公开(公告)号：US20240264590A1

公开(公告)日：2024-08-08

申请号：US18105317

申请日：2023-02-03

Applicant: PALO ALTO RESEARCH CENTER INCORPORATED

Inventor： Peetak P. Mitra ,  Kai Frank Goebel

IPC: G05B23/02

CPC classification number: G05B23/0283 ,  G05B23/0254

Abstract: A system determines that an asset of an engineering system has transitioned from a quasi-steady degradation stage to an accelerated degradation phase based on sensor measurements received from an asset. During the accelerated degradation phase, features are extracted from the sensor measurements that are indicative of wear of the asset. A conformal mathematical filter is applied to the features that causes the features to conform to a wear curve formulation associated with the asset. An output of the filter is resampled to form a noise-reduced signal. The noise-reduced signal is input into a sequence machine learning model. A loss function of the sequence machine learning model uses an increased penalty to overprediction and a relaxed penalty for underprediction. An output of the sequence machine learning model is used to predict a remaining useful life (RUL) of the asset.

公开(公告)号：US20240104269A1

公开(公告)日：2024-03-28

申请号：US17946826

申请日：2022-09-16

Applicant: Palo Alto Research Center Incorporated

Inventor： Amirhassan Abbasi ,  Kai Frank Goebel ,  Peetak P. Mitra

IPC: G06F30/27

CPC classification number: G06F30/27 ,  G06F2119/02

Abstract: A transferable hybrid method for prognostics of engineering systems based on fundamental degradation modes is provided. The method includes developing a degradation model that represents degradation modes shared in different domains of application through the integration of physics and machine learning. The system measures sensor signals and data processing provides for extracting health indicators correlated with the fundamental degradation modes from sensors data. For the integration of physics and machine learning, the degradation mode is separated into different phases. Before the accelerated degradation phase of a system, the method is looking out to detect when the accelerated phase begins. When accelerated phase is active, the system applies a machine-learning model to provide information on the accelerated degradation phase, and evolves the degradation towards a failure threshold in a simulation of the updated physics-based model to predict the degradation progression. The system estimates the remaining useful life of the target system.