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

By Diego Alejandro Tobon-Mejia, Kamal Medjaher, Noureddine Zerhouni, Gerard Tripot. IEEE Transactions on Reliability, 2012

359 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fb7e04fdd61b2f0701c0670070338276e9c70006

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Citations in the corpus

Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, neutral): “Fault prognostics can be defined as the prediction of when a failure might take place (Tobon-Mejia et al. 2012).”
Cloud-enabled prognosis for manufacturing (2015, methodology): “Tobon-Mejia et al. [119] introduced the mixture of Gaussian HMM as an approach to estimate the RUL and the associated confidence level of a bearing.”
On condition based maintenance policy (2015, background): “On the other hand, prognostics corresponds to the estimation of the time to failure and the risk for one or more existing and future failure modes based on anticipated future usage [28,36].”
Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine (2017, methodology): “In the last step, the extracted features are used to train the machine learning models such as support vector machine (SVM) [4], artificial neural networks (ANN) [17], hidden Markov model [18], and k-nearest neighbor [19].”
Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics (2015, background): “The extracted features can be entropy, RMS, or other statistical features [26], [29].”