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The importance of feature processing in deep learning based condition monitoring of motors

The importance of feature processing in deep learning based condition monitoring of motors
The importance of feature processing in deep learning based condition monitoring of motors
The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL-models, DL based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that, they may effectively contribute towards the implementation of DL models as applied to motor condition monitoring.
1024-123X
Harris, Nicholas R.
237cfdbd-86e4-4025-869c-c85136f14dfd
Soother, Dileep Kumar
7a8cf16f-527d-46f1-a03e-26e39ae5312b
Daudpoto, Jawaid
2af54b34-e286-4b62-a082-8be4a5a00763
Hussain, Majid
e7560183-7352-4324-bf46-7578faa4ec9d
Mehran, Sanaullah
00fd95d0-1ac9-4b57-af80-c293093ad312
Kalwar, Imtiaz
7b575643-46e4-4f6d-a097-2e737d0f13a5
Hussain, Tanweer
dbde92c6-6c24-45a3-b4e7-e563afbe3570
Memon, Tayab Din
1abffde4-5d37-4452-bab2-56765eb4f23c
Harris, Nicholas R.
237cfdbd-86e4-4025-869c-c85136f14dfd
Soother, Dileep Kumar
7a8cf16f-527d-46f1-a03e-26e39ae5312b
Daudpoto, Jawaid
2af54b34-e286-4b62-a082-8be4a5a00763
Hussain, Majid
e7560183-7352-4324-bf46-7578faa4ec9d
Mehran, Sanaullah
00fd95d0-1ac9-4b57-af80-c293093ad312
Kalwar, Imtiaz
7b575643-46e4-4f6d-a097-2e737d0f13a5
Hussain, Tanweer
dbde92c6-6c24-45a3-b4e7-e563afbe3570
Memon, Tayab Din
1abffde4-5d37-4452-bab2-56765eb4f23c

Harris, Nicholas R., Soother, Dileep Kumar, Daudpoto, Jawaid, Hussain, Majid, Mehran, Sanaullah, Kalwar, Imtiaz, Hussain, Tanweer and Memon, Tayab Din (2021) The importance of feature processing in deep learning based condition monitoring of motors. Mathematical Problems in Engineering. (In Press)

Record type: Article

Abstract

The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL-models, DL based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that, they may effectively contribute towards the implementation of DL models as applied to motor condition monitoring.

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Accepted/In Press date: 1 May 2021

Identifiers

Local EPrints ID: 448993
URI: http://eprints.soton.ac.uk/id/eprint/448993
ISSN: 1024-123X
PURE UUID: 9410d0c2-0aa1-444a-8c69-943eadd6f6de
ORCID for Nicholas R. Harris: ORCID iD orcid.org/0000-0003-4122-2219

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Date deposited: 12 May 2021 16:49
Last modified: 13 May 2021 01:35

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Contributors

Author: Nicholas R. Harris ORCID iD
Author: Dileep Kumar Soother
Author: Jawaid Daudpoto
Author: Majid Hussain
Author: Sanaullah Mehran
Author: Imtiaz Kalwar
Author: Tanweer Hussain
Author: Tayab Din Memon

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