• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
For visually-impairedUser profile (HSE staff only)SearchMenu

Industrial Machine Learning for Fault Detection on Aircraft Engines Sensor Data

Student: Aleksei Pokoev

Supervisor: Ilya Makarov

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Year of Graduation: 2020

Estimation of the remaining useful life of an equipment from sensor time-series data is an important problem in the industrial area. Machine maintenance costs take up a large part of the expense item, especially if it is a complex system of devices. RUL estimation helps to detect faults and repair the equipment in a timely manner, that ensure operational availability of the system. Recently, recurrent neural networks have demonstrated great performance in the considered problem. This paper reviews multiple machine learning methods for RUL estimation. One of the primary objective is to examine neural networks architectures described in methods and compare their effectiveness regarding to different metrics. Furthermore, we propose new models, whose architectures are similar to the considered ones, but based on convolutional layers instead of recurrent one.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses