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

Authors: Liu, Kuijian | Feng, Yunwen | Xue, Xiaofeng

Article Type: Research Article

Abstract: In order to solve the problems that traditional diagnostic method is heavily dependent on the signal processing techniques and expert experience, and the diagnostic accuracy is difficult to have big improvement anymore with the accumulation of operational data, which cannot meet the needs of fault diagnosis in the big data age, a multi-source signals feature fusion method by deep learning model is proposed in this paper. The stacked denoising autoencoders (SDAE) is used to extract the abstract and deep features from original features, and then locality preserving projections (LPP) is used for dimensionality reduction to complete the feature fusion. Finally, …the fused low-dimensional features act as inputs to the support vector machine (SVM) to realize the failure detection and fault location of typical fault modes of the landing gear hydraulic retraction system. The inhibitory effect of the feedback control on the incipient fault is discussed as well. Moreover, a severity assessment method is presented considering the gradual degradation of leakage fault of the actuator. The diagnostic results show that the proposed method has a better feature fusion ability and higher diagnostic accuracy. The health assessment model can evaluate the health state of the actuator. The significance of this paper is to provide a feasible idea for the fault diagnosis of the landing gear hydraulic retraction system and health assessment of the actuator. Show more

Keywords: Fault diagnosis, feature fusion, hydraulic retraction system, multi-source signals, health assessment

DOI: 10.3233/JIFS-169539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3635-3649, 2018

Authors: Meng, Jing | Zhao, Liye | Shen, Fei | Yan, Ruqiang

Article Type: Research Article

Abstract: Vibration signals generated from gears often exhibit nonlinearity. Characterization of such signals using nonlinear time series analysis can be a good alternative for identifying gear faults. This paper presets a recurrence network based approach to extract features from vibration signals for gear fault diagnosis. Quantitative parameters (such as mean degree centrality, global clustering coefficient, assortativity of the recurrence network, or network entropy) related to the dynamical complexity of the vibration signals are calculated from the generated recurrence network to help classify different gear faults with two kinds of classifiers, i.e., support vector machine and extreme learning machine. Experimental studies performed …on two different gear test systems have verified the effectiveness of the presented recurrence network approach for gear fault severity evaluation, as well as gear fault classification. Show more

Keywords: Nonlinear time series, recurrence network, isolation rate, fault diagnosis

DOI: 10.3233/JIFS-169540

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3651-3660, 2018

Authors: Sun, Quan | Wang, Youren | Jiang, Yuanyuan | Shao, Liwei

Article Type: Research Article

Abstract: Condition monitoring is an effective methodology to evaluate the health state of power electronics converters. Aiming at multiple devices health state estimation for boost converters, a non-invasive condition monitoring technique is proposed in this paper. Taking the equivalent circuit model of these components into consideration, the formulations of failure precursors with detection signals are derived based on hybrid system theory. Then, the parameter identification problem is translated into an objective function optimization issue. Therefore, the precursor parameter values of inductor, capacitor, diode and power MOSFET can be obtained using crow search algorithm. Meanwhile, the boost converters under variable operating conditions …are also analyzed. Compared with particle swarm optimization (PSO) method, both simulations and experiments are conducted to validate the effectiveness of the presented approach. The results show that these parameters can be estimated simultaneously and the identification accuracy of them reaches to more than 90%. Show more

Keywords: Condition monitoring, boost converter, parameter estimation, crow search algorithm, failure precursor

DOI: 10.3233/JIFS-169541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3661-3670, 2018

Authors: Liao, Yixiao | Zhang, Lei | Li, Weihua

Article Type: Research Article

Abstract: Machine learning based intelligent diagnosis methods can adaptively generate the fault diagnosis model by historical data, which have attracted much attention. Artificial neural network (ANN) is one of the most important tools for gearbox intelligent diagnosis. However, the training of ANN has the problem of local optima, and it is hard to determine the ANN structure. These problems have great influence on the diagnosis performance of ANN. In this paper, a variable neural network (RegPSOVNN) is proposed for gearbox fault diagnosis based on regrouping particle swarm optimization. Ten time-domain features are selected to form the input of the ANN. Regrouping …particle swarm optimization (RegPSO) is utilized for the optimization of ANN structure and network training. It can simultaneously optimize the structure and parameters of ANN and effectively avoid the problem of local optima. To evaluate the diagnosis performance of the proposed method, gearbox failure experiments were conducted, and backpropagation neural network (BPNN), firefly variable neural network (FAVNN) and particle swarm optimization based variable neural network (PSOVNN) were used for comparison. Experimental results indicated that the proposed method can effectively optimize the network structure and diagnosis the gearbox faults. Show more

Keywords: Gearbox fault diagnosis, regrouping particle swarm optimization, neural network, variable

DOI: 10.3233/JIFS-169542

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3671-3680, 2018

Authors: Wang, Bing | Wang, Wei | Hou, Meihui | Hu, Xiong

Article Type: Research Article

Abstract: In allusion to performance degradation condition recognition issue for rolling bearing, a method based on improved pattern spectrum entropy (abbreviated as IPSE ) and fuzzy C-means algorithm (abbreviated as FCM ) is proposed in this paper. Basic pattern spectrum analysis is improved by introducing morphological corrosion operator and IPSE is proposed as the degradation feature parameter in describing bearing performance degradation degree. Simulation analysis shows that IPSE value will increase monotonously along with the deepening of the degradation degree. IPSE and degradation degree has a stable relevance. On this basis, in consideration of the fuzzy character of …performance degradation condition boundary, FCM is introduced in degradation condition recognition so that the degradation condition could be recognized effectively in line with maximum subordination degree principle. Rolling bearing fatigue life enhancement testing was carried out in Hangzhou Bearing Test & Research Center, the whole life data was gathered and applied using the proposed technique. The classification coefficient reaches 0.9849 and average fuzzy entropy gets 0.0239 for training set clustering, meanwhile, the whole recognition ratio reaches 90% for testing set. The analysis shows that the technique has a good clustering effect and an acceptable recognition result. Show more

Keywords: Degradation feature extraction, mathematics morphology, fuzzy c-means, degradation condition recognition, rolling bearing

DOI: 10.3233/JIFS-169543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3681-3693, 2018

Authors: Pei, Hong | Si, Xiaosheng | Hu, Changhua | Wang, Zhaoqiang | Du, Dangbo | Pang, Zhenan

Article Type: Research Article

Abstract: As the essential component of prognostic and health management (PHM), life prediction for equipment plays a more and more significant role in recent years. However, current studies cannot fully consider the influence of imperfect maintenance activities that the equipment may experience on the degradation process and prognostic result. In this paper, we propose a degradation model subjected to the influence of imperfect maintenance for life prediction. Firstly, the multi-stage Wiener process is employed to characterize the influence of imperfect maintenance activities on the degradation level and degradation rate. Then, the theoretical expression of life probability distribution is derived under …the concept of first hitting time using the convolution operation, and the approximate expression of life probability distribution is evaluated by the Monte Carlo simulation algorithm. Furthermore, we utilize the maximum likelihood estimation (MLE) to estimate unknown parameters in the concerned model. Finally, a numerical example and a practical case study are provided to substantiate the practicality and effectiveness of the newly proposed life prediction method. The results indicate that the proposed model can guarantee that the relative error (RE) is almost below 5%. Show more

Keywords: Life prediction, imperfect maintenance, multi-stage Wiener process, Monte Carlo, maximum likelihood estimation

DOI: 10.3233/JIFS-169544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3695-3705, 2018

Authors: Sun, Fuqiang | Wang, Ning | Li, Xiaoyang | Cheng, Yuanyuan

Article Type: Research Article

Abstract: Accelerated degradation testing (ADT) has been widely used to accelerate failure/degradation processes and to quickly evaluate the reliability and lifetime of products. In particular, the application of copula function provides a convenient and efficient way to model the ADT data of products that have two or more s-dependent degradation measures. However, little effort has focused on the pointwise infimum and supremum of the multivariate joint-distribution function. For this paper, a novel prognostics method was developed for bivariate ADT data on the basis of Brownian motion and time-varying copula method, which can estimate the pointwise best-possible bounds on bivariate joint reliable …life function with a given measure of association, such as Kendall’s τ or Spearman’s ρ . The proposed model is applied to the real ADT data of microwave assembly to illustrate its performance and effectiveness. Show more

Keywords: Prognostics, ADT, s-dependency, time-varying copula, reliable life bounds

DOI: 10.3233/JIFS-169545

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3707-3718, 2018

Authors: Wang, Yanxue | Li, Huaxin | Yang, Jianwei | Yao, Dechen

Article Type: Research Article

Abstract: Roller bearings are among the most frequently encountered components in the majority of rotating machines. Thus, prognostic and health management of roller bearing plays an important role on the working conditions of the machine. Remaining useful life prediction is one of keys to apply PHM for practical applications. The collected bearing vibration signals are generally non-linear and non-stationary. However, those auto-regression model based methods are only suitable for the prediction of linear and stationary time series. Moreover, most of the existing machine learning based techniques require considerable training and parameter tunings which are time consuming and difficult for practical applications. …To overcome these issues, a novel remaining useful life prediction method for rolling bearing prognostics is proposed in this work based on the sparse coding and sparse linear auto-regression model without training and parameter tunings. Sparse coding is formulated as a basis pursuit L 1 -norm problem, where a sparse set of weight can be estimated for each test vector. Sparse local linear and neighbor embedding are employed to construct the proposed weight constraint sparse coding method. Two different experimental validations are conducted to well demonstrate the effectiveness and robustness of the proposed method for remaining useful life prediction of bearing via root-mean-square, peak-to-peak and kurtosis indicators in time-domain. Show more

Keywords: Prognostic and health management, trend prediction, remain useful life, sparse coding, roller bearing

DOI: 10.3233/JIFS-169546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3719-3733, 2018

Authors: Cheng, Zhiwei | Cai, Bin

Article Type: Research Article

Abstract: Predicting the remaining useful life (RUL) of rolling element bearings (REBs) has emerged as a vital technique for guaranteeing the safety, availability, and efficiency of rotating machinery systems. An approach using locally linear fusion regression (LLFR) is developed for the RUL prediction of REBs. The original features, derived from the time domain and time– frequency domain of the vibration signal of the REBs, are extracted first. Utilizing locally linear embedding, the extracted features are then fused into a condition indicator reflecting the entire degradation process. The adaptive network-based fuzzy inference system is then introduced for the RUL prediction. The reported …approach is investigated with real REB data. Peer models are employed to validate the performance of the proposed method in this work. The derived experimental results indicate that LLFR has superior prediction ability as compared to the peer models in terms of the introduced performance criteria and that it can obtain more reliable and precise prediction results. Show more

Keywords: Remaining useful life, multi-feature fusion, regression, locally linear embedding, rolling element bearings

DOI: 10.3233/JIFS-169547

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3735-3746, 2018

Authors: Tang, Gang | Zhang, Yao | Wang, Huaqing

Article Type: Research Article

Abstract: The prediction of performance degradation is significant for the health monitoring of rolling bearing, which helps to greatly reduce the loss caused by potential faults in the entire life cycle of rotating machinery. As a new method of machine learning based on statistical learning theory, a so-called multivariable least squares support vector machines (LS-SVM) was developed. However, it is unsatisfactory for the prediction of performance degradation without adequate consideration of time variation and data volatility, which are notable features of the obtained time series signal from bearings. To overcome these problems, a new multivariable LS-SVM with a moving window over …time slices is proposed. In this model, different features over time slices are extracted through a moving window to construct new sample pairs according to the embedding theory. The model adaptability is also improved through an iterative updating strategy. Furthermore, the algorithm parameters are optimized using coupled simulated annealing to improve the prediction accuracy. Bearing fault experiments show that the proposed model outperforms the general multivariable LS-SVM. Show more

Keywords: Multivariable least squares support vector machines, performance degradation prediction, time slices, moving window

DOI: 10.3233/JIFS-169548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 6, pp. 3747-3757, 2018