公开(公告)号：US20180165592A1

公开(公告)日：2018-06-14

申请号：US15602697

申请日：2017-05-23

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Po-Yu Huang ,  Chuang-Hua Chueh ,  Jia-Min Ren

IPC: G06N7/00 ,  G06N99/00

CPC classification number: G06N20/00 ,  G05B23/0283

Abstract: A system and method for predicting remaining lifetime of a component of equipment is provided. The prediction system includes a data module, a feature module, a current data-based prediction module, a historical data-based prediction module, and a confidence module. The data module obtains a test sensor data of the component of equipment. The feature module obtains a historical health indicator and the current-health indicator. The current data-based prediction module obtains a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator. The historical data-based prediction module obtains a second predicted remaining lifetime and a second prediction confidence according to the historical health indicator. The confidence module generates a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence.

公开(公告)号：US11106190B2

公开(公告)日：2021-08-31

申请号：US15602697

申请日：2017-05-23

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Po-Yu Huang ,  Chuang-Hua Chueh ,  Jia-Min Ren

IPC: G06N20/00 ,  G05B19/4065 ,  G05B23/02

Abstract: A system and method for predicting remaining lifetime of a component of equipment is provided. The prediction system includes a data module, a feature module, a current data-based prediction module, a historical data-based prediction module, and a confidence module. The data module obtains a test sensor data of the component of equipment. The feature module obtains a historical health indicator and the current-health indicator. The current data-based prediction module obtains a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator. The historical data-based prediction module obtains a second predicted remaining lifetime and a second prediction confidence according to the historical health indicator. The confidence module generates a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence.

公开(公告)号：US10262270B2

公开(公告)日：2019-04-16

申请号：US15239106

申请日：2016-08-17

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Hsiang-Tsung Kung ,  Jia-Min Ren ,  Chuang-Hua Chueh ,  Sen-Chia Chang

IPC: G06N5/04 ,  G06N7/00 ,  G05B23/02 ,  G06N20/00

Abstract: A system and a method for predicting a remaining useful life (RUL) of a component of an equipment are provided. The system for predicting the RUL of the component of the equipment includes a data acquisition unit, a feature capturing unit, a mapping function generating unit, a similarity analyzing unit and a RUL calculating unit. The feature capturing unit obtains an estimation feature according to a real time sensing record, and obtains a plurality of training features according to a set of history sensing records. The similarity analyzing unit obtains k similar features which are similar to the estimation feature according to the training features. The RUL calculating unit obtains at least one of k predicting information via a mapping function according to the k similar features and calculates an estimation RUL according to at least one predicting value.

公开(公告)号：US11551155B2

公开(公告)日：2023-01-10

申请号：US16231732

申请日：2018-12-24

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Chuang-Hua Chueh ,  Jia-Min Ren ,  Po-Yu Huang ,  Yu-Hsiuan Chang

IPC: G06N20/20 ,  G06K9/62

Abstract: An ensemble learning prediction method includes: establishing a plurality of base predictors based on a plurality of training data; initializing a plurality of sample weights of a plurality of sample data and initializing a processing set; in each iteration round, based on the sample data and the sample weights, establishing a plurality of predictor weighting functions of the predictors in the processing set and predicting each of the sample data by each of the predictors in the processing set for identifying a prediction result; evaluating the predictor weighting functions, and selecting a respective target predictor weighting function from the predictor weighting functions established in each iteration round and selecting a target predictor from the predictors in the processing set to update the processing set and to update the sample weights of the sample data.