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Compared with silicon (Si) IGBTs, silicon carbide (SiC) MOSFETs have much faster switching speeds and high switching frequency operation capability, enabling compact and high-efficiency motor drive systems. However, the increased switching speed and frequency also cause increased-level of common-mode (CM) voltage with higher slew rates (dv/dt). The CM voltage leads to machine leakage currents as well as bearing currents such as electric discharge machining (EDM) type current and capacitive-type (dv/dt) current. These bearing currents can cause degradation or failure of motor bearings. In order to investigate the impact of SiC motor drives on the motor bearing currents, in this paper, the mechanism and modelling of bearing currents are analyzed first. Then, a direct measurement method is used to assess the bearing currents in the induction motor driven by the SiC converter. In the experiment, the number of EDM current events is counted and the amplitude of the capacitive-type of current is also examined under various switching speeds and frequencies of SiC devices. Furthermore, the bearing currents are also extensively tested under various bearing temperatures, motor speeds and dc-link voltages. The correlations between the bearing currents and various factors have been revealed in this paper.

The health condition of rolling bearing possesses a significant impact on the safety and efficiency of rotating machinery. Accordingly, to diagnose the faults in rolling bearings effectively and accurately, a novel hybrid approach coupling variational mode decomposition (VMD), composite multiscale fine-sorted dispersion entropy (CMFSDE) and support vector machine (SVM) optimized by mutation sine cosine algorithm and Harris hawks optimization (MSCAHHO) is proposed in the paper. Firstly, VMD is employed to decompose raw vibration signals with various fault types into different sets of intrinsic mode functions (IMFs) to weaken the non-stationarity of signals, before which the parameter $K$ of VMD is decided through central frequency observation method. Subsequently, CMFSDE is put forward in this paper to analyze the complexity of fault signals by fully considering the relationship between neighboring elements based on composite multiscale technique, with which the representative features of different fault samples are extracted to construct feature vectors. Later, an enhanced hybrid optimization approach called MSCAHHO is proposed by integrating sine cosine algorithm (SCA) and a periodic mutation strategy to improve Harris hawks optimization (HHO). Then, MSCAHHO is employed to optimize the parameters of SVM, after which the optimal SVM model is utilized for fault classification. Finally, the performance of the proposed methodology is evaluated with four validity indices through comparative experiments. The experimental results reveal that the proposed VMD-CMFSDE-MSCAHHO-SVM method achieves favorable diagnosis results comparing with other relevant methods.

With the trend of Industry 4.0, the global machine tool industry is developing towards smart manufacturing. The ball bearing is a key component of the rotary axis of machine tool, and its functionality is to bear the external load on the axis as well as maintain the center position of the axis. A damaged bearing will result in abnormal vibration and noise, and thus will lead to the damage of the machine and produced workpieces. Therefore, inspection and identification of ball bearing failures is particularly important. This paper discusses the fault signals of ball bearings published by the Society for Machinery Failure Prevention Technology (MFPT) and creates a recognition model for the ball bearing state based on different fault states, and then we adopt two different approaches for feature extraction. The first approach implements Finite Impulse Response Filter (FIR) and Approximate Entropy (ApEn) to extract the signal features. The second approach utilizes the Chen-Lee chaotic system for analysis and takes its chaotic attractor as the feature of the state recognition. The comparison of model recognition accuracy for Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and K Nearest Neighbor (KNN) was conducted after acquiring the features through the two approaches in this paper. The results of the experiments in this paper show that both of the feature extraction approaches enable the state to be recognized easily. The Chen-Lee chaotic system with BPNN not only reaches 100% identification rate and it has the highest overall efficiency; it takes only 0.054 second to complete the feature extraction for 63 sets of data; this study is able to provide timely and precise solution for the failure of key mechanical components.

Fault diagnosis of rotating machinery (FDRM) has attracted continuous attention because of its great importance to industrial engineering, promoting the healthy and prosperous development of the field. A large number of literature reviews on FDRM have been reported, including signal processing methods in FDRM; artificial intelligence techniques in FDRM; fault diagnosis of bearings, gearboxes and induction motors; and others with broader areas of focus. Using bibliometric techniques, this paper provides objective insight and presents a comprehensive review of FDRM. The review begins with rigorous bibliometric analyses of 2532 published studies. These analyses enable mapping the scope and structure of the discipline, discovering the established collaboration patterns among countries and institutions, and identifying authoritative papers and authors. In addition, a deep analysis of the co-citation network allows graphically classifying the key research topics, illustrating their evolution over time, and identifying the current research interests and potential future research directions. The findings in this paper provide an overall picture of the development of FDRM from 1998 to 2019 and a robust roadmap for future investigations in this field.

High-speed machining is a key technology for greatly improving productivity and reducing production costs. Angular contact ball bearing is the key component of ball screw feed system. Predictive analysis of bearings thermal behavior is significant to ensure the working accuracy of machine tools. Due to the continuous dynamic changes under actual working conditions, it is difficult to predict and model of the bearings heating characteristics. This paper develops a comprehensive calculation method for predicting the thermodynamic characteristics on high speed feed shaft bearings. Based on quasi-static model and thermal dynamic model, Monte Carlo optimization algorithm is used to inverse the temperature field experimental temperature data. The real heating characteristics of heat source are comprehensively predicted and analyzed, so as to accurately predict the bearing thermal dynamic performances of feed shaft bearings. The research work in this paper lays a strong foundation for the thermal error modeling and thermodynamic analysis of the ball screw feed system of machine tools.

The instantaneous phase signal contains abundant information about the health state of machineries, which plays an important role in fault detection of machineries, rotor dynamic balance, transmission error of Rotate Vector reducer, etc. Traditional methods use signal processing methods based on encoders, EMD and Hilbert transforms or fractional delay filters to extract instantaneous phase information indirectly. These traditional methods are state-of-art, however those algorithms are slightly complicated and time-consuming, which is not conducive to online prognostics and health management to some extent. In fact, high precision and high reliability sensor technology can effectively reduce the complexity of subsequent signal processing. In this paper, an instantaneous phase direct detection technique based on eccentric demodulation principle is proposed. The method is derived from the kinematics between the cam and the pushrod. The laser displacement sensor is used to replace the pushrod to realize non-contact measurement. The instantaneous phase is modulation by the cam, and the proposed method can realize the obtain of the theoretical phase infinite resolution, which reaches 16,000-line resolution. Compared with the traditional encoder and other inspection technologies, the proposed method is also suitable for the measurement of broadband shifting and reciprocating motion. By means of simple signal processing methods, this paper realizes the fault diagnosis of the rolling bearing with typical defectives, and realizes the extraction and identification of the fault signal of the reciprocating compressor. Experimental results proved that the proposed inspection technology can serve as an effective technology for mechanical fault diagnosis.

This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is “small, light, and fast,” and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery.

Different mechanisms by which bearing currents flow inside the electrical machine are well studied. In contrast, investigation and development of different techniques to mitigate these currents remain a field for improvement, and the final common solution to the problem has not been provided yet. In pursuit of addressing the problem, this paper deals with noncirculating (or capacitive) bearing currents. The circuit mechanism of capacitive currents is explained in brief, and an approach for their mitigation is introduced. The paper studies the influence of the stator winding and the slot geometry and presents different geometries to modify the motor to reduce the capacitive bearing currents. The effectiveness of the proposed mitigation technique is shown by finite element-based modeling and verified by laboratory tests with different induction motor designs, where the winding and magnetic circuit geometries are varied. The technique can be used, in particular, for the mitigation of noncirculating currents, but it also offers potential for further research. Post-print / Final draft

In this paper, a method for identifying uncertain parameters in a rotordynamic system composed of a flexible rotating shaft, rigid discs and two radial active magnetic bearings is presented. Shaft and disc dynamics are mathematically described using a Finite Element (FE) model while magnetic bearing forces are represented by linear springs with negative stiffness. Bearing negative stiffness produces an unstable rotordynamic system, demanding implementation of feedback control to stabilize the rotordynamic system. Thus, to identify the system parameters, closed-loop system identification techniques are required., The main focus of the paper relies on how to effectively identify uncertain parameters, such as stiffness and damping force coefficients of bearings and seals in rotordynamic systems. Dynamic condensation method, i.e. pseudo-modal reduction, is used to obtain a reduced order model for model-based control design and fast identification., The paper elucidates how nodal parametric uncertainties, which are easily represented in the full FE coordinate system, can be represented in the new coordinate system of the reduced model. The uncertainty is described as a single column vector of the system matrix A of the full FE model while it is represented as several elements spread over multiple rows and columns of the system matrix of the reduced model. The parametric uncertainty, for both the full and reduced FE model, is represented using Linear Fractional Transformation (LFT). In this way the LFT matrices represent the mapping of the uncertainties in and out of the full and reduced FE system matrices. Scaling the LFT matrices easily leads to the amplitudes of the uncertainty parameters., Youla Parametrization method is applied to transform the identification problem into an open-loop stable problem, which can be solved using standard optimization methods., An example shows how to decouple and identify an uncertainty in the linear bearing stiffness of a reduced FE rotordynamic system.

As the main driving equipment of modern industrial production activities, if a motor fails, it causes serious consequences. Bearings are the component with the highest motor failure frequency. It is of practical engineering significance to establish a high-precision algorithm diagnostic model for motor bearings. At present, in data-driven motor bearing fault diagnosis methods, the method of manually adjusting hyperparameters is usually adopted in complex network structure models with many hyperparameters. To realize the automatic optimization selection of hyperparameters, in this paper, a motor bearing fault diagnosis algorithm based on a convolutional long short-term memory network of Bayesian optimization (BO-CLSTM) is proposed. The algorithm combines the Bayesian optimization algorithm (BO), a long short-term memory network (LSTM) and the convolutional layer of a convolutional neural network (CNN). It saves the considerable workload of manually adjusting the hyperparameters, has good noise resistance, and realizes the true end-to-end motor bearing fault diagnosis. The proposed method is trained based on the original vibration signal of the bearing, and the accuracy of the final model reaches 100%. In addition, compared with other advanced fault diagnosis methods based on deep learning, the performance of the proposed method is significantly improved.

To find out the hidden danger in the industrial production process in time, it is necessary to monitor the health condition of the key components of the mechanical system in operation. However, traditional fault diagnosis methods usually adopt manual feature extraction, which not only costs expensively and depends on prior knowledge. Therefore, it is of great significance to study the application of automatic fault identification based on the original vibration signals. Recently, existing studies have shown that most of fault diagnoses are implemented by using deep neural network. Although these methods have achieved satisfactory performances, there are obvious limitations in real applications, that is, the complexity of deep neural network requires a lot of hardware computing resources. This hinders the development of online fault diagnosis tools. To solve this problem, this paper proposes a fault diagnosis model based on lightweight convolutional neural network MobileNet, and realizes an end-to-end intelligent fault classification and diagnosis application. We evaluated the proposed method with the rolling bearing dataset from Western Reserve University. The best average precision, recall and F1 score of ten different bearing health conditions are about 96%, 82% and 88%, respectively. In addition, we also compare the accuracy of the rolling bearing fault diagnosis classification model under the standard ReLU and the improved ReLU. Experimental results show that both obtain good performances, but the improved ReLU reaches the over 96% accuracy more rapidly.

Bearings are important part of aerospace equipments. Therefore, bearings fault diagnosis and fault detection is necessary for the safe operation. Generally, the bearing are core component and runs in a complex and heavy background noise environment. Thence, the bearing signals will be overwhelmed by noise. At present, the diagnosis methods of bearings are based on the experience of experts which costs a lot of labor and time-consuming. Consequently, to address these inadequacies, a novel method based on variational mode decomposition (VMD) algorithms and Alexnet neural network has been presented. Firstly, it decomposes the nonstationary bearing signals into intrinsic mode functions adaptively. However, background noise will seriously affect the number of signal decompositions and reduce accuracy even over-decomposition. Thence, this paper proposed the hierarchical fuzzy entropy method to adaptively extract weak fault characteristics of bearings. Then Alexnet neural network has been utilized to learn the relationship of fault features and bearings health conditions. The architecture of Alexnet diagnosis model is convenient and efficient. Finally, two tentative dataset are adopted to verify the effectiveness and feasibility of the method proposed in this article. The results show that the presented method has a higher fault diagnosis accuracy rate than traditional convolutional neural networks.

Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault condition monitoring and diagnosis method focusing on computation efficiency, which is an important aspect of a continuous monitoring and embedded-based diagnosis device. In the proposed method, acoustic emission signals containing bearing health information are converted into 2-D spectrograms by Constant Q-Transform (CQT) before using a convolutional neural network to infer the bearing state. To reduce the latency while maintaining high accuracy, we propose an efficient search space for neural network architecture search, i.e., a channel distribution search, that automatically obtain the best performing network. Moreover, we present a separation between two processes of condition monitoring and fault diagnosis to save overall computing resources, with a policy of sharing weights in the training process and sharing features in the testing process. The experimental results show that the proposed method reduces about 50% inference time compared to previous methods while achieving an accuracy of 99.82% for eight types of single and compound fault diagnosis for variable rotational speeds.

Aiming at the problems of unclear early fault characteristics and difficult extraction of rolling bearings, a new nonlinear dynamic analysis method called refined composite multi-scale amplitude-aware permutation entropy (RCMAAPE) is introduced in this paper. Firstly, RCMAAPE is used to extract features from the bearing life data, and Chebyshev’s inequality is used to establish a health threshold to evaluate the performance degradation state. Secondly, RCMAAPE is also used for bearing fault diagnosis. Both experimental results prove that RCMAAPE could extract fault characteristics effectively. RCMAAPE can accurately reflect the degradation trend of bearing in the whole life process, and is especially sensitive to the early failure of the bearing. RCMAAPE is also able to effectively identify the states of bearing faults. Especially after selecting the features, RCMAAPE only needs a small number of features to effectively identify the different states of the bearing and the recognition accuracy is up to 100%. Compared with the existing methods, the proposed method can extract fault features more effectively, has higher computational efficiency and obvious advantages.

An inertially stabilized platform is subject to the vibration force and moment from its support base, and low-frequency vibrations cannot be eliminated by mechanical vibration isolation. Combining gimbals with magnetic bearings instead of mechanical bearings, a maglev inertially stabilized platform (MISP) is characterized by no friction or an active vibration control capability. In this paper, an improved linear extended state observer (LESO) replacing displacement error with next-order error is proposed to estimate the low-frequency vibration and improve the estimation accuracy. An active vibration isolation control method is then designed to realize cancellation compensation on the MISP. Finally, a simulation example is presented to validate that the proposed measures can effectively eliminate the low-frequency vibration force transmitted from the base and ensure the stability of the MISP.

Early diagnosis of faults in induction machines is an extensively investigated field, for cost and maintenance savings. Mechanical imbalances and bearing faults account for a large majority of faults in a machine, especially for small-medium size machines. Therefore their diagnosis is an intensively investigated field or research. Recently many research activities were focused on the diagnosis of bearing faults by current signal. Stator current components are generated at predictable frequencies related to the electrical supply and mechanical frequencies of bearing faults. However their detection is not always reliable, since the amplitude of fault signatures in the current signal is very low. This paper compares the bearing fault detection capability obtained with vibration and current signals. To this aim a testbed is realized that allows to test vibration and current signal on a machine with healthy or faulty bearings. Signal processing techniques for both cases are reviewed and compared in order to show which procedure is best suited to the different type of bearing faults. The paper contribution is the use of a simple and effective signal processing technique for both current and vibration signals, and a theoretical analysis of the physical link between faults and current components including torque ripple effects. As expected because of the different nature of vibration and current, bearing fault diagnosis is effective only for those fault whose mechanical frequency rate is quite low. Experiments are reported that confirm the proposed approach.

Many researches have been carried out on incipient fault prediction technology for key machine components (such as bearings) based on historical and real-time condition monitoring data. However, there is still lack of well-understood systematic methodologies for detecting incipient fault for rotating machines. Based on machine learning technology, this paper studies an incipient fault prediction model applying with wavelet packet decomposition and dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs of engineering applications. The incipient fault prediction WPD-DKPCA model, which does not require knowledge on equipment structure and failure mechanisms, only requires normal state data of the machine, and incipient fault prediction can be achieved through self-learning. Run-to-failure experimental data and engineering case data have been used to verify the constructed model, and the verification results show that the constructed model can reliably and accurately detect an incipient bearing fault. Comparisons of fault prediction effects prove that using T2 statistic monitoring can detect upcoming faults of machines much earlier than Kurtosis and Root Mean Square (RMS).

Rolling bearing is one of the most critical components in rotating machinery, so in order to efficiently select features, reduce feature dimensions and improve the correctness of fault diagnosis, a feature selection and fusion method based on weighted multi-dimensional feature fusion is proposed. Firstly, features are extracted from different domains to constitute the original high-dimensional feature set. Considering the large number of invalid and redundant features contained in such original feature set, a feature selection process that combines with support vector machine (SVM) single feature evaluation, correlation analysis and principal component analysis-weighted load evaluation (PCA-WLE) is put forward in this paper for selecting sensitive features. The selected features are weighted and fused according to their sensitivity so as to further weaken the interference of low important features. Finally, this process is applied to the data provided by the Case Western Reserve University Bearing Data Center and Xi'an Jiaotong University School of Mechanical Engineering, respectively, and the fault is diagnosed by using the particle swarm optimization-support vector machine (PSO-SVM). The results show that this method can accurately identify different fault categories and degrees of bearing, which is superior and practical than single-domain fault diagnosis with higher recognition ability.

The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of bearing, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, depthiwise separable convolution is adopted, and a LCNN structure is constructed through an inverse residual structure and a linear bottleneck layer operation. Secondly, a novel decomposed Hierarchical Search Space is introduced to automatically explore the optimal LCNN for bearing fault diagnosis in the context of the IIoT. In the meantime, the real-time monitoring and fault diagnosis of the model are also deployed. In order to verify the validity of the designed model, Case Western Reserve University Bearing fault dataset and MFPT bearing fault dataset are adopted. The results demonstrate the great advantages of the model. The LCNN model can automatically learn and select the appropriate features, highly improving the fault diagnosis accuracy. Meanwhile, the computational and storage costs of the model are largely reduced, which contributes to its being applied to the mechanical system in the IIoT context.

The deep belief network is widely used in fault diagnosis and health management of rotating machinery. However, on the one hand, deep belief networks only tend to focus on the global information of bearing vibration, ignoring local information. On the other hand, the single deep belief network has limited learning ability and cannot diagnose the health of rotating machinery more accurately and stably. As a non-recursive variational signal decomposition method, variational mode decomposition can easily obtain local information of signals. And the ensemble deep belief network composed of multiple deep belief networks also improves the accuracy and stability of the health status diagnosis of rotating machinery. This paper combines the advantages of ensemble deep belief network and variational mode decomposition to propose a novel diagnostic method for rolling bearings. Firstly, the variational mode decomposition is used to decompose the vibration data of the rolling bearing into intrinsic mode functions with local information. Then, using the deep belief network based on cross-entropy to learn the intrinsic mode functions of the rolling bearing data and reconstruct the vibration data. Finally, In the decision-making layer, the improved combination strategy is used to process the health status information of the bearings obtained by multiple deep belief networks to obtain a more accurate and stable diagnosis result. This method is used to diagnose experimental bearing vibration data. The results show that the method can simultaneously focus on and learn the global and local information of bearing vibration data and overcome the limitations of individual deep learning models. Experiments show that it is more effective than the existing intelligent diagnosis methods.

Bearing fault diagnosis is of great significance to ensure the safe operation of mechanical equipment. This paper proposes an intelligent fault diagnosis method of rolling bearings based on deep belief network (DBN) with hyperparameter optimization by using parallel computing. Different with traditional diagnosis methods that extract the features manually depending on much prior knowledge about signal processing techniques and diagnostic expertise, DBN extracts fault features automatically by machine learning mechanism. Considering the time consuming problem, parallel computing is adopted to the DBN training process by using a Master/Slave mode to improve the training speed so that the global optimization with Genetic Algorithm and higher diagnosis accuracy can be achieved. Finally, the proposed method is verified with the public datasets provided by Case Western Reserve University (CWRU) with various fault depths in different locations and loads of rolling bearings. The results indicate that the proposed method can identify bearing faults under different conditions correctly which significantly enhances the intelligence of fault classification and reduces the time for parameter selection of deep learning models.

The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is presented for the fault detection of rolling bearing. Based on morphological erosion operator and morphological dilation operator, a new morphological top-hat operator, namely average of erosion and dilation hat (AEDH) operator is firstly proposed to extract the fault impulses in the vibration signal. Simulation analysis shows the filter characteristics of proposed AEDH operator. Comparative analyses demonstrate that the feature extraction property of the AEDH operator is superior to existing top-hat operators. Then, the probabilistic principal component analysis is introduced to enhance the filter property of AEDH for highlighting the fault feature information of rolling bearing further. Experimental signals collected from the test rig and the engineering are employed to validate the availability of proposed method. Experimental results show that the OSAEDH-PPCA can effectively extract the early fault impulses from vibration signal of rolling bearing. Comparison results verify that the OSAEDH-PPCA has advantage in early fault detection of rolling bearing than other morphological filters in existence.

Data-driven fault diagnosis is critical for the rolling bearing to improve its healthy states and save invaluable cost. Nowadays, various intelligent fault diagnosis methods using machine learning (ML) or deep learning (DL) techniques have achieved much success. The convolutional neural network (CNN) based method, as a representative DL technique, can extract the features of raw data automatically for its excellent sparse connectivity and weight sharing properties. In this paper, a novel data-driven intelligent fault diagnosis method of rolling bearing under variable working loads has been proposed by using 1-D stacked dilated convolutional neural network (1D-SDCNN). First, 1-D vibration signals were used as input data without additional signal processing and diagnostic expertise. Second, the stacked dilated convolution, which can capture larger scale associated information and achieve large receptive fields with a few layers, was used to replace the traditional convolution and pooling structure. Third, the 1D-SDCNN architecture was flexible which is based on the relationship between receptive fields and the length of the input signal. And the number of network layers can be adjusted according to signal length. Further, it can adapt to the changing working loads of the mechanical environment. Finally, the effectiveness of the proposed method was confirmed through the experiment. And the results demonstrated that 1D-SDCNN was able to learn in-deep features under three variable working loads and the average accuracy was 96.8%.

Concerns about advantages such as high efficiency and power density, small size and weight as well as fast dynamic response contribute to the increasing industrial applications of high speed permanent magnet machines (HSPMMs). This paper presents a review on the design and analysis technologies for HSPMMs. Specifically, the stator/rotor structures and materials used in HSPMMs are firstly introduced. Then, the calculation models in terms of stator copper loss, stator iron loss, rotor eddy-current loss and air-friction loss are summarized. Because of the non-negligible influences of high working temperature on motor performance, different calculation approaches of HSPMM temperature distribution and rise as well as the cooling methods are compared. Moreover, the research status of HSPMM mechanical characteristics, including the rotor material strength, bearing support and dynamic behaviors, are analyzed, followed by the conclusions and future directions.

In order to analyze the influence of the rotor-bearing coupling vibration on the dynamic performance of electric vehicles driven by an in-wheel motor, taking a typical four-phase 8/6-pole switched reluctance motor driven electric vehicle without deceleration mechanism as an investigated object, a nonlinear dynamic model of an electric vehicle with the in-wheel motor driving system is established on the basis of considering the motor exciting force and the bearing nonlinear force. Through the numerical simulation, the influence of the rotor-bearing coupling vibration on the dynamic behavior of electric vehicle driven by the in-wheel motor is studied. Here, in this paper, the dynamic characteristics of an electric vehicle with and without motor exciting force and bearing nonlinear force are compared and analyzed. In addition, the influence of the suspension parameters on the dynamic performance of electric vehicle driven by the in-wheel motor is analyzed. According to the results, the influence of the rotor-bearing coupling vibration on the dynamic behavior of the electric vehicle is mainly embodied in the tire vibration acceleration and tire dynamic load. Moreover, the existence of the in-wheel motor driving system will deteriorate the evaluation index of the electric vehicle vibration performance. By choosing appropriate parameters of the vehicle suspension system, the ride comfort of the electric vehicle can be effectively improved.

The squared envelope spectrum (SES) is one of the most effective methods in rolling element bearing fault diagnosis. Being a technique based on demodulation, the success of SES depends highly on the extraction of information caused by the bearings fault. Therefore, a preprocessing step of choosing an optimal frequency band (OFB), before the implementation of the SES, is absolutely needed and should always be taken prior. Fast kurtogram (FK), as the most commonly used method targeting resonance frequency band which is excited by the bearing fault, is applied as the benchmark tool for the selection of the OFB. However, recent theoretical works show that the kurtosis used as the criterion for OFB selection was both sensitive to the impulsiveness and cyclostationarity, which means irrelevant impulsive and cyclostationary components are able to mislead the kurtosis based OFB selection techniques and subsequently bring great difficulties in bearing fault diagnosis. To tackle this problem, in this paper, a novel statistical model parameter-based method, called Distcsgram, is proposed as a substitution to the traditional FK technique so that an OFB can be selected with the least influences from the irrelevant cyclostationary and impulsive interferences. Together with the SES, the Distcsgram is explained and validated in both simulation and experimental studies. Furthermore, through the comparisons with the traditional methods, the superiority of the proposed method over those traditional methods for the diagnosis of rolling element bearing is proved.

As key components in a rotating machinery system, bearings affect the safety of the entire mechanical system. Hence, early-stage monitor of bearing degradation is critical to avoid abrupt mechanical system failure. In this paper, a novel bearing performance assessment model is constructed based on ensemble empirical mode decomposition (EEMD) and affinity propagation (AP) clustering. Unlike most clustering methods, AP clustering, which automatically finds the center of all available clusters, can determine the bearing degradation status without an experience-based selection of the number of degradation states. The original bearing vibration signal is first decomposed by EEMD and its degradation fault features are extracted from the singular-value decomposition of intrinsic mode functions. Then, the degradation features are selected as the input of AP clustering to find the cluster centers of different bearing health statuses: “normal”, “slight”, and “severe”. Last, a health evaluation indicator, referred to as the confidence value, which is obtained from the dissimilarity between actual samples and the various cluster centers, is used to evaluate the bearing health status. To prove the superiority of the approach, the proposed model is compared to various popular clustering methods, including, k-means, k-medoids, fuzzy c-means, Gustafson-Kessel, and Gath-Geva, and commonly used time-domain indicators such as root mean square and kurtosis. The experimental results show that the proposed method outperforms the above time-domain indicators and clustering methods in monitoring early-stage degradation, without presetting the number of clusters.

Considering the wear in mechanical bearings and the requirement for sensors or electrical power in active magnetic bearings, a novel nutation blood pump using a passive magnetic spherical bearing was developed in this paper. A mathematical model was derived to calculate the magnetic forces of the bearing between two pairs of magnetic sleeves in the nutation process. The calculation results demonstrate that the fluctuations of magnetic forces enlarge with the increase in the nutation angle; the magnetic forces obviously increase with the decrease in the air gap, especially along the z-axis. The dynamic magnetic finite element simulation was carried out to validate the mathematical model. The simulation and calculation results of the magnetic forces show consistent trends and provide a theoretical basis for the parameter design. To validate the performance of the pump, computational fluid dynamics (CFD) analyses and in vitro experiments were conducted. The predicted values of the flow velocity vector through the pump, and the wall shear stress, demonstrate that the pump has an antithrombotic property and would not cause serious blood damage. The hydraulic experiment shows that a pressure rise can be achieved in the range of 60-140 mmHg, at a rotational speed of 600-1600 rpm and a flow rate of 0.4-6.7 L/min. The normalized index of hemolysis (NIH) of the nutation pump was 0.0043 ± 0.0008 g/100L. The in vitro tests indicate the feasibility of a magnetically levitated ventricular-assist nutation blood pump for further suspension stability and animal trials.

Bearing faults are the most common failure modes in the rotating system. Vibration data from the rotating system carry important information, that is, characterization and diagnosis; therefore, the vast vibration signals collected from multiple sensors mounted in different sites are transmitted in a certain order for online fault diagnosis. However, due to the influence of transfer paths and noises, the sensitivities to the same fault signal of measured data streams are of significant differences, and signals containing weak sensitivity to the fault are likely to be transmitted preferentially while neglecting transmission order. Meanwhile, high volume vibration data greatly increase the transmission burden. These above-mentioned reasons dramatically reduce online diagnostic efficiency. Thus, fully considering the sensitive differences to the fault for multiple channels, how to transmit measured data streams of multiple sensors for timely online detecting the bearing failure is still a primary challenge. In order to solve this problem, a novel online bearing fault diagnosis method based on the multiple data streams transmission schemes (MDSTS) is proposed in this paper. Multiple sensors are numbered consecutively, and data streams from all channels are transmitted according to the preset order and transport protocol via a certain length at the beginning of diagnosis. Then, a fault sensitivity assessment model (FSAM) is established on maximum mean discrepancy (MMD) for transmitting the most sensitive data stream by calculating the distribution discrepancies between each channel's data streams and the historical datasets in the frequency domain, and then, the fault diagnosis model based on K-nearest neighbor (KNN) trained on historical datasets was used to evaluate the transmission scheme and acquire reliable diagnostic results via predicting performances of multiple and consecutive datablocks until all these exceed an alarm value. The extensive experiment results show that the proposed method can timely and accurately identify the bearing faults and outperforms obviously competitive approaches.

© 2013 IEEE. The characteristics of the clutch engagement process would have significant influences on the torque transmissibility and operation comfort. However, some crucial components are simplified in many previous literature, which would cause imprecision. Therefore, it is important to build a detailed mathematical model of these components and inspect the whole process of clutch engagement. In order to improve the torque transmissibility and achieve better pedal releasing comfort, solutions based on the modeling of the clutch cover assembly and the friction disc assembly, the analysis of the clamping force and the releasing characteristics of the release bearing are proposed in this paper. Furthermore, models of the crucial components such as the diaphragm spring, which connects the straps and cushion plate, are built and the corresponding mechanical properties are analyzed. Based on the manufacturing tolerance, the life cycle, and the wear properties, diaphragm spring correction formula is proposed by referring to Almen-Laszlo method. On the system level, the whole engagement process is divided into four stages because of the differences between the engaging and disengaging processes, which would affect the pedal releasing comfort in the manual transmission system and the shifting quality in the automated transmission system. To demonstrate the effectiveness of the proposed method, detailed mathematic models are built and the corresponding experiments are conducted.

Rolling element bearing is a critical component in rotating machinery that reduces the friction between moving pairs. Bearing fault diagnosis is always considered as a research hotspot in the field of prognostics and health management, especially with the application of deep learning. Deep learning, such as a convolutional neural network (CNN), can extract features automatically compared with traditional methods. However, the construction of the CNN model and the training process still need a lot of prior knowledge, and it takes a lot of time to build an optimal model to achieve a high classification accuracy. In addition, great challenges of universal applicability exist when different input forms (e.g., different sampling lengths or signal forms) are considered. This paper presents a universal bearing fault diagnosis model transferred from a well-known Alexnet model, and only the last fully connected layer needs to be replaced, which could reduce prior knowledge and extra time in establishing a new model. Accordingly, it is necessary to convert a raw acceleration signal to a uniform-sized time-frequency image, even when these data have different sizes. Furthermore, standardized images created by eight time-frequency analysis methods are applied to validate the effectiveness of the proposed method in two case studies. The results indicate that this method can be applied in bearing fault diagnosis, and t-SNE helps to understand the process of feature extraction and condition classification.

This paper focuses on the bearing estimation problem of far-field signal source via time-difference-of-arrival (TDOA) with a synchronized array in 3-D space. It is usually assumed that the propagation speed (PS) is perfectly known in localization. In reality, only an imperfect knowledge of PS could be obtained. The traditional closed-form solutions without involving PS have the advantage of low complexity, but suffer from low estimation accuracy. A measurement-division model is proposed to offer an alternative solution without the need of the propagation speed. This speed-free model combines two original TDOA measurement equations into a division formula, whose Cramer-Rao lower bound (CRLB) is derived for the observed data. A typical optimization method, i.e. the Levenberg-Marquardt (LM) algorithm is adopted to resolve the nonlinear measurement-division model, resulting in an estimation accuracy improvement because of its iterative search behavior. The theoretical performance of this solution is evaluated in terms of bias and covariance. Simulations are conducted to demonstrate an accuracy advantage of the solution over the related methods.

This paper deals with a design method to improve the power density and energy efficiency of a wound-field synchronous motor (WFSM) using hairpin type rectangular wire for electric vehicle traction. First, the prototype is analyzed via experiments to come up with plans to improve it. In addition, mechanical loss of the prototype including the bearing and brush friction losses is obtained and used for designing the improved motor. After then, an analytical approach is presented for the magnetic circuit design to minimize magnetic resistance in the motor core. Also, the analytical methods are proposed to predict the resistance of the field and armature windings. At this step, rectangular wire is considered for the armature winding to reduce the copper loss. Moreover, the calculation process is presented to estimate the iron loss considering harmonics. The performances such as power density and efficiency of the improved WFSM are analyzed and compared with those of the prototype. In addition, the energy efficiency of the motors in the new European drive cycle (NEDC) is analyzed. Finally, the performances of the improved WFSM are compared with the experimental results to verify the validity of the proposed design process.

Since roller bearing is one of the most vulnerable components, bearing faults usually occur in an unprepared situation with multiple faults, and the quantity of sensors is limited in the real-time working environment, resulting in an underdetermined blind source separation (UBSS) problem to extract the fault features. Because the collected signals are usually not independent and not sparse enough, traditional methods of separating signals cannot perform well. In this paper, an optimized intrinsic characteristic-scale decomposition (OICD) method is proposed to solve the underdetermined problem. Meanwhile, the constraint error factor is introduced to overcome the drawback that the ideal ending condition of ICD is not proper for the vibration signal of bearing. In addition, given that non-negative matrix factorization (NMF) is not limited by the source signal independence and sparsity, an improved UBSS model is constructed, and the PCs are used as the input matrix of local NMF to obtain the separation signal. Ultimately, envelope analysis is utilized to detect the source signal feature. Both simulated and experimental vibration signals are used to verify the effectiveness of the proposed approach. Besides, the traditional method is juxtaposed with the suggested method. The results indicate that the proposed method is effective in dealing with the compound faults separation of the rotating machinery.

To ensure a long-time stable operation of the rolling bearing, it is important to accurately assess their working performance, especially the incipient degradation based on the massive service process data. As a new and effective tool, deep learning model is applied widely in the field of fault diagnosis but limited to rare labeled data. In this paper, a bearing performance assessment method based on signal component tracking is proposed to realize the bearing degradation detection. More general features are obtained by local convolution operation to represent the local characteristics in the spectrum or time–frequency distribution of vibration signal, which follows the forward features mapping process of the convolutional neural network (CNN). Then, a novel quantification criterion based on the comparison of those local features is used to provide the selection strategy of optimal fault components. The proposed method takes into account the abnormal information in degradation monitoring and utilizes it to achieve bearing incipient fault diagnosis. The experimental results prove that the features extracted by the proposed method possess high recognition efficiency when being used in incipient failure detection and diagnosis.

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/ .

Fault detection of axial piston pumps is of great significance to improve the reliability and life of fluid power systems. However, it is difficult to detect multiple faults on key lubricating interfaces due to the liquid-solid coupling. This paper proposes a fault detection strategy of the three key lubricating interfaces based on the one against all (OAA) and spare support vector machine (SSVM). The parameter sparsity is imposed to deal with the performance degradation of OAA-SVM model as a result of the imbalanced dataset. Experimental investigations on the benchmark dataset and axial piston pumps are carried out. Results show that the OAA-SSVM model accuracies of the benchmark dataset and axial piston pump are 96.67% and 95.83%, respectively, which are better than the OAA-SVM model. The recall rates of the bearing fault 3 and pump fault 2 can decrease by 13.33% and 10.00%, respectively. And the false discovery rates of the normal bearing and normal pump can be reduced by up to 7.58% and 6.24%, respectively. Besides, the OAA-SSVM model can improve the feature sparsity. Results show that the proposed method is effective in detecting multiple faults of axial piston pumps.

Bearings are widely used in rotating machinery, such as aircraft engines and wind turbines. In this paper, we proposed a new data-driven method called frozen convolution and activated memory network (FCAMN) for bearing remaining useful life (RUL) estimation based on the deep neural network. The proposed method is composed of two parts: the multi-scale convolutional neural network is carried out to pre-train the raw data to directly obtain the global and local features, and the second step is accomplished by the convolutional-memory neural network, which enables to connect the convolutional layer with the long-short-time-memory layer together to predict the continuous bearing RUL. Compared with the traditional networks, the proposed network can additionally extract both the global and local information on the vertical feature axis and the associated context information on the horizontal time axis. The experiments are conducted to prove that the proposed method requires fewer training samples and outperforms other methods in RUL estimation.

This paper offers a new perspective on the vibrations of discrete bearing faults by focusing on the micro-motion states of rolling elements in spall fault bearings and proposes an improved matching pursuit algorithm for quantitative diagnosis with a high accuracy of atom selection and calculation efficiency. The generation mechanism of the vibration response signal is explained by analyzing the micro-motion status when rolling elements passing through the spall. A concatenation dictionary composed of an impact dictionary as the higher level and step dictionary as the lower level is constructed based on the acceleration variation analysis of the rolling elements. The information output by the higher-level dictionary is used as the input information for the lower-level dictionary to extract the fault features. Only one iteration on the higher-level dictionary is necessary to extract the correct impact atoms, with all subsequent iterative steps assigned to the lower-level dictionary. The advantage is that the influence of high-energy impact components on the extraction of step atoms can be removed. Thereafter, the optimized algorithm based on the concatenation dictionary is applied to the analysis of simulation and experimental signals. The comparative analysis demonstrates that the effective quantitative diagnosis is obtained, while the diagnostic precision and calculation efficiency are improved.

The linear wheel model is the most widely used in ride comfort analysis of tracked vehicles. However, as the speed of the vehicle increases and the application of new materials, and the road wheel becomes lighter and softer, its nonlinear characteristics become more and more obvious and can't be ignored in vehicle dynamics simulation. This paper aims to study the effect of nonlinear stiffness of a novel flexible road wheel (FRW) with unique suspension bearing structure on the ride comfort of a tracked vehicle traversing random uneven road. The linear and nonlinear models of FRW were established by fitting the load-deflection data obtained from the static loading test of the physical prototype. The established linear and nonlinear models of FRW were added to a half-vehicle model of a tracked vehicle which has been proved by published test results. The ride comfort of the half-vehicle model of the tracked vehicle with linear and nonlinear models of FRW on random uneven road surface was studied in detail. The study results show that, compared with the linear wheel model, the nonlinear model has a tremendous influence on the dynamic response of the tracked vehicle, effectively suppressing the vibration, especially for the high frequency excitation. In addition, the nonlinear factor has a greater impact on the dynamic performance of the wheel than on the suspension and the body. The research results enrich the study of nonlinear dynamics of tracked vehicles, and provide reference for nonlinear modeling of other pneumatic tires and non-inflatable wheels.

Incipient damages of wind turbine rolling bearing are very difficult to be detected because of the interference of multi-frequency components and strong ambient noise. To solve this problem, this paper proposes a new detected method named VMD-AMCKD, combining complementary advantages of variational mode decomposition (VMD) and adaptive maximum correlated kurtosis deconvolution (AMCKD). A novel index is proposed to screen out the most sensitive mode containing fault information after VMD decomposition. The mode also can determine a suspectable range for the fault frequency, based on which the optimized range of devolution period T in MCKD can be pre-determined. The Grasshopper optimization algorithm (GOA) is adapted to adaptively select the key parameters in MCKD. The proposed method can successfully diagnose the simulated signal mixed with strong white Gaussian noise. Its robustness is further proven by the diagnosis for three different types of experimental signal from CWRU bearing data center. Finally, the VMD-AMCKD is applied to detect incipient damages of rolling bearings in a laboratory wind turbine.

PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions.

To solve the problem in which the auxiliary white-noise parameters need to be artificially selected in a noise-assisted multivariate empirical mode decomposition (NA-MEMD) and considering the fact that obtaining a large number of typical fault samples in practical engineering is difficult, a rolling bearing fault-diagnosis method based on velocity modified-mutation particle swarm optimization (PSO)-optimized NA-MEMD and improved functional neural fuzzy network (FNFN) is proposed. First, the original vibration signal is processed using the velocity modified-mutation PSO–NA-MEMD method to decompose it into a series of intrinsic mode functions (IMFs) with different characteristic time scales. Owing to the distribution characteristics of the IMF with the time scales and because the energy in different states of a bearing in different frequency bands changes, we calculate the energy moment of each IMF as a fault feature vector. Second, the fault feature vectors are then used as input to construct the improved FNFN structure. Finally, the method is validated using the data from the bearing data center of Case Western Reserve University and the vibration signals of the propulsion-motor bearings in the rudder paddle compartment during normal ship navigation and is compared with other neural network. The results show that the method proposed in this paper can quickly and more accurately diagnose rolling-bearing faults using limited training samples.

Motor bearing is subjected to the joint effects of much more loads, transmissions, and shocks that cause bearing fault and machinery breakdown. A vibration signal analysis method is the most popular technique that is used to monitor and diagnose the fault of motor bearing. However, the application of the vibration signal analysis method for motor bearing is very limited in engineering practice. In this paper, on the basis of comparing fault feature extraction by using empirical wavelet transform (EWT) and Hilbert transform with the theoretical calculation, a new motor bearing fault diagnosis method based on integrating EWT, fuzzy entropy, and support vector machine (SVM) called EWTFSFD is proposed. In the proposed method, a novel signal processing method called EWT is used to decompose vibration signal into multiple components in order to extract a series of amplitude modulated–frequency modulated (AM-FM) components with supporting Fourier spectrum under an orthogonal basis. Then, fuzzy entropy is utilized to measure the complexity of vibration signal, reflect the complexity changes of intrinsic oscillation, and compute the fuzzy entropy values of AM-FM components, which are regarded as the inputs of the SVM model to train and construct an SVM classifier for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by using the simulated signal and real motor bearing vibration signals. The experiment results show that the EWT outperforms empirical mode decomposition for decomposing the signal into multiple components, and the proposed EWTFSFD method can accurately and effectively achieve the fault diagnosis of motor bearing.

Flywheel bearing is a key mechanical part of a satellite. Its health plays an important role in the fatigue life of the satellite. However, it is rather difficult to diagnose the health state of the bearings due to the complex satellite system. This paper attempts to propose a three-direction correlation dimension method to diagnose the bearings of a satellite flywheel at three typical states based on K-medoids clustering technology. A set of spatial spheres representing different bearings are modeled to recognize these three states of the bearings. To avoid misdiagnosis or loss of the bearing state, a twice-cluster scheme is employed. A series of tests is carried out to observe the effectiveness of the proposed method. The result shows that the proposed method is capable of diagnosing the different states of the bearings and its accuracy is higher than 99% at given conditions.

For improving the control system of traditional ship fin stabilizers, this paper presents a novel control system to solve the deviation of system feedback by using dynamic lift. However, the difficulty of lift feedback control is lift measurement technology. Thus, the lift sensor is designed using Spoke-type structure and Wheatstone bridge-type strain conversion circuit. The design of compensation resistors is adopted in the circuit, which effectively reduces the effect of temperature on zero drift and sensitivity for the lift sensor. In addition, the mechanical decoupling method of fin hydrodynamic force and the lift measurement method based on double bearing load are proposed. In addition, then, the control system of ship fin stabilizer is improved in order to solve the main technical problems. Finally, the experimental results demonstrate that the designed sensors meet the requirements of lift measurement. In addition, the simulation results of the ship at different situations show that the effect of roll stabilization (86.803%–93.858%) is improved effectively.

Failures of rolling element bearings are amongst the main causes of machines breakdowns. To prevent such breakdowns, bearing health monitoring is performed by collecting data from rotating machines, extracting features from the collected data, and applying a classifier to classify faults. To avoid the burden of much storage requirements and processing time of a tremendously large amount of vibration data, the present paper proposes a combined compressive sampling (CS)-based on multiple measurement vector (MMV) and feature ranking framework to learn optimally fewer features from a large amount of vibration data from which bearing health conditions can be classified. The CS-based on the MMV model is the first step in this framework and provides compressively sampled signals based on compressed sampling rates. In the second step, the search for the most important features of these compressively sampled signals is performed using the feature ranking and selection techniques. For that purpose, we have investigated the following: 1) two compressible representations of vibration signals that can be used within CS framework, namely, fast Fourier transform-based coefficients and thresholded Wavelet transform-based coefficients and 2) several feature ranking and selection techniques, namely, three similarity-based techniques, fisher score, Laplacian score, Relief-F; one correlation-based technique, Pearson correlation coefficients; and one independence test technique, Chi-Square (Chi-2) to select fewer features that can sufficiently represent the original vibration signals. These selected features, in combination with three of the popular classifiers–multinomial logistic regression classifier, artificial neural networks, and support vector machines, have been evaluated for the classification of bearing faults. Results show that the proposed framework achieves high classification accuracies with a limited amount of data using various combinations of methods, which outperform recently published results.

Rolling element bearing is a critical component in many mechanical systems in view of its critical functionality. One of the major issues industries face today is the failure of bearings, which results in catastrophic consequences. Although various prognostic approaches were proposed for the degradation of bearings, the incapability of adaptation of those models yields inaccurate predictions under different running conditions of the bearings. To address this issue, this paper proposes a prognostic algorithm using the variable forgetting factor recursive least-square (VFF-RLS) combined with an auto-regressive and moving-average (ARMA) model. The structure and parameters of ARMA model were initially determined using the vibrational data of the bearing without significant defect presented. During the bearing degradation process, the ARMA model makes predictions of the future degradation trend. Once the future acquired signal becomes available, the error between the acquired and predicted vibrational signal is calculated. The VFF-RLS algorithm uses the calculated error, correlation matrix and other parameters to update the coefficients of the ARMA model. In addition, the VFF-RLS algorithm updates the forgetting factor during each iteration to achieve faster convergence and reduced error. The updated ARMA model makes new predictions and the adaptive process continues. To demonstrate the applicability of adaptive prognosis methodology, the accuracy of the prediction of the proposed model is tested using experimental and simulated data in comparison with an auto-regressive integrated moving average (ARIMA) model without adaptation. Results show accurate predictions of the vibrational signal and degradation trend of the bearings over the ARIMA model.

Rolling bearing is a key component of rotating machinery, and its remaining useful life (RUL) estimation is also a critical technique in prognostics health management activities. In this paper, we propose a two-stage strategy for bearing health monitoring, where the bearing health process is divided into two stages: normal stage and degeneration stage, and a new method is proposed to estimate the bearing RUL by combining a new health indicator (HI) and particle filtering (PF). First, the structural information of the spectrum (SIOS) algorithm is employed to construct the HI called SIOS-based indicator (SIOSI) for bearing degeneration monitoring. Second, the initial degenerate point (IDP) is evaluated by a novel index calculated with self-zero space observer in order to conduct the two-stage division of normal and degeneration stages. Third, after detecting the IDP, the bearing RUL is estimated using the SIOSI and the PF-based algorithm with the help of a degeneration model. The effectiveness of the proposed methodology is validated using vibration data collected from bearing run-to-failure tests. Experimental results have shown that the bearing RUL could be estimated acceptably with the proposed method, and its performance is greatly superior to that presented by $L_{10}$ life formula.