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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Bearings are essential parts in mechanical transmission systems, and their running states directly affect the reliability and stability of the systems. Therefore, an efficient diagnosis method is necessary to detect faults in bearings. In the present, a simulation model based fault diagnosis method for bears is proposed by combination of finite element method (FEM), wavelet packet transform (WPT) and support vector machine (SVM). In this method, firstly, the agreeable finite element models to simulate faulty bearings are presented to obtain the vibration response signals. Secondly, the vibration signals are decomposed into eight signal components using WPT. Ten time-domain feature parameters of all the signal components are calculated to generate the training samples to train the SVM. Finally, the eight signal components decomposed by WPT from the measured vibration signal in a bear, which are serve as a test sample into the trained SVM, and the work condition of the bearing can be determined. Experimental investigations are performed to verify the effectiveness of the present method. The classification accuracy rates for four type faults, i.e., inner race fault, rolling body fault, outer race fault and the combination of rolling body and outer race faults, are 79%, 81%, 71% and 76%, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field. [ABSTRACT FROM AUTHOR]