for receiving (108) operational and dynamics data (104) from sensors associated with devices (102), filtering the data, establishing a set of baseline dynamics data and eliminating data dependencies (110). The system and method further include generating an expected level of data variation (112), identifying an anomaly based on a deviation of the device data from the baseline data normalized by the expected level of data variation (114), optionally correlating an anomaly with potential causes, and providing an output indicating an anomaly (116).

## Claims

### Claim 1

A computer implemented method for anomaly detection, the method comprising:
utilizing one or more processors and associated memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for:
receiving operational and dynamics data from a plurality of sensors associated with a plurality of devices;
filtering the data, wherein said filtering the data comprises data smoothing and eliminating outlying data, the data smoothing comprising:
Fourier transforming the dynamics data;
extracting an amplitude and frequency for a plurality of pressure oscillation spectral bins over a time period; and
calculating a backward running average;
establishing a set of baseline dynamics data including calculating a reference mean for each dynamic based on identifying historical data values
a. relating to a sliding time window, or
b. corresponding to a database query establishing reference data requirements and tolerances; identifying previous points in time that satisfy said data requirements and tolerances; and averaging the value of dynamics data related to said identified points in time;
eliminating data dependencies;
generating an expected level of data variation;
identifying an anomaly based on a deviation of the device data from the baseline data normalized by the expected level of data variation;
optionally correlating the anomaly with potential causes; and
providing an output indicating the anomaly.

### Claim 2 (depends on 1)

The method of claim 1, wherein the devices comprise gas turbine combustor cans.

### Claim 3 (depends on 2)

The method of claim 2, wherein the dynamics data comprise pressure oscillations.

### Claim 4 (depends on 2)

The method of claim 2, wherein the dynamics data further comprise at least one of electromagnetic radiation, chemiluminescence, velocity oscillations or another observable related to combustion dynamic data.

### Claim 5 (depends on 1)

The method of claim 1, wherein the time period comprises ten seconds to two hours.

### Claim 6 (depends on 1)

The method of claim 1, wherein the spectral bins comprise a plurality of dynamics, optionally at least one of low frequency dynamics, first intermediate dynamics, second intermediate dynamics or high frequency dynamics.

### Claim 7 (depends on 1)

The method of claim 1, wherein said eliminating outlying data comprises:
eliminating bad sensor data; and/or
eliminating data excursions present in data related to a gas turbine combustor can but not present in data relating to adjacent gas turbine combustor cans.

### Claim 8 (depends on 1)

The method of claim 1, wherein the sliding time window looks back at predefined time periods and the baseline data is updated at each pre-defined time period.

### Claim 9 (depends on 1)

The method of claim 1, wherein said data requirements comprise at least one of power, machine-on time, fuel composition, transient operation, ambient temperature, inlet guide vane angle, exit temperature, fuel splits or flashback temperature.

### Claim 10 (depends on 1)

The method of claim 1, wherein said eliminating data dependencies comprises:
subtracting the reference mean from the dynamics data for each device;
calculating the non-uniformity of each device relative to other devices; and
analyzing changes in one device relative to changes in the other devices.

### Claim 11 (depends on 1)

The method of claim 1, wherein said expected level of data variation comprises:
quantifying a level of data variation across devices.

### Claim 12

An anomaly detection system for detecting anomalies in a turbine engine, the anomaly detection system comprising:
an input data module configured to receive sensor data from the turbine engine;
a processing module adapted to
i) filter the data, wherein said filtering the data comprises data smoothing and eliminating outlying data, the data smoothing comprising:
Fourier transforming the dynamics data;
extracting an amplitude and frequency for a plurality of pressure oscillation spectral bins over a time period; and
calculating a backward running average;
ii) establish a set of baseline dynamics data including calculating a reference mean for each dynamic based on identifying historical data values a) relating to a sliding time window, or b) corresponding to a database query capable of establishing reference data requirements and tolerances, identifying previous points in time that satisfy said data requirements and tolerances and averaging the value of dynamics data related to said identified points in time,
iii) eliminate data dependencies,
iv) generate an expected level of data variation; and
v) identify an anomaly based on a deviation of the sensor data from the baseline data normalized by the expected level of data variation;
a database capable of storing sensor data and communicating with the processing module;
an output data module capable of reporting results identified by the processing module;
an interface module capable of communicating results reported by the output data module;
a processor capable of managing operation of the data input module, processing module, database, output data module and/or interface module; and
memory capable of storing instructions and data for execution by the system.

### Claim 13 (depends on 12)

The system of claim 12, wherein said sliding time window looks back at predefined time periods.

### Claim 14 (depends on 13)

The system of claim 13, wherein the processing module is further adapted to:
subtract the reference mean from the dynamics data for each device;
calculate the non-uniformity of each device relative to other devices; and
analyze changes in one device relative to changes in the other devices.

### Claim 15

A non-transitory computer-readable storage medium on which is encoded executable program code for performing a method comprising:
receiving operational and dynamics data from a plurality of sensors associated with a plurality of devices;
filtering the data, wherein said filtering the data comprises data smoothing and eliminating outlying data, the data smoothing comprising:
Fourier transforming the dynamics data;
extracting an amplitude and frequency for a plurality of pressure oscillation spectral bins over a time period; and
calculating a backward running average;
establishing a set of baseline dynamics data including calculating a reference mean for each dynamic based on identifying historical data values
a. relating to a sliding time window, or
b. corresponding to a database query establishing reference data requirements and tolerances; identifying previous points in time that satisfy said data requirements and tolerances; and averaging the value of dynamics data related to said identified points in time;
eliminating data dependencies;
generating an expected level of data variation;
identifying an anomaly based on a deviation of the device data from the baseline data normalized by the expected level of data variation;
optionally correlating said anomaly with potential causes; and
providing an output indicating the anomaly.

### Claim 16 (depends on 15)

The non-transitory computer-readable storage medium of claim 15, wherein said devices comprise gas turbine combustor cans and said dynamics data comprise pressure oscillations.

### Claim 17 (depends on 16)

The non-transitory computer-readable storage medium of claim 16, wherein said eliminating data dependencies comprises:
subtracting the reference mean from the dynamics data for each device;
calculating the non-uniformity of each device relative to other devices; and
analyzing changes in one device relative to changes in the other devices.