Rotating machinery prognostics: State of the art, challenges and opportunities

A summary of fault modelling and predictive health monitoring of rolling element bearings

https://sites.ualberta.ca/~ztian/index_files/Papers/Renewable%20Energy_2011.pdf

Condition based maintenance optimization for wind power generation systems under continuous monitoring

Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. In this paper, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. A function generalized from the Weibull failure rate function is used to fit each condition monitoring measurement series for a failure history, and the fitted measurement values are used to form the ANN training set so as to reduce the effects of the noise factors that are irrelevant to the equipment degradation. A validation mechanism is introduced in the ANN training process to improve the prediction performance of the ANN model. The proposed ANN method is validated using real-world vibration monitoring data collected from pump bearings in the field. A comparative study is performed between the proposed ANN method and an adapted version of a reported method, and the results demonstrate the advantage of the proposed method in achieving more accurate remaining useful life prediction.

An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM) The method relies on two phases: an off-line phase, and an on-line phase During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence The developed method is tested on benchmark data taken from the “NASA prognostics data repository” related to several experiments of failures on bearings done under different operating conditions Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance

/pdf/a-data-driven-failure-prognostics-method-based-on-mixture-of-49ddd6mw8p.pdf

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

Diagnostic technologies for rolling element bearings are relatively well developed, but accurate prediction of remaining life once an incipient fault has been detected is considerably more difficult. This paper describes a comprehensive experimental study of bearing spall progression and a physics-based model being developed for bearing prognostics. The model computes the spall growth trajectory and time to failure based on operating conditions, and uses diagnostic feedback to self-adjust and reduce prediction uncertainty. The predictions compare very well to fault progression tests on both subscale bearings and full-scale turbine engine bearings. The experimental data has demonstrated that spall propagation is better behaved than once thought and can be predicted with high confidence. For turbine engine core thrust bearings with a typical mission mix, the prognostic window (first detection to failure) is on the order of 100 flight hours, which provides ample opportunity to plan future missions and maintenance activities with considerable safety margin. Since the model is physics-based, it is readily adaptable to most bearing applications, and a reduced order version has also been developed which is efficient enough to run on-board with little or no loss in accuracy.

B.P. Morton

Papers

Predicting the remaining life of propulsion system bearings