Flexible software for condition monitoring, incorporating novelty detection and diagnostics

Christos Emmanouilidis (Corresponding Author), Erkki Jantunen, John MacIntyre

Research output: Contribution to journalArticleScientificpeer-review

27 Citations (Scopus)

Abstract

Condition monitoring and machinery fault diagnosis are central to the implementation of efficient maintenance management strategies. They can be based on empirical modelling, which aims at associating measured data to machine conditions. Arguably, different monitoring tasks present different challenges to the maintenance engineer. This paper presents the development of a flexible software solution for condition monitoring, novelty identification and machinery diagnostics, which can easily be customised to a wide range of monitoring scenarios. Its main constituents are a number of independent software modules, such as the fault and symptom tree, the fuzzy classification module, the novelty detection and the neural network diagnostics sub-systems. It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.
Original languageEnglish
Pages (from-to)516-527
Number of pages12
JournalComputers in Industry
Volume57
Issue number6
DOIs
Publication statusPublished - 2006
MoE publication typeA1 Journal article-refereed

Fingerprint

Condition monitoring
Monitoring
Machinery
Machine tools
Failure analysis
Neural networks
Engineers

Keywords

  • condition monitoring
  • fault diagnosis
  • novelty detection
  • neural networks
  • machinery
  • machinery diagnostics

Cite this

Emmanouilidis, Christos ; Jantunen, Erkki ; MacIntyre, John. / Flexible software for condition monitoring, incorporating novelty detection and diagnostics. In: Computers in Industry. 2006 ; Vol. 57, No. 6. pp. 516-527.
@article{a09eccf4422c48a68b04dbf3fcecc565,
title = "Flexible software for condition monitoring, incorporating novelty detection and diagnostics",
abstract = "Condition monitoring and machinery fault diagnosis are central to the implementation of efficient maintenance management strategies. They can be based on empirical modelling, which aims at associating measured data to machine conditions. Arguably, different monitoring tasks present different challenges to the maintenance engineer. This paper presents the development of a flexible software solution for condition monitoring, novelty identification and machinery diagnostics, which can easily be customised to a wide range of monitoring scenarios. Its main constituents are a number of independent software modules, such as the fault and symptom tree, the fuzzy classification module, the novelty detection and the neural network diagnostics sub-systems. It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.",
keywords = "condition monitoring, fault diagnosis, novelty detection, neural networks, machinery, machinery diagnostics",
author = "Christos Emmanouilidis and Erkki Jantunen and John MacIntyre",
year = "2006",
doi = "10.1016/j.compind.2006.02.012",
language = "English",
volume = "57",
pages = "516--527",
journal = "Computers in Industry",
issn = "0166-3615",
publisher = "Elsevier",
number = "6",

}

Flexible software for condition monitoring, incorporating novelty detection and diagnostics. / Emmanouilidis, Christos (Corresponding Author); Jantunen, Erkki; MacIntyre, John.

In: Computers in Industry, Vol. 57, No. 6, 2006, p. 516-527.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Flexible software for condition monitoring, incorporating novelty detection and diagnostics

AU - Emmanouilidis, Christos

AU - Jantunen, Erkki

AU - MacIntyre, John

PY - 2006

Y1 - 2006

N2 - Condition monitoring and machinery fault diagnosis are central to the implementation of efficient maintenance management strategies. They can be based on empirical modelling, which aims at associating measured data to machine conditions. Arguably, different monitoring tasks present different challenges to the maintenance engineer. This paper presents the development of a flexible software solution for condition monitoring, novelty identification and machinery diagnostics, which can easily be customised to a wide range of monitoring scenarios. Its main constituents are a number of independent software modules, such as the fault and symptom tree, the fuzzy classification module, the novelty detection and the neural network diagnostics sub-systems. It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.

AB - Condition monitoring and machinery fault diagnosis are central to the implementation of efficient maintenance management strategies. They can be based on empirical modelling, which aims at associating measured data to machine conditions. Arguably, different monitoring tasks present different challenges to the maintenance engineer. This paper presents the development of a flexible software solution for condition monitoring, novelty identification and machinery diagnostics, which can easily be customised to a wide range of monitoring scenarios. Its main constituents are a number of independent software modules, such as the fault and symptom tree, the fuzzy classification module, the novelty detection and the neural network diagnostics sub-systems. It is implemented on two different applications, namely machine tool monitoring and gearbox monitoring.

KW - condition monitoring

KW - fault diagnosis

KW - novelty detection

KW - neural networks

KW - machinery

KW - machinery diagnostics

U2 - 10.1016/j.compind.2006.02.012

DO - 10.1016/j.compind.2006.02.012

M3 - Article

VL - 57

SP - 516

EP - 527

JO - Computers in Industry

JF - Computers in Industry

SN - 0166-3615

IS - 6

ER -