Machine fault detection by signal denoising—with application to industrial gas turbines

Zhang, Yu; Bingham, C.M.; Yang, Zhijing; Ling, Bingo Wing‐Kuen; Gallimore, Michael

doi:10.1016/j.measurement.2014.08.020

Cited by 42 publications

(13 citation statements)

References 30 publications

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“…However, to build an accurate dynamic model that can accommodate the full operating envelope of IGTs is, in general, computationally demanding. In such circumstances, direct signal processing and data fusion methods often provide for more practical and effective monitoring solutions [6]. It is this latter category of techniques that is considered here.…”

Section: Introductionmentioning

confidence: 99%

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Zhang

Bingham

Martínez-García

et al. 2017

International Journal of Rotating Machinery

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This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.

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Section: Introductionmentioning

confidence: 99%

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Zhang

Bingham

Martínez-García

et al. 2017

International Journal of Rotating Machinery

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“…Apart from that, wavelet packets Corresponding author: Lei Shu (email: lshu@lincoln.ac.uk) decomposition (WPD) benefits from effectively decomposing frequency bands into detail and approximate coefficients with multi-levels [6], which has been applied in [7], [8]. The advent of EMD, proposed by Huang and Wu, provides a powerful self-adaptive signal processing method for analyzing nonstationary and non-linear signals by decomposing the signals into a set of intrinsic mode functions (IMFs) [9]- [11], which are determined by the signal itself rather than the pre-defined kernels compared with wavelet analysis. Generally, after signal decomposition, desired coefficient vectors and IMFs can be further selected to characterize fault symptoms hidden in machine's signals measured from different conditions of rotating shafts.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of WPD and EMD for shaft fault diagnosis

Huo

Shu

2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

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Abstract-Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) on fault diagnosis of rotating shafts using multiscale entropy (MSE). After WPD and EMD, the most sensitive reconstruction vectors and intrinsic mode functions (IMFs) are selected using Shannon entropy. Then, these feature vectors are fed into support vector machine (SVM) for fault classification, where the entropy features represent the complexity of vibration signals with different scales. Experimental results have demonstrated that WPD combined with MSE can achieve an accuracy of 97.3% for crack fault detection in rotating shafts, whilst EMD combined with MSE has shown a higher detection rate of 98.5%.

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“…4 Because of their selective nature, such techniques are regularly considered as underpinning methods in wider application¯elds of fault/anomaly detection, pattern recognition and classi¯cation systems. 5,6 Many linear FE techniques have been reported and successfully applied. The most established being Principal Component Analysis (PCA), 7 which accomplishes FE by searching for a subset of orthogonal linear combinations of the original data with the greatest variances, i.e., the principal components.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Hierarchical Clustering — Piecewise Aggregate Approximation, with Applications

Gallimore

Bingham

Chen

et al. 2016

Int. J. Comp. Intel. Appl.

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PublishedPiecewise Aggregate Approximation (PAA) provides a powerful yet computationally e±cient tool for dimensionality reduction and Feature Extraction (FE) on large datasets compared to previously reported and well-used FE techniques, such as Principal Component Analysis (PCA). Nevertheless, performance can degrade as a result of either regional information insu±ciency or over-segmentation, and because of this, additional relatively complex modi¯cations have subsequently been reported, for instance, Adaptive Piecewise Constant Approximation (APCA). To recover some of the simplicity of the original PAA, whilst addressing the known problems, a distance-based Hierarchical Clustering (HC) technique is now proposed to adjust PAA segment frame sizes to focus segment density on information rich data regions. The e±cacy of the resulting hybrid HC-PAA methodology is demonstrated using two application case studies on non-timeseries data viz. fault detection on industrial gas turbines and ultrasonic biometric face identi¯-cation. Pattern recognition results show that the extracted features from the hybrid HC-PAA provide additional bene¯ts with regard to both cluster separation and classi¯cation performance, compared to traditional PAA and the APCA alternative. The method is therefore demonstrated to provide a robust readily implemented algorithm for rapid FE and identi¯cation for datasets.

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Machine fault detection by signal denoising—with application to industrial gas turbines

Cited by 42 publications

References 30 publications

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

A comparative study of WPD and EMD for shaft fault diagnosis

Hybrid Hierarchical Clustering — Piecewise Aggregate Approximation, with Applications

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