Journal of Intelligent & Fuzzy Systems - Volume 34, issue 6

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3395-3395, 2018

Authors: Li, Chuan | de Oliveira, José Valente

Article Type: Editorial

DOI: 10.3233/JIFS-169520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3397-3401, 2018

Authors: Chen, Jiayu | Zhou, Dong | Wang, Yang | Fu, Hongyong | Wang, Mingfang

Article Type: Research Article

Abstract:  Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accidents and guaranteeing sufficient maintenance. This paper presents a method based on image processing for fault diagnosis of rotating machinery. Different from traditional methods of signal analysis in the one-dimensional space, this study employs computing methods in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. …First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, histogram of oriented gradient (HOG), is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form the feature vector. In the case study, two typical rotating machineries, gearbox and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. Show more

Keywords: Rotating machinery, bi-spectrum, histogram of oriented gradient (HOG), image processing, fault diagnosis

DOI: 10.3233/JIFS-169521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3403-3412, 2018

Authors: Su, Zuqiang | Xu, Haitao | Luo, Jiufei | Zheng, Kai | Zhang, Yi

Article Type: Research Article

Abstract: This study presents a new manifold learning framework for machinery fault diagnosis, in order to further improve fault diagnosis accuracy. The new manifold learning framework contains two stages: unsupervised manifold learning for nonlinear denoising and supervised manifold learning for feature extraction. Firstly, the nonlinear denoising method with unsupervised manifold learning was introduced, which combined advantages of manifold learning in revealing nonlinear manifold structure as well as advantages of phase space reconstruction in representing spatial distribution of signal and noise. Then, fault feature extraction was carried out according to the frequency spectrum of vibration signals after denoising. In order to reduce …the high dimension and remove redundant information of frequency spectrum, an improved supervised local tangent space alignment (ISLTSA) was proposed to further enlarge diversity of the fault samples and thus increase separability. Finally, the extracted low-dimensional fault features were inputted into a pattern recognition method for fault identification. The effectiveness of the proposed method was verified by studying the fault diagnosis of bearings. Show more

Keywords: Vibration signal, manifold learning, signal denoising, feature extraction, fault diagnosis

DOI: 10.3233/JIFS-169522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3413-3427, 2018

Authors: Wang, Dong | Yi, Cai | Tsui, Kwok Leung

Article Type: Research Article

Abstract: Rolling element bearings are widely used in machinery, such as cooling fan, railway axle, centrifugal pump, transaction motor, gas turbine engine, wind turbine gearbox, etc., to support rotating shafts. Bearing failures will accelerate failures of other adjacent components and finally result in the breakdown of systems. To prevent any unexpected accidents and reduce economic loss, condition monitoring and fault diagnosis of rolling element bearings should be immediately conducted. Ensemble empirical mode decomposition (EEMD) as an improvement on empirical mode decomposition is a data-driven algorithm to adaptively decompose vibration signals collected from the casing of machinery for bearing fault feature extraction …without the requirement of expertise and thus its easy usage attracts much attention in recent years from readers and engineers. The direct applications of EEMD to preprocessing bearing fault signals for intelligent bearing fault diagnosis can be found in lots of publications and conferences every year. However, such applications are not always effective in extracting bearing fault features because the Fourier spectrum of the first intrinsic mode function is too wide and contains many unwanted strong low-frequency periodic components. In this paper, according to results from the analyses of industrial railway axle bearing fault signals, we experimentally show that the direct use of EEMD is not always effective in extracting bearing fault features. Further, to make EEMD more effective, we introduce the concept of blind fault component separation to separate low-frequency periodic vibration components from high-frequency random repetitive transients, such as bearing fault signals. Results show that the idea of blind fault component separation is much helpful in enhancing the effectiveness of EEMD in extracting bearing fault features in the case of industrial railway axle bearing fault diagnosis. Show more

Keywords: Ensemble empirical mode decomposition, intelligent bearing fault diagnosis, fault feature extraction, blind fault component, industrial railway axle

DOI: 10.3233/JIFS-169523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3429-3441, 2018

Authors: Chen, Zhouliang | Li, Zhinong

Article Type: Research Article

Abstract: Based on the deficiency in the traditional fault diagnosis method of rotating machinery, i.e. shallow learning is usually used to characterize complex mapping relationship between vibration signals and the rotor system, a deep neural network (DNN) based on stacked denoising autoencoder (SDAE) is proposed. The proposed method has been successfully applied to the fault diagnosis of rotating machinery. In the proposed method, the frequency domain information of vibration signal is used as input signal, and the deep neural network is obtained by layer-by-layer feature extraction from denoising autoencoder (DAE). Then the dropout method is used to adjust the network …parameters, and reduces the over-fitting phenomenon. In additional, the principal component analysis is used to extract fault features. The experiment result shows that the proposed method is very effective, and can effectively extract the hidden features in the vibration signal of rotating machinery. Show more

Keywords: Stacked denoising autoencoder (SDAE), deep learning, fault diagnosis, rotating machinery

DOI: 10.3233/JIFS-169524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3443-3449, 2018

Authors: Peña, Mario | Cerrada, Mariela | Alvarez, Ximena | Jadán, Diana | Lucero, Pablo | Milton, Barragán | Guamán, Rodrigo | Sánchez, René-Vinicio

Article Type: Research Article

Abstract:  The number of features for fault diagnosis in rotating machinery can be large due to the different available signals containing useful information. From an extensive set of available features, some of them are more adequate than other ones, to classify properly certain fault modes. The classic approach for feature selection aims at ranking the set of original features; nevertheless, in feature selection, it has been recognized that a set of best individually features does not necessarily lead to good classification. This paper proposes a framework for feature engineering to identify the set of features which can yield proper clusters of …data. First, the framework uses ANOVA combined with Tukey’s test for ranking the significant features individually; next, a further analysis based on inter-cluster and intra-cluster distances is accomplished to rank subsets of significant features previously identified. Our contribution aims at discovering the subset of features that discriminates better the clusters of data associated to several faulty conditions of the mechanical devices, to build more robust multi-fault classifiers. Fault severity classification in rolling bearings is studied to verify the proposed framework, with data collected from a test bed under real conditions of speed and load on the rotating device. Show more

Keywords: Feature engineering, ANOVA, cluster validity assessment, KNN, fault diagnosis, bearings

DOI: 10.3233/JIFS-169525

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3451-3462, 2018

Authors: Sánchez, René-Vinicio | Lucero, Pablo | Vásquez, Rafael E. | Cerrada, Mariela | Macancela, Jean-Carlo | Cabrera, Diego

Article Type: Research Article

Abstract:  Gearboxes and bearings play an important role in industries for motion and torque transmission machines. Therefore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage to the machine or human losses; additionally, an appropriate diagnosis contributes to increase productivity and reduce maintenance costs. This paper addresses a methodological framework for the diagnosis of multi-faults in rotating machinery through the use of features rankings. The classification uses K nearest neighbors and random forest, based on the information that comes from the measured vibration signal. Thirty features in time domain are calculated from the vibration signal, twenty-four features commonly used in …fault diagnosis in rotating machinery, and six features are used from the field of electromyography. Feature ranking methods such as ReliefF algorithm, Chi-Square, and Information Gain are used to select the ten most relevant features, the same ones that enter the classifiers. Five databases were used to validate the proposed methodological framework. The results show good accuracy in classification for the five databases; furthermore, in all the databases in the first ten features ranked by the three rankings methods are present at least two nonconventional features. Show more

Keywords: Feature ranking, multi-fault diagnosis, rotating machinery, time features

DOI: 10.3233/JIFS-169526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3463-3473, 2018

Authors: Xie, Jingjing | Wang, Xiaoxue | Liu, Yu | Bai, Yun

Article Type: Research Article

Abstract: Particulate matter (PM) is one of the most significant air pollutants in recent decades that has tremendous negative effects on the ambient air quality and the public health. Accurate PM forecasting provides a possibility for establishing an early warning system. In this paper, a deep feature learning architecture, i.e., autoencoder-based deep belief regression network (AE-based DBRN), is introduced and utilized to forecast the daily PM concentrations (PM2.5 and PM10 ). Prior to establishing this model, Pearson correlation analysis is applied to look for the possible input-output mapping, where the input candidate variables contain seven meteorological parameters and PM concentrations …within one-day ahead, and the output variables are the local PM forecasts. The addressed model was evaluated by the dataset in the period of 28/10/2013 to 31/8/2016 in Chongqing municipality of China. Moreover, two shallow models, feed forward neural network and least squares support vector regression, were employed for the comparison. The results indicate that the AE-based DBRN model has remarkable better performances among the comparison models in terms of mean absolute percentage error (PM2.5 21.092%, PM10 19.474%), root mean square error (PM2.5 8.600μg/m3 , PM10 11.239μg/m3 ) and correlation coefficient criteria (PM2.5 0.840, PM10 0.826). Show more

Keywords: Deep belief regression network, autoencoder, particulate matter, meteorological data, forecasting

DOI: 10.3233/JIFS-169527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3475-3486, 2018

Authors: Jin, Yaqiang | Liu, Zhiliang | Peng, Dandan | Kang, Jinlong | Ding, Jianming

Article Type: Research Article

Abstract: Local defects of rotating machinery give rise to periodic impulses in vibration. To acquire this fault information, many diagnostic methods have been reported in the past decades. Among them, the envelope spectrum analysis is usually used as the final diagnostic tool; however, its success highly depends on the correct informative frequency band selection. The key problem is how to find the correct centre frequency and its related bandwidth associated to the fault. In this paper, a novel method is proposed for selection of the optimal frequency band parameters. This method improves the informative frequency band selection performance with two aspects. …One is that it incorporates the normal data as a health reference, and the other is that an objective indicator that could fuse multidimensional information is proposed. An optimal frequency band can be obtained through this algorithm, and fault mode is then determined via comparing the squared envelope spectrum between the test and normal signals. At the end of this paper, the proposed method is validated on two diagnosis cases and is compared with two of the other diagnostic methods: the conventional envelope analysis and the kurtogram. Though comparison of the results, the validity and superiority of the proposed method have been proven. Show more

Keywords: Frequency band selection, classification, health reference, envelope analysis, fault diagnosis

DOI: 10.3233/JIFS-169528

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3487-3498, 2018