Recent trend in condition monitoring for equipment fault diagnosis

Bhattacharya, Abhisekh; Dan, Pranab K.

doi:10.1007/s13198-013-0151-z

Cited by 18 publications

(3 citation statements)

References 117 publications

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“…4. Early, close proximity, pairings are common with (18,19), (1,10), (5,11), (9,24) and (14,15), the latter two pairs also quickly forming a homogeneous set along with harmonic 12. Pressure envelope harmonic features could be considered as 5 early formed groups (T < 0.2) or 2 groups (T < 0.35).…”

Section: Input Parameter Peductionmentioning

confidence: 99%

“…Condition monitoring (CM) is concerned with preventing, or at the very least predicting, impending component failure. Modern process monitoring is complemented with maintenance on demand, maintenance based on real time observations of operations with respect to expected normal behaviour [1,2] . With increasing pressure on excellence of process performance and product quality, CM becomes ever more vital.…”

Section: Problem Specificationmentioning

confidence: 99%

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An Approach to Reducing Input Parameter Volume for Fault Classifiers

Smith

Ball

2019

Int. J. Autom. Comput.

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As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Naïve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers, demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables. Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Naïve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Naïve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.

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Section: Input Parameter Peductionmentioning

confidence: 99%

Section: Problem Specificationmentioning

confidence: 99%

An Approach to Reducing Input Parameter Volume for Fault Classifiers

Smith

Ball

2019

Int. J. Autom. Comput.

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show abstract

“…The effect of measuring transducers on the acoustic signatures based on AEM has been analyzed by (Faria, Costa, & Olivas, 2015) and the possibility of using AEM for testing of OLTCs during normal operation has been presented by (Cichoń & Borucki, 2012). A review of monitoring methods used in power transformers for predictive maintenance has been presented by (Bhattacharya & Dan, 2014) and recent trends of condition monitoring for fault diagnosis of equipment has been reviewed by (Wang et al, 2015). Fuzzy logic based autodiagnosis of OLTCs has been suggested by (Henriquez & Alonso, 2014;Hu, Duan, & Yong, 2015).…”

Section: Literature Review and Novelty Of Proposed Algorithmmentioning

confidence: 99%