Condition Monitoring Of Roller Bearings Research Articles

Early faults diagnosis and severity assessment of rolling element bearings on wireless signal transfer

Machine condition monitoring in remote locations and harsh environment where network infrastructure is not feasible, or hardwired network connectivity is not possible, wireless communications provides an alternative which also offer installation cost savings, improve reliability and quicker deployment. This paper describes the implementation of wireless sensor network (WSN) for early fault diagnosis of rolling element bearings based on signal autocorrelation technique. A low-power 2.4 GHz wireless HART transceiver, a low-cost wireless vibration transmitter, 26.76 mv/g accelerometers and a 1420 wireless gateway with AMS software was implemented. The research describes the methodology of acquiring peak values data in high frequency region. The noise was averaged out by applying four-time averaging and natural frequencies or fault frequencies of bearing elements was captured. The experimental results show that the signal autocorrelation algorithm can successfully diagnose the roller bearing faults at early stage on wireless signal transfer. As the raw data was processed before wireless transmission on analyzing unit and spectrum was transferred in JPG format on display unit, minimum power consumption has been noted. The technique provided a better alternative of wired system for real time condition monitoring of roller bearings in rotating equipment installed in remote area.

Condition monitoring of roller bearings using acoustic emission

Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

Symplectic incremental matrix machine and its application in roller bearing condition monitoring

For roller bearing condition monitoring, the collected signals have complex internal structure, which can be naturally represented as matrices. Support matrix machine (SMM), as a new classifier with matrices as inputs, makes full use of the correlation between rows and columns of matrices and achieves ideal classification results. Unfortunately, SMM have ignored the issue of redundant features, which seriously affects the operational efficiency and recognition accuracy of algorithm. In this paper, we introduce symplectic geometry, l1-norm and incremental proximal descent (IPD) to SMM, and symplectic incremental matrix machine (SIMM) is proposed. In SIMM, through symplectic geometry similarity transformation, the de-noising symplectic geometry coefficient matrix is obtained, and the noise robustness of SMM method is therefore improved. Moreover, l1-norm is used to constrain the objective function, which can weaken the influence of redundant features, and thus greatly improving the recognition accuracy of SMM. Meanwhile, we use IPD to solve the objective function, which can obviously enhance the algorithm efficiency under the constant recognition rates. The experimental results of two kinds of roller bearings show that the proposed method has a good effectiveness in roller bearing condition monitoring, and the achieved recognition rate can reach 3%–25% much higher than those of the traditional recognition methods in 5-cross validation.

Symplectic interactive support matrix machine and its application in roller bearing condition monitoring

Support matrix machine (SMM) is an effective method to solve the problem of mechanical condition monitoring while the matrix is taken as the input. It makes full use of the effective information between rows and columns of the matrix to establish an ideal prediction model and achieve good condition monitoring results. However, Similar to support vector machine (SVM), the core principle of the SMM is to distinguish the data effectively by two parallel hyperplanes. Unfortunately, two parallel hyperplanes may not be able to maximize the interval. Therefore, the concept of interactive support matrix machine (ISMM) is proposed, which constructs a pair of interactive hyperplanes to maximize the interval between two types of data. Interactive hyperplanes may be more able to distinguish between two types of data, so that each hyperplane is as close as to one of the two types and as far away as possible from the other. However, the input of the model often contain noise information, which seriously interferes with the classification results. Therefore, a symplectic interactive support matrix machine (SISMM) method is further proposed, which combines symplectic geometry similarity transformation (SGST) with ISMM. In SISMM, it can directly get the symplectic geometry coefficient matrix without noise from the original signal, and intelligent classification recognition is realized. By analyzing and comparing the signal of roller bearings, the results show that the proposed method has better recognition performance and it is feasible for roller bearing condition monitoring.

Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors.

This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.

A Novel Roller Bearing Condition Monitoring Method Based on RHLCD and FVPMCD

Effective condition monitoring provides some benefits such as improving safety and reliability. Roller bearing is the key component of rotating machinery, and a novel roller bearing condition monitoring method based on rational Hermite interpolation-local characteristic-scale decomposition (RHLCD) and fusion variable predictive model-based class discriminate method (FVPMCD) is proposed in this paper. RHLCD can adaptively decompose any complex signal into a sum of rational intrinsic scale components (RISCs), whose instantaneous frequency has physical meaning. In addition, targeting the limitation of variable predictive model-based class discriminate method (VPMCD), FVPMCD is presented. First, four kinds of common models are used to recognize a sample. Then, the recognition results of each model are satisfied, and the recognition probability of each state is calculated. Finally, the largest recognition probability of the state is chosen to recognize categories. The analytical results of experimental signals indicate that the proposed condition monitoring approach can identify the states of roller bearing effectively.

Bearing Race Faults Classification using Simulation-generated Training Data and Feature Free Methods

Rotating machinery is central to transportation and power generation, and maximizing its uptime while minimizing unplanned maintenance is important from safety and economic perspectives. Roller bearings are ubiquitouscomponents in such machinery and are very often the cause of failures; thus, condition monitoring of roller bearings is a topic of key interest to many industries. Historically accomplished using human expertise and experience toestimate the condition of a machine from a limited set of signals and curated statistical features, machine learning has become an effective tool in bearing fault identification with modern computing and algorithmic advances. In this regard, two major challenges exist: first, the availability of inservice data from bearings containing faults is often rare or difficult to obtain, and moreover, for machines being deployed to the field for the first time, no such data exists. Secondly, the extracted statistical measures (features) must be chosen to maximize the classifier accuracy (or another metric), and their selection of these to maximize accuracy can be a challenging task. We directly address these challenges with two separate approaches. With validation against four experimental datasets, we show that in the absence of recorded in-service data, machine learning algorithms trained using simulated bearing vibration signals (i.e. simulation-driven machine learning) can classify bearing race faults with greater accuracy than those trained using data collected from other machines. Next, we propose convolutional neural networks and nearest-neighbor dynamic time warping (NNDTW) as statistical feature-free methods to detect bearing race faults using a signal processing pipeline based on angle synchronous averaging. We show that these methods offer superior accuracy from the simulation-driven perspective and can predict an inservice wind turbine fault over one month before failure.

Dynamic simulation for a tapered roller bearing considering a localized surface fault on the rib of the inner race

Vibration characteristics of a tapered roller bearing system will be significantly affected by a localized surface fault in the rib of the inner race. The operating condition for the tapered roller bearing system is always monitored to prevent serious failures from happening based on changes in the vibration characteristics. However, most of the previous works are focused on dynamic simulations for a localized surface fault in the race surface. A new dynamic simulation method for a tapered roller bearing with a localized surface fault on the rib of the inner race is proposed. The non-Hertzian contact of taper roller to races and rib is considered. The time-varying deflection excitation caused by the fault is formulated in the proposed method, as well as both the axial and radial contact deformation between the races and rollers. Effects of the axial load, radial load, and fault sizes on the vibration characteristics for the tapered roller bearing are analyzed. An experimental investigation is also developed to validate the proposed method. The results show that the proposed dynamic simulation method can formulate the vibration characteristics for the tapered roller bearing caused by the localized surface fault on the rib of the inner race, which may give some guidance for the tapered roller bearing condition monitoring and fault diagnosis, especially for the incipient localized surface fault.

A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings.

The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples.

Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals

In the condition monitoring of roller bearings, the measured signals are often compounded due to the unknown multi-vibration sources and complex transfer paths. Moreover, the sensors are limited in particular locations and numbers. Thus, this is a problem of underdetermined blind source separation for the vibration sources estimation, which makes it difficult to extract fault features exactly by ordinary methods in running tests. To improve the effectiveness of compound fault diagnosis in roller bearings, the present paper proposes a new method to solve the underdetermined problem and to extract fault features based on variational mode decomposition. In order to surmount the shortcomings of inadequate signals collected through limited sensors, a vibration signal is firstly decomposed into a number of band-limited intrinsic mode functions by variational mode decomposition. Then, the demodulated signal with the Hilbert transform of these multi-channel functions is used as the input matrix for independent component analysis. Finally, the compound faults are separated effectively by carrying out independent component analysis, which enables the fault features to be extracted more easily and identified more clearly. Experimental results validate the effectiveness of the proposed method in compound fault separation, and a comparison experiment shows that the proposed method has higher adaptability and practicability in separating strong noise signals than the commonly-used ensemble empirical mode decomposition method.

Rolling Bearing Safety Region Estimation Based on Information Entropy

The basic idea of safety region is introduced into roller bearing condition monitoring. Power Spectral Entropy, Singular value Entropy are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then establish Power Spectral Entropy, Singular value Entropy as characteristics of roller bearings. Finally, SVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class SVM was used of the identification of the four states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the method.

A Statistical Feature Utilising Wavelet Denoising and Neighblock Method for Improved Condition Monitoring of Rolling Bearings

Rolling element bearings are of great importance in industrial applications as well as in critical applications in transport. Signal processing techniques can enhance the ability of bearings condition monitoring to identify faults during operation. In this work, state of the art signal denoising techniques are applied for condition monitoring of roller bearings. In particular wavelet denoising with NeighBlock threshold technique is applied in vibration waveforms. A standard data base for lifelong operation of roller bearings is used for the tests. The condition monitoring efficiency of a statistical feature is assessed taking into account both raw and denoised bearing vibration signals. A brief assessment shows that such a signal denoising technique can evidently improve the remaining useful life estimation as well as the change point detection of the structural health of the asset.

Roller bearing safety region estimation and state identification based on LMD–PCA–LSSVM

The basic idea of safety region is introduced into roller bearing condition monitoring. Local mean decomposition (LMD), principal component analysis (PCA) and least square support vector machine (LSSVM) are used comprehensively for the estimation of the safety region and the identification of normal state and faulty state for the roller bearing operational status. First, the vibration acceleration data was segmented according to a certain time interval and then Product Functions (PFs) of each piece of the data were obtained by LMD. Based on this, statistics control limits T2 and SPE were extracted by PCA as roller bearings’ state characteristics. Finally, LSSVM was used for the estimation of the safety region of the roller bearing operation state, and multi-class LSSVM was used for the identification of the four normal, ball fault, inner race fault and outer race fault states. The results show that both the safety region estimation and state identification are accurate, and confirm the validity of the LMD–PCA–LSSVM method.