Naipeng Li

An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

The remaining useful life (RUL) prediction of rolling element bearings has attracted substantial attention recently due to its importance for the bearing health management. The exponential model is one of the most widely used methods for RUL prediction of rolling element bearings. However, two shortcomings exist in the exponential model: 1) the first predicting time (FPT) is selected subjectively; and 2) random errors of the stochastic process decrease the prediction accuracy. To deal with these two shortcomings, an improved exponential model is proposed in this paper. In the improved model, an adaptive FPT selection approach is established based on the 3σ interval, and particle filtering is utilized to reduce random errors of the stochastic process. In order to demonstrate the effectiveness of the improved model, a simulation and four tests of bearing degradation processes are utilized for the RUL prediction. The results show that the improved model is able to select an appropriate FPT and reduce random errors of the stochastic process. Consequently, it performs better in the RUL prediction of rolling element bearings than the original exponential model.

A recurrent neural network based health indicator for remaining useful life prediction of bearings

In data-driven prognostic methods, prediction accuracy of bearing remaining useful life (RUL) mainly depends on the performance of bearing health indicators, which are usually fused from some statistical features extracted from vibration signals. However, many existing bearing health indicators have the following two shortcomings: (1) many statistical features do not have equal contribution to construction of health indicators since the ranges of these statistical features are different; (2) it is difficult to determine a failure threshold since health indicators of different machines are generally different at a failure time. To overcome these drawbacks, a recurrent neural network based health indicator (RNN-HI) for RUL prediction of bearings is proposed in this paper. Firstly, six related-similarity features are proposed to be combined with eight classical time-frequency features so as to form an original feature set. Then, with monotonicity and correlation metrics, the most sensitive features are selected from the original feature set. Finally, these selected features are fed into a recurrent neural network to construct the RNN-HI. The performance of the RNN-HI is verified by two bearing data sets collected from experiments and an industrial field. The results show that the RNN-HI obtains fairly high monotonicity and correlation values and it is beneficial to bearing RUL prediction. In addition, it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.

A Model-Based Method for Remaining Useful Life Prediction of Machinery

Remaining useful life (RUL) prediction allows for predictive maintenance of machinery, thus reducing costly unscheduled maintenance. Therefore, RUL prediction of machinery appears to be a hot issue attracting more and more attention as well as being of great challenge. This paper proposes a model-based method for predicting RUL of machinery. The method includes two modules, i.e., indicator construction and RUL prediction. In the first module, a new health indicator named weighted minimum quantization error is constructed, which fuses mutual information from multiple features and properly correlates to the degradation processes of machinery. In the second module, model parameters are initialized using the maximum-likelihood estimation algorithm and RUL is predicted using a particle filtering-based algorithm. The proposed method is demonstrated using vibration signals from accelerated degradation tests of rolling element bearings. The prediction result identifies the effectiveness of the proposed method in predicting RUL of machinery.

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.

A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction

Remaining useful life (RUL) prediction is a key process in condition-based maintenance for machines. It contributes to reducing risks and maintenance costs and increasing the maintainability, availability, reliability, and productivity of machines. This paper proposes a new method based on stochastic process models for machine RUL prediction. First, a new stochastic process model is constructed considering the multiple variability sources of machine stochastic degradation processes simultaneously. Then the Kalman particle filtering algorithm is used to estimate the system states and predict the RUL. The effectiveness of the method is demonstrated using simulated degradation processes and accelerated degradation tests of rolling element bearings. Through comparisons with other methods, the proposed method presents its superiority in describing the stochastic degradation processes and predicting the machine RUL.

Two new features for condition monitoring and fault diagnosis of planetary gearboxes

Planetary gearboxes are widely used in aerospace, automotive and heavy industrial applications that require compactness and high torque-to-weight ratios. Despite these advantages, tough operation conditions under which planetary gearboxes are typically used may lead to damage on their key components, for example, gears and bearings. Because of the unique behaviors of planetary gearboxes significantly different from fixed-axis gearboxes, the diagnostic features developed and working well for fixed-axis gearboxes will degrade when applied to planetary gearboxes. Therefore, there is a demand to develop features specifically for monitoring and diagnosing planetary gearboxes. To meet this demand, this paper proposes two features, namely, accumulative amplitudes of carrier orders and energy ratio based on difference spectra. The effectiveness of the proposed features is demonstrated using the vibration data acquired from a planetary gearbox test rig. The vibration data is measured under different motor speeds and various types of faults on gears and bearings. The proposed features are also compared with those reported in the literature. The comparison results show that the proposed features are more successful than others in monitoring and diagnosing planetary gearboxes.

Remaining Useful Life Prediction Based on a General Expression of Stochastic Process Models

In remaining useful life (RUL) prediction, stochastic process models are widely used to describe the degradation processes of systems. For age-dependent stochastic process models, the RUL probability density function (PDF) can be calculated using a closed-form solution. For state-dependent models, however, it is difficult to calculate such a closed-form solution. Therefore, the RUL is always approximately estimated using a sequential Monte Carlo-based method, but this method has some limitations. First, it only provides a numerical approximation result whose accuracy highly relies on the quality and quantity of the simulated degradation trajectories. Second, the time interval is unable to be adjusted during the state transition process, resulting in too few discrete probability densities in the result near the end-of-life. This paper describes the degradation processes using a general expression of age- and state-dependent models. The analytical solution of the RUL PDF is derived from the general expression. After that, a new RUL prediction method is proposed. In this method, a series of degradation trajectories are generated through degradation process simulation. The RUL PDF is estimated by inputting the state values of the degradation trajectories into the analytical solution. The validity of the proposed method is verified using fatigue-crack-growth data.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

A particle filtering-based approach for remaining useful life predication of rolling element bearings

Rolling element bearings are one of the most widely used components in rotating machinery. However, they are also the components which frequently suffer from damage. Remaining useful life (RUL) prediction of rolling element bearings has received considerable attention, since it can avoid failure risks, and ensure availability, reliability and security. Model-based methods are commonly used in RUL prediction because of their high accuracy in long-time prediction. In model-based methods, a degradation indicator which describes the whole degradation process of bearings, however, is very critical but difficult to be extracted. A model function, used to predict the evolution trend and the RUL of bearings, is difficult to develop as well. In this paper, a particle filtering (PF)-based approach is developed to predict the RUL of rolling element bearings. In this approach, two modules are included, i.e. indicator calculation module and PF-based prediction module. In the first module, a new degradation indicator is calculated based on correlation matrix clustering and weight algorithm. This indicator fuses different characteristics of multiple features, includes more fault information and therefore has a better prediction tendency. In the second module, a PF-based approach is proposed to predict the RUL of bearings. Different from the traditional PF-based approach, a new algorithm of parameter initialization is introduced to calculate the initial parameters of the state space model. Experimental data of rolling element bearings are used to demonstrate the effectiveness of this approach. For comparison, another RUL prediction approach based on adaptive neuro-fuzzy inference system (ANFIS) is also utilized to process the experimental data. The result shows that the proposed approach can effectively calculate the appropriate degradation indicator, initialize the model parameters and perform better in RUL prediction than the ANFIS-based approach for rolling element bearings.

Machinery health indicator construction based on convolutional neural networks considering trend burr

Abstract In the study of data-driven prognostic methods of machinery, much attention has been paid to constructing health indicators (HIs). Most of the existing HIs, however, are manually constructed for a specific degradation process and need the prior knowledge of experts. Additionally, for the existing HIs, there are usually some outlier regions deviating to an expected degradation trend and reducing the performance of HIs. We refer to this phenomenon as trend burr. To deal with these problems, this paper proposes a convolutional neural network based HI construction method considering trend burr. The proposed method first learns features through convolution and pooling operations, and then these learned features are constructed into a HI through a nonlinear mapping operation. Furthermore, an outlier region correction technique is applied to detect and remove outlier regions existing in the HIs. Unlike traditional methods in which HIs are manually constructed, the proposed method aims to automatically construct HIs. Moreover, the outlier region correction technique enables the constructed HIs to be more effective. The effectiveness of the proposed method is verified using a bearing dataset. Through comparing with commonly used HI construction methods, it is demonstrated that the proposed method achieves better results in terms of trendability, monotonicity and scale similarity.