Machine Prognosis with Full Utilization of Truncated Lifetime Data

Intelligent machine fault prognostics estimates how soon and likely a failure will occur with little human expert judgement. It minimizes production downtime, spares inventory and maintenance labour costs. Prognostic models, especially probabilistic methods, require numerous historical failure instances. In practice however, industrial and military communities would rarely allow their engineering assets to run to failure. It is only known that the machine component survived up to the time of repair or replacement but there is no information as to when the component would have failed if left undisturbed. Data of this sort are called truncated data. This paper proposes a novel model, the Intelligent Product Limit Estimator (iPLE), which utilizes truncated data to perform adaptive long-range prediction of a machine component's remaining lifetime. It takes advantage of statistical models' ability to provide useful representation of survival probabilities, and of neural networks ability to recognise nonlinear relationships between a machine component's future survival condition and a given series of prognostic data features. Progressive bearing degradation data were simulated and used to train and validate the proposed model. The results support our hypothesis that the iPLE can perform better than similar prognostics models that neglect truncated data

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

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Bearing performance degradation assessment and prediction based on EMD and PCA-SOM

Bearings are used in a wide variety of rotating machineries. Bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment/prediction and fault diagnosis method based on empirical mode decomposition (EMD) and principal component analysis (PCA)-self organizing map (SOM) is proposed in this paper. First, vibration signals are decomposed into a finite number of intrinsic mode functions, after which the EMD energy feature vector, which is composed of all the IMF energy, is obtained. PCA is then introduced to reduce the dimension of feature vectors. After that, the reduced feature vectors are selected as input vectors of the SOM neural network for performance degradation and fault diagnosis. Finally, the degradation trend of bearing is predicted by Elman neural network. The analysis results from bearings with different fault degrees or degradation trend and fault patterns show that the proposed method can assess and predict the degradation of bearing suitably and achieve a fault recognition rate of over 95 % for various bearing fault patterns

Performance Degradation Assessment and Fault Diagnosis of Bearing Based on EMD and PCA-SOM

Bearings are used in a wide variety of rotating machineries, and bearing vibration signals are non-stationary signals. According to the non-stationary characteristics of bearing vibration signals, a bearing performance degradation assessment and fault diagnosis method based on empirical mode decomposition (EMD) and PCA-SOM is proposed in this paper. Firstly, vibration signals are decomposed into a finite number of intrinsic mode functions (IMFs), and EMD energy feature vector, which is composed of all the IMF energy, is obtained; then, principal component analysis (PCA) is introduced in to reduce the dimension of feature vectors; finally, the reduced feature vectors are chosen as input vectors of SOM neural network for performance degradation and fault diagnosis. The analysis results from bearings with different fault diameters and fault patterns show that the proposed method is able to assess the degradation of bearing suitably, and achieve fault recognition rate of over 95% for various bearing fault patterns

Rolling bearing health assessment using only normal samples

To take maintenance measures timely and to prolong the lifetime of a bearing as a whole, bearing users need to know the current health state of the bearing. Those existing methods for health assessment are mostly based on fault samples and/or decline samples of bearings. However, the bearings are always not allowed to fail in the practical engineering application in consideration of the enormous harms and great damages might be caused by the bearing fault. So the fault samples or decline samples of bearing are often lacking. In this point of view, this paper presents a quantificational health assessment method for rolling bearing based solely on normal samples and SOM network. To demonstrate the capability of the proposed method, a series of vibration datasets of the bearings under various health states were employed to conduct case study. And this paper expresses the uncertainty of assessment results after training for many times by the probability density function (PDF)