The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 31, 2023

Filed:

Mar. 13, 2019
Applicant:

Tata Consultancy Services Limited, Mumbai, IN;

Inventors:

Pankaj Malhotra, Noida, IN;

Vishnu Tv, Noida, IN;

Lovekesh Vig, Gurgaon, IN;

Gautam Shroff, Gurgaon, IN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 20/00 (2019.01); G06F 11/22 (2006.01);
U.S. Cl.
CPC ...
G06N 3/0445 (2013.01); G06F 11/2263 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01);
Abstract

Estimating Remaining Useful Life (RUL) from multi-sensor time series data is difficult through manual inspection. Current machine learning and data analytics methods, for RUL estimation require large number of failed instances for training, which are rarely available in practice, and these methods cannot use information from currently operational censored instances since their failure time is unknown. Embodiments of the present disclosure provide systems and methods for estimating RUL using time series data by implementing an LSTM-RNN based ordinal regression technique, wherein during training RUL value of failed instance(s) is encoded into a vector which is given as a target to the model. Unlike a failed instance, the exact RUL for a censored instance is unknown. For using the censored instances, target vectors are generated and the objective function is modified for training wherein the trained LSTM-RNN based ordinal regression is applied on an input test time series for RUL estimation.


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