Fault Detection and Localization of Centrifugal Gas Compressor System Using Fuzzy Logic and Hybrid Kernel-SVM Methods

Nail, Bachir; Hadroug, Nadji; Hafaifa, Ahmed; Kouzou, Abdellah

doi:10.1007/978-981-15-1746-4_7

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples of challenging topics are speed regulation [ 50 ], robust stabilization [ 51 ], and surge avoiding [ 52 , 53 , 54 , 55 , 56 ]. In addition to these topics, FDI systems on centrifugal compressors have also proven to be a very useful tool [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Time series analysis is combined with neural network in [ 57 ] to perform a correct fault diagnosis on centrifugal compressors.…”

Section: Introductionmentioning

confidence: 99%

“…A fault detection case study using this method is described, e.g., vibration variation due to abnormal opening of an anti-surge control valve. A strategy based on the method of hybrid Kernel-SVM method is adopted in [ 66 ]; here, a fault detection and localization strategy applied to a centrifugal gas compressor system is proposed which exploits experimental data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery

Zanoli,

Pepe

2023

Sensors

View full text Add to dashboard Cite

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

show abstract