Adaptive sequential probability ratio test to facilitate a robust remaining useful life estimation for critical assets

A non-transitory, computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for estimating a remaining useful life (RUL) of an asset, wherein during a surveillance mode, the method comprises:

• receiving a set of present time-series signals gathered from sensors in the asset;
• using an inferential model to generate estimated values for the set of present time-series signals;
• performing a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals;
• performing a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms having an associated tripping frequency (TF);
• while the TF exceeds a TF threshold, iteratively adjusting sensitivity parameters for the SPRT to reduce the TF, and performing the SPRT again on the residuals;
• and
• when the TF for the SPRT alarms falls below the TF threshold,
• using a logistic regression model to compute an RUL-based risk index for the asset based on the TF, and
• when the risk index exceeds a risk-index threshold, generating a notification indicating that the asset needs to be replaced.

A system that estimates a remaining useful life (RUL) of an asset, comprising:

• at least one processor and at least one associated memory;
• and
• a notification mechanism that executes on the at least one processor, wherein during a surveillance mode, the notification mechanism:
• receives a set of present time-series signals gathered from sensors in the asset;
• uses an inferential model to generate estimated values for the set of present time-series signals;
• performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals;
• performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms having an associated tripping frequency (TF);
• while the TF exceeds a TF threshold, iteratively adjusts sensitivity parameters for the SPRT to reduce the TF, and performs the SPRT again on the residuals;
• and
• when the TF for the SPRT alarms falls below the TF threshold,
• uses a logistic regression model to compute an RUL-based risk index for the asset based on the TF, and
• when the risk index exceeds a risk-index threshold, generates a notification indicating that the asset needs to be replaced.