Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor
Li, X, Duan, F, Loukopoulos, P, Bennett, I and Mba, D (2018). Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor. Control Engineering Practice. 72, pp. 177-191.
|Authors||Li, X, Duan, F, Loukopoulos, P, Bennett, I and Mba, D|
© 2017 Centrifugal compressors are widely used for gas lift, re-injection and transport in the oil and gas industry. Critical compressors that compress flammable gases and operate at high speeds are prioritized on maintenance lists to minimize safety risks and operational downtime hazards. Identifying incipient faults and predicting fault evolution for centrifugal compressors could improve plant safety and efficiency and reduce maintenance and operation costs. This study proposes a dynamic process monitoring method based on canonical variable analysis (CVA) and long short-term memory (LSTM). CVA was used to perform fault detection and identification based on the abnormalities in the canonical state and the residual space. In addition, CVA combined with LSTM was used to estimate the behavior of a system after the occurrence of a fault using data captured from the early stages of deterioration. The approach was evaluated using process data obtained from an operational industrial centrifugal compressor. The results show that the proposed method can effectively detect process abnormalities and perform multi-step-ahead prediction of the system's behavior after the appearance of a fault.
|Keywords||0102 Applied Mathematics; 0906 Electrical And Electronic Engineering; 0913 Mechanical Engineering; Industrial Engineering & Automation|
|Journal||Control Engineering Practice|
|Journal citation||72, pp. 177-191|
|Digital Object Identifier (DOI)||doi:10.1016/j.conengprac.2017.12.006|
|01 Mar 2018|
|Publication process dates|
|Deposited||30 Oct 2018|
|Accepted||16 Dec 2017|
Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor.pdf
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