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

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

Article Type: Research Article

Abstract: To overcome the low diagnosis accuracy caused by the scarcity of labeled training samples, a fault diagnosis method was proposed using orthogonal Semi-supervised linear local tangent space alignment (OSSLLTSA) for feature extraction and transductive support vector machine (TSVM) for fault identification. Through extracting the statistical features were extracted from the sub-bands of vibration signals decomposed by wavelet packet decomposition (WPD), the high-dimensional feature set could be obtained. Following that, the improved kernel space distance evaluation method was applied to remove non-sensitive fault features. Then, a semi-supervised manifold learning method (OSSLLTSA) was proposed to reduce the dimensionality of the fault feature …set, and thus to extract fused fault features with high clustering performance. OSSLLTSA overcomes the over-learning of supervised manifold learning and projection aimlessness of unsupervised manifold learning. Finally, the low-dimensional feature set after dimension reduction was inputted into TSVM for fault diagnosis. TSVM was able to completely utilize the fault information contained in unlabelled samples to modify the model, and the trained fault diagnosis model has better generalization ability. The effectiveness of the proposed method was verified based on the case of gearbox fault. Experimental results showed that the proposed method is able to achieve very high fault diagnosis accuracy even when labeled samples were insufficient. Show more

Keywords: Fault diagnosis, semi-supervised manifold learning, gearbox, transductive support vector machine

DOI: 10.3233/JIFS-169529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3499-3511, 2018

Authors: Jiang, Hongkai | Shao, Haidong | Chen, Xinxia | Huang, Jiayang

Article Type: Research Article

Abstract: It is a great challenge to accurately and automatically identify different faults of the key components in rotating machinery. In this paper, a new method called feature fusion deep belief network is proposed for the intelligent fault diagnosis of rolling bearing. Firstly, a deep belief network (DBN) is constructed with several pre-trained restricted Boltzmann machines for feature learning of the raw vibration data. Secondly, locality preserving projection (LPP) is adopted to fuse the deep features to further enhance the quality of the learned deep features. Finally, the fusion deep features are fed into Softmax for automatic and accurate fault …diagnosis. The proposed method is applied to analyze the experimental rolling bearing signals, and the results show that the proposed method is more effective than the traditional intelligent diagnosis methods. Show more

Keywords: Deep belief network, feature fusion, intelligent fault diagnosis, rotating machinery, locality preserving projection

DOI: 10.3233/JIFS-169530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3513-3521, 2018

Authors: Li, Xuejiao | Ren, Yongmei | Tan, Xiaoyong

Article Type: Research Article

Abstract: Reliable degradation prognosis of mechanical components is very important for condition-based maintenance to improve the reliability and reduce the cost of maintenance. This paper reports the development of a fuzzy feature fusion and multimodal regression method for the degradation prognosis of mechanical components. Initially, the raw features from the vibration signals of the mechanical components are extracted. A degradation index is subsequently yielded by merging the obtained features through/using the fuzzy fusion technique. The ensemble empirical mode decomposition is then introduced to decompose the fusion index into several multimodal sub-series to acquire more detailed information. Extreme learning machines are established …to predict the sub-series in different modes. The predicted results are obtained by integrating the multimodal sub-results. The reported approach was evaluated with real data from a rolling element bearing. Moreover, two peer models were imported to validate the effectiveness of the proposed method. The experimental results indicate that the reported approach is capable of erecting the degradation index reflecting the bearing degradation and that it had better performance in the remaining useful life prediction than the peer methods. Show more

Keywords: Degradation prognosis, fuzzy fusion, degradation index, ensemble empirical mode decomposition, extreme learning machine

DOI: 10.3233/JIFS-169531

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3523-3533, 2018

Authors: Wang, Benkuan | Chen, Yafeng | Liu, Datong | Peng, Xiyuan

Article Type: Research Article

Abstract: On-line anomaly detection is critical for the safety of unmanned aerial vehicles (UAVs). However, the flight status assessment still depends on ground control stations, which cannot meet the time requirement for autonomous and safe flight. The lack of on-board intelligent anomaly detection systems makes it rather difficult for on-line flight status estimation and assessment. In order to achieve real-time monitoring of UAV flight status and enhance the reliability and safety of UAVs, an embedded intelligent system is designed to address the challenging issues of UAV on-line anomaly detection in this paper. During the flight, the status of sensors and key …components are continuously detected via flight data which can reflect the current status of the UAV. The proposed embedded anomaly detection system includes two main parts: (1) a general heterogeneous computing architecture which is based on Xilinx Zynq-7000 SoC with dual-core Cortex A9 processors and Field Programmable Gate Arrays (FPGA), (2) an on-line anomaly detection intelligent algorithm which is based on least squares support vector machine (LS-SVM) prediction model and utilized as a demonstration that needs high computing performance. The simulation flight data are used to verify the proposed system, and the experimental results show that the proposed intelligent system is capable of effective UAV on-line anomaly detection. Show more

Keywords: Unmanned aerial vehicle (UAV), anomaly detection, on-line, LS-SVM

DOI: 10.3233/JIFS-169532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3535-3545, 2018

Authors: Shi, Juanjuan | Jiang, Xingxing | Liang, Ming | Ding, Rongmei | Zhu, Zhongkui

Article Type: Research Article

Abstract: Bearing fault diagnosis under variable speed often faces two obstacles: a) blurry time frequency representation (TFR) and thus ambiguous and even unattainable instantaneous frequency (IF) for resampling, and b) complicated and error-prone resampling processes. To address such problems, this paper proposes a new tacholess and resampling-free method for bearing fault diagnosis under variable speed conditions. This method consists of two main steps: a) extract an accurate IF from the vibration data following a dual pre-IF integration strategy and a regional peak search algorithm to search the frequency bins point by point at local frequency regions, and b) with the accurate …IF estimator (either shaft IF, instantaneous fault characteristic frequency (IFCF) or their harmonics), multi-demodulate the signal and superpose the resulting frequency spectra of all demodulated signal components using an order peak highlighting method. Then, the instantaneous frequency order (IFO) of signal components of interest contained in the original signal can be highlighted and the IFO spectra can be obtained for bearing fault diagnosis under variable speed conditions. In this manner, the bearing fault can be diagnosed without tachometer devices and resampling procedure. Therefore, the proposed method can substantially reduce human involvement and facilitate its implementation in a fault detection expert system. The effectiveness of the proposed method is validated using both simulated and experimental data. Show more

Keywords: Time-varying speed operation, integration strategy, bearing fault diagnosis, generalized demodulation, resampling-free method

DOI: 10.3233/JIFS-169533

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3547-3563, 2018

Authors: Li, Chuan | Cerrada, Mariela | Cabrera, Diego | Sanchez, René Vinicio | Pacheco, Fannia | Ulutagay, Gözde | Valente de Oliveira, José

Article Type: Research Article

Abstract: Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays an important role as it can be used in fault detection, classification, and prognosis. A variety of clustering algorithms have been proposed and applied in this context. However, when the extensive literature on this topic is investigated, it is not clear …which clustering algorithm is the most suitable, if any. In an attempt to bridge this gap, in this study four representative fuzzy clustering algorithms are compared under the same experimental realistic conditions: fuzzy c-means (FCM), the Gustafson-Kessel algorithm, FN-DBSCAN, and FCMFP. The study considers only real-world bearing vibration data coming from both a benchmark data set (CWRU) and from a lab setup where interference between bearing faults can be studied. The comparison takes into account the quality of the generated partitions measured by the external quality (Rand and Adjusted Rand) indexes. The conclusions of the study are grounded in statistical tests of hypotheses. Show more

Keywords: Bearing, fault detection, fault diagnosis, fault classification, fuzzy rules, fuzzy clustering, FCM, Gustafson-Kessel clustering, FCMFP, FN-DBSCAN

DOI: 10.3233/JIFS-169534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3565-3580, 2018

Authors: Cerrada, Mariela | Sánchez, René-Vinicio | Cabrera, Diego

Article Type: Research Article

Abstract: Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. In real industrial applications, a Machine Learning based Classifier (ML-C) analyses data from a current machinery condition to detect abnormal behaviours. Usually, this is achieved through a previous training of the ML-C model, under supervised learning; however, for new machinery conditions, the classifier is not able to correctly identify these new condition. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that could be associated to new faults. The framework relies on an algorithm to build evolving models in simultaneous scenarios of …classification and clustering. The design is inspired by the main principles of the K-means and the One Nearest Neighbour (1-NN) algorithms. A heuristic metric is defined to analyse the new discovered clusters; as a result, these new clusters can be labelled as new classes corresponding to new faulty patterns. Once a new pattern is identified, the associated data feeds a dedicated supervised classifier which is updated through a new training phase. The proposed framework is tested on data collected from a gearbox test bed under realistic conditions of faults. Experimental results show that the algorithm is able to discover new valuable knowledge than can be identified as new faulty classes. Show more

Keywords: Knowledge discovery, machine learning, semi-supervised learning, fault detection, fault diagnosis, gearboxes

DOI: 10.3233/JIFS-169535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3581-3593, 2018

Authors: Duan, Lixiang | Wang, Xuduo | Xie, Mengyun | Yuan, Zhuang | Wang, Jinjiang

Article Type: Research Article

Abstract: Machine learning is widely used for fault diagnosis research. In general, most models used for fault diagnosis are based on the same data distribution, whereas applying equipment to practical productions and operations are mostly done under variable conditions. This often produces changes in data distribution and makes the model unavailable. As one of the most commonly used pieces of equipment in industry, a reciprocating compressor operates under various operating conditions (e.g., variable speed), which may produce changes in data distribution. Thus, the current model established under stable conditions is no longer applicable for fault diagnosis under variable conditions. To solve …this problem of variable conditions, a model should be established that 1) reduces the differences caused by different operating conditions as much as possible, and 2) learns representative fault features under different working conditions. Thus, a new strategy that employs an auxiliary model is proposed that combines a convolutional neural network (CNN) and a marginalized stacked denoising autoencoder (mSDA). In our method, 1) the pre-training model CNN is used for feature learning, and 2) the learned features are transformed by mSDA to eliminate data distribution differences between different conditions. A statistical measure based on kernel maximum mean discrepancy is used to evaluate the differences across different domains. Experimental results of a reciprocating compressor under different operating conditions demonstrate that the proposed method can learn class sensitive features and eliminate differences with changing working conditions. It also obtains higher classification accuracy for reciprocating compressor diagnosis under different working conditions. Show more

Keywords: Auxiliary model, domain adaptation, reciprocating compressor, fault diagnosis, variable conditions

DOI: 10.3233/JIFS-169536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3595-3604, 2018

Authors: Medina, Ruben | Alvarez, Ximena | Jadán, Diana | Macancela, Jean-Carlo | Sánchez, René–Vinicio | Cerrada, Mariela

Article Type: Research Article

Abstract: Fault detection in rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. Signal processing based fault detection is usually performed by considering classical techniques for alternative representation of significant signals in time domain, frequency domain or time-frequency domain. An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed. A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed. A dictionary is learned by using a training set of signals from the normal case. This dictionary …is used for obtaining the sparse representation of signals in the test set and the norm metric is used to measure the residual from the sparse representation. The extracted features are useful for machine learning based fault detection. The analysis is performed considering different load conditions. ANOVA statistical analysis shows that there are significant differences between features in the normal case and each of the faulty classes, and best ranked features form well separated clusters. An experiment of fault classification is developed using a support vector machine for multi-class classification of faults. The accuracy obtained is 95.1% in the cross-validation testing. Show more

Keywords: Dictionary learning, sparse representation, vibration signal, gearbox fault, feature extraction

DOI: 10.3233/JIFS-169537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3605-3618, 2018

Authors: Wang, Xin | Qin, Yi | Zhang, Aibing

Article Type: Research Article

Abstract: A planetary gearbox is a crucial but failure-prone component in rotating machinery, therefore an intelligent and integrated approach based on impulsive signals, deep belief networks (DBNs) and feature uniformation is proposed in this paper to achieve real-time and accurate fault diagnosis. Since the gear faults usually generate the repetitive impulses, an integrated approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding is applied to extract impulse components from original signals. Then time-domain features and frequency-domain features are calculated by both original signals and impulsive signals, and probability density functions are applied to study the sensitivities of the features …to the faults. The extracted features are fed into DBNs to identify the fault types, and the results show that the DBN-based fault diagnosis method is feasible and the impulsive signals play a positive role to improve the accuracies. Finally, by the mean value of various signals under multiple load conditions, uniformed time-domain features are constructed to reduce the interference of loads, and the experimental results validate that feature uniformation can improve the accuracies and robustness of intelligent fault diagnosis approach. Show more

Keywords: Deep belief networks (DBNs), impulsive signals, feature uniformation, mixed load condition, fault diagnosis

DOI: 10.3233/JIFS-169538

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3619-3634, 2018