Applications of Fault Diagnosis techniques for a multishaft centrifugal compressor

Zanoli, Silvia Maria; Astolfi, Giacomo; Barboni, L.

doi:10.1109/med.2010.5547615

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…Several approaches for selecting the optimal number of PCs have been developed. The approach followed in this paper is based on the ANalysis Of Variance, ANOVA test (see [20,21]), that was proven to be a reliable and objective method.…”

Section: Theoretical Background On Pcamentioning

confidence: 99%

“…Accordingly, with the PCA model, the measurement space has been partitioned into two orthogonal spaces: the principal component subspace, which includes data variations according to the principal component model, and the residual subspace, which includes data variation not explained by the model. Applying the ANOVA procedure developed by the authors (see [20,21]), the dimension of the PC subspace is set to four. In order to verify if the selected number of PCs is adequate to correctly explain the original system's variables, the signals of the original variables are compared with the reconstructed ones.…”

Section: Fault Detection and Isolation Systemmentioning

confidence: 99%

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Application of a Fault Detection and Isolation System on a Rotary Machine

Zanoli

Astolfi

2013

International Journal of Rotating Machinery

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The paper illustrates the design and the implementation of a Fault Detection and Isolation (FDI) system to a rotary machine like a multishaft centrifugal compressor. A model-free approach, that is, the Principal Component Analysis (PCA), has been employed to solve the fault detection issue. For the fault isolation purpose structured residuals have been adopted while an adaptive threshold has been designed in order to detect and to isolate the faults. To prove the goodness of the proposed FDI system, historical data of a nitrogen centrifugal compressor employed in a refinery plant are considered. Tests results show that detection and isolation of single as well as multiple faults are successfully achieved.

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Section: Theoretical Background On Pcamentioning

confidence: 99%

Section: Fault Detection and Isolation Systemmentioning

confidence: 99%

Application of a Fault Detection and Isolation System on a Rotary Machine

Zanoli

Astolfi

2013

International Journal of Rotating Machinery

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“…This increasing complexity of modern machinery aggravates the quality of diagnosis, forcing a shift from a model-based approach to a data-based approach for equipment monitoring and diagnose of failures [11]. As a result, collecting operating and sensor information, from a machine while it is working, has become of the utmost importance to detect anomalies that might manifest a future component failure.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Palacín

Gibert

Planes

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Predicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors’ data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance.

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“…[14][15][16][17][18][19][20], In Ref. [21], principle component analysis (PCA) was used for compressor fault detection. If the number of variables in the reduced-order model is not selected properly, then it cannot perverse original model specifications; therefore, analy sis of variance for selecting a proper number of variables was sug gested.…”

Section: Introductionmentioning

confidence: 99%

Stable and Efficient Operation of Gas Compressor With Improving of Surge Detection System

Alavinia

Sadrnia

Khosrowjerdi

et al. 2014

Journal of Engineering for Gas Turbines and Power

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This paper investigates the application of fault diagnosis (FD) approach for improving performance of compressors within exact operating point determination. Detecting of sensor fault or failure status is more important in the compressor for safety-critical appli cation. No work has previously been reported on the use of the FD system within a com pressor surge-suppressing system. Therefore, the main contribution of this paper is presenting different and complementary techniques for surge-suppressing studies via sen sor FD. By data acquisition from a nonlinear Moore-Greitzer model, a neural network (NN) and innovation complex decision logic provide residual generation and evaluation blocks in an analytical redundancy FD system, respectively. The proposed FD deals with the most-common sensor faults and failures in seven different scenarios according to their nature, such as bias, cutoff, loss of efficiency, and freeze.

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Applications of Fault Diagnosis techniques for a multishaft centrifugal compressor

Cited by 14 publications

References 10 publications

Application of a Fault Detection and Isolation System on a Rotary Machine

Application of a Fault Detection and Isolation System on a Rotary Machine

Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Stable and Efficient Operation of Gas Compressor With Improving of Surge Detection System

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