Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation entropy (F2CMPE), Laplacian score (LS) and support vector machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on the wavelet packet decomposition. The entropy measure estimates the complexity of time series from both low-and highfrequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional composite multiscale permutation entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification, respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify the bearing fault patterns under different fault states and severity levels.INDEX TERMS Fault detection and diagnosis, fine-to-coarse multiscale permutation entropy, Laplacian score, support vector machine.
Abstract:The paper presents readily implementable approaches for fault detection and diagnosis (FDD) based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are shown to provide robust and user-friendly tools for application to industrial gas turbine (IGT) systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps (CMs) that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.Keywords: Fault detection and diagnosis, hierarchical clustering, self-organizing map neural network. Introduction 1The purpose of fault detection is to automatically generate an 'alarm' or 'flag' to inform operators of impending or developing failure, whilst fault diagnosis aims to identify the location and predict the consequences of the failure [1] . The adoption of 'early warning' systems to identify and localize emerging faults has therefore attracted considerable attention due to the widely-recognized benefits of facilitating reduced down-time and assurance of safety, through the use of fault detection and diagnosis (FDD) [2,3] algorithms.Of the methods previously explored to date, FDD techniques can be broadly divided into three categories viz. knowledge-, model-and signal processing-based approaches [3,4,5] . Knowledge-based approaches often rely on monitoring residuals Manuscript received date; revised date * Corresponding author:Tel: +44 1522 837912; Email:cbingham@lincoln.ac.uk between multiple sensor measurements [6] , however, due to the high number of sensors used on modern industrial gas turbines (IGTs) and other complex industrial systems, the adoption of additional redundant sensors is prohibitively expensive. When using model-based approaches, a virtual sensor (a 'model' by some description) is employed to provide an estimate of expected measurements, from which residuals are then used as an indicator of potential failure modes being present [3] . However, for large IGT systems, which are often custom-designed to meet individual orders, the use of application specific materials and components (for example, to satisfy off-s...
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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