1,095 research outputs found

    A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis.

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    In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the proposed method selects the fault characteristic coefficients of the Walsh spectrum. In the second stage, statistical features in the time and Walsh spectrum domain are extracted from the selected fault characteristic coefficients of the Walsh transform. These extracted raw statistical features result in a hybrid high-dimensional space. Not all these extracted features help illustrate the condition of the centrifugal pump. To overcome this issue, novel cosine linear discriminant analysis is introduced in the third stage. Cosine linear discriminant analysis is a dimensionality reduction technique which selects similar interclass features and adds them to the illustrative feature pool, which contains key discriminant features that represent the condition of the centrifugal pump. To achieve maximum between-class separation, linear discriminant analysis is then applied to the illustrative feature pool. This combination of illustrative feature pool creation and linear discriminant analysis forms the proposed application of cosine linear discriminant analysis. The reduced discriminant feature set obtained from cosine linear discriminant analysis is then given as an input to the K-nearest neighbor classifier for classification. The classification results obtained from the proposed method outperform the previously presented state-of-the-art methods in terms of fault classification accuracy

    A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

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    Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults

    DATA-DRIVEN TECHNIQUES FOR DIAGNOSING BEARING DEFECTS IN INDUCTION MOTORS

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    Induction motors are frequently used in many automated systems as a major driving force, and thus, their reliable performances are of predominant concerns. Induction motors are subject to different types of faults and an early detection of faults can reduce maintenance costs and prevent unscheduled downtime. Motor faults are generally related to three components: the stator, the rotor and/or the bearings. This study focuses on the fault diagnosis of the bearings, which is the major reason for failures in induction motors. Data-driven fault diagnosis systems usually include a classification model which is supported by an efficient pre-processing unit. Various classifiers, which aim to diagnose multiple bearing defects (i.e., ball, inner race and outer race defects of different diameters), require well-processed data. The pre-processing tasks plays a vital role for extracting informative features from the vibration signal, reducing the dimensionality of the features and selecting the best features from the feature pool. Once the vibration signal is perfectly analyzed and a proper feature subset is created, then fault classifiers can be trained. However, classification task can be difficult if the training dataset is not balanced. Induction motors usually operate under healthy condition (than faulty situation), thus the monitored vibration samples relate to the normal state of the system expected to be more than the samples of the faulty state. Here, in this work, this challenge is also considered so that the classification model needs to deal with class imbalance problem

    Fault Diagnosis and Prognosis of Critical Components

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    Editoria

    Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

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    Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602
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