Fault detection and identification method based on multivariate statistical techniques

Fuente, José Luis de la; Garcia-Alvarez, D.; Sainz-Palmero, G.I.; Villegas, T.

doi:10.1109/etfa.2009.5346998

Cited by 12 publications

(7 citation statements)

References 13 publications

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“…Teorema leverages on multivariate statistical analysis for fault detection, as it represents a powerful diagnostics solution [7]. In addition, ice cream machines also record a list of events, including events related to the normal operating conditions of the machines such as commands issued by the machine operators, completion of processes such as pasteurization, etc., and transfer them to the central management station.…”

Section: The Teorema Architecturementioning

confidence: 99%

Teorema: An e-maintenance platform for ice cream machines

Lazzarini¹,

Virgilli²,

Stefanelli

et al. 2011

Etfa2011

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In modern manufacturing, the integration of ICT in the maintenance process, led to the development of emaintenance, that automates management operations. Emaintenance, that initially interested only large plant machinery, is now becoming affordable for mass-produced equipment, thanks to the recent advances in ICT. This paper presents Teorema, an innovative e-maintenance solution for Carpigiani ice cream machines, which provides several services: remote monitoring of machines, automatic notification of malfunctions, diagnostics and prognostics functions, remote assistance interventions, and automated reporting of production data. Teorema is already in production and it is significantly improving the after-sale service to Carpigiani customers.

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Section: The Teorema Architecturementioning

confidence: 99%

Teorema: An e-maintenance platform for ice cream machines

Lazzarini¹,

Virgilli²,

Stefanelli

et al. 2011

Etfa2011

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“…PCA and its variants like dynamic principal component analysis (DPCA), kernel principal component analysis (KPCA) etc., have widely been used for fault detection of industrial systems (see [27,12,19,10,29,34] and references therein). Additionally, integration of PCA with artificial intelligence, for example, neural network PCA (NNPCA) has been utilized for process monitoring [1].…”

Section: Introductionmentioning

confidence: 99%

Exponential discriminant analysis for fault diagnosis

et al. 2016

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“…Because fault diagnosis problems can be considered as classification problems [7], Fisher discriminant analysis (FDA), which is studied in detail in the pattern classification literature, has been applied to conduct fault diagnosis. However, at present, its application has mainly been concentrated to industrial process (especially chemical processes), but rarely to rotating machinery equipment [8][9][10][11]. Moreover, although performance assessment, fault detection, diagnosis, and prognosis have received increased attention with significant progress [12], currently, few methods can realize those purposes alone.…”

Section: Introductionmentioning

confidence: 99%

An approach to performance assessment and fault diagnosis for rotating machinery equipment

Tao

Chuan

et al. 2013

EURASIP J. Adv. Signal Process.

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Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.

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Fault detection and identification method based on multivariate statistical techniques

Cited by 12 publications

References 13 publications

Teorema: An e-maintenance platform for ice cream machines

Teorema: An e-maintenance platform for ice cream machines

Exponential discriminant analysis for fault diagnosis

An approach to performance assessment and fault diagnosis for rotating machinery equipment

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