A fast feature selection algorithm applied to automatic faults diagnosis of rotating machinery

Boldt, Francisco de Assis; Rauber, Thomas W.; Varejão, Flávio Miguel

doi:10.4013/jacr.2013.32.02

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…Feature extraction for vibration analysis has been discussed in numerous publications, extensive reviews can be found for instance in (D. Wang et al, 2017;Singh & Vishwakarma, 2015). The extraction of typical statistical features in time domain is described in (Sharma & Parey, 2016;Lei, He, Zi, & Hu, 2007;Shen, Wang, Kong, & Tse, 2013;Decker & Lewicki, 2003;Alattas & Basaleem, 2007;Boldt, Rauber, & Varejão, 2013;Jalil, Butt, & Malik, 2013;Suma & Gurumurthy, 2010;Kollialil, Gopan, Harsha, & Joseph, 2013). Features in time-frequency and frequency domain are proposed and investigated in (Sharma & Parey, 2016;Lei et al, 2007;Alattas & Basaleem, 2007;Boldt et al, 2013).…”

Section: Methods Lcmmentioning

confidence: 99%

“…The extraction of typical statistical features in time domain is described in (Sharma & Parey, 2016;Lei, He, Zi, & Hu, 2007;Shen, Wang, Kong, & Tse, 2013;Decker & Lewicki, 2003;Alattas & Basaleem, 2007;Boldt, Rauber, & Varejão, 2013;Jalil, Butt, & Malik, 2013;Suma & Gurumurthy, 2010;Kollialil, Gopan, Harsha, & Joseph, 2013). Features in time-frequency and frequency domain are proposed and investigated in (Sharma & Parey, 2016;Lei et al, 2007;Alattas & Basaleem, 2007;Boldt et al, 2013). Typical symptom parameters in frequency domain for rotating machinery are extracted in (H. Wang & Chen, 2007).…”

Section: Methods Lcmmentioning

confidence: 99%

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A Comparison of Vibration based Bearing Fault Diagnostic Methods

Ooijevaar

Pichler

et al. 2019

IJPHM

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This paper presents a benchmark study in which three vibration based bearing diagnostic algorithms are compared. The three methods are a data driven approach developed by the Linz Center of Mechatronics (LCM), a physics based method of Flanders Make (FM) and an approach developed by the Center for Intelligent Maintenance Systems (IMS). Two experimental tests have been performed, an accelerated lifetime test to degrade a bearing and introduce an operational bearing fault and a gearbox test containing various faulty test bearings. The methods are compared based on their diagnostic performance, practical applicability, training and configuration requirements. Based on the accelerated lifetime test, it is concluded that the method of IMS and FM, employing bearing specific features, showed to be more sensitive for early bearing fault detection than purely statistical features used in the method of LCM. On the contrary, the method of LCM does not require specific system knowledge and is not limited to bearing monitoring only. The method is more widely applicable to fault monitoring problems. The methods of IMS and LCM seem to outperform the method of FM in the gearbox test. However, the training and testing data used by those methods were acquired for the same bearing sample and for the same bearing assembly. This could lead to a high correlation between the training and testing data and hence a misleading classification accuracy. Therefore, attention should be paid to the quality of the training data. It is concluded that the training data should comprise all relevant system variations, including e.g. remounting of the bearing, to ensure that the classification is uniquely based on bearing fault related effects. The methods of IMS and LCM require validated training data of both healthy and faulty bearing scenarios, whereas the method of FM relies on training data of healthy bearings only. In practice, the availability of training data of faulty bearings is often scarce and could make the adoption more complicated. The findings presented in this paper serve as a guideline to support the selection of an appropriate method for practical applications.

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Section: Methods Lcmmentioning

confidence: 99%

Section: Methods Lcmmentioning

confidence: 99%

A Comparison of Vibration based Bearing Fault Diagnostic Methods

Ooijevaar

Pichler

et al. 2019

IJPHM

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“…The data are then (1) processed, (2) their features are extracted, and (3) analysis/decisions are performed [1]. Possible features include time-domain features [2,3], but usually frequency domain features are used. Approaches typically use a Fourier transform for feature extraction such as in [4,5], but all these assume a known frequency fingerprint to be extracted.…”

Section: Introductionmentioning

confidence: 99%

“…Approaches typically use a Fourier transform for feature extraction such as in [4,5], but all these assume a known frequency fingerprint to be extracted. In order to specifically construct a feature-space that best suits the application some works define their own feature extraction process [3,6].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Data Mining of Families of Rotating Machinery

Hofer

Mohrenschildt

2022

Applied Sciences

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Machines designed to perform the same tasks using different technologies can be organized into families based on their similarities or differences. We are interested in identifying common properties and differences of such machines from raw sensor data for analysis and fault diagnostics. The usual first step is a feature extraction process that requires an understanding of the machine’s harmonics, bearing frequencies, etc. In this paper, we present a model-free path from the raw sensor data to statistically meaningful feature vectors. This is accomplished by defining a transform independent of the operating frequency and performing statistical reductions to identify the components with the largest variances, resulting in a low dimensional statistically meaningful feature space. To obtain an insight into the family relationships we perform a clustering. As the data set has some labeled characteristics we define an entropy-based measure to evaluate a clustering using the a priori-known labels, resulting in a symmetric measurement uniquely defining the clustering goal. Applying this hierarchically we obtain the family tree. The methods are presented can be applied in general situations. As a case study we apply them to a real data set of vibrating screens.

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“…Each of the fault datasets includes two parts: 480 training samples and 800 testing samples, and each sample includes 52 variables. The further intensive introduction of TE process can be found in the work of Boldt et al [36] The TE process is very close to the actual production process, and it has been extensively applied to evaluate different control and diagnostic strategies in recent years.…”

Section: Introduction Of the Te Processmentioning

confidence: 99%

A novel fault classification method using reconstructed distance‐based discriminant locality preserving projection for industrial processes

2022

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Fault detection and classification is a crucial issue in modern industrial processes for ensuring steady operation and high product quality. The process data collected and stored fully reflect the equipment running state and the production process. Moreover, the extracted nonlinear features can directly affect the effectiveness of the data-driven fault classification model. In this paper, a novel fault classification method based on nonlinear feature extraction using reconstructed distance-based discriminant locality preserving projection (RD-DLPP) is proposed. First, a hypersphere model for each class of data is developed according to the spatial structures and classes information in highdimensional space. The hyperspheres are used as indicators to evaluate the discriminatory difficulty of samples. Second, the constraints of the correlations between the k-nearest neighbour points of the sample and the hypersphere are introduced, which can efficiently reconstruct new measure metrics between the sample and its k-nearest neighbour points. Finally, an improved fault classification model based on RD-DLPP is established for the construction of the highly discriminant subspace. The Bayesian decision is then used to classify the samples. The feasibility and efficiency of the proposed method are verified by the Tennessee Eastman process as a case study.

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A fast feature selection algorithm applied to automatic faults diagnosis of rotating machinery

Cited by 4 publications

References 16 publications

A Comparison of Vibration based Bearing Fault Diagnostic Methods

A Comparison of Vibration based Bearing Fault Diagnostic Methods

Model-Free Data Mining of Families of Rotating Machinery

A novel fault classification method using reconstructed distance‐based discriminant locality preserving projection for industrial processes

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