System and method for anomaly detection

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.

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.