Flexible software for condition monitoring, incorporating novelty detection and diagnostics

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

    Research output: Contribution to journalArticleScientificpeer-review

    31 Citations (Scopus)


    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
    JournalComputers in Industry
    Issue number6
    Publication statusPublished - 2006
    MoE publication typeA1 Journal article-refereed


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


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