Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation

Abstract

In this paper, a novel method for long-term data segmentation in the context of machine health prognosis is presented. The purpose of the method is to find borders between three data segments. It is assumed that each segment contains the data that represent different statistical properties, that is, a different model. It is proposed to use a moving window approach, statistical parametrization of the data in the window, and simple clustering techniques. Moreover, it is found that features are highly correlated, so principal component analysis is exploited. We find that the probability density function of the first principal component may be sufficient to find borders between classes. We consider two cases of data distributions, Gaussian and α-stable, belonging to the class of non-Gaussian heavy-tailed distributions. It is shown that for random components with Gaussian distribution, the proposed methodology is very effective, while for the non-Gaussian case, both features and the concept of moving window should be re-considered. Finally, the procedure is tested for real data sets. The results provided here may be helpful in understanding some specific cases of machine health prognosis in the presence of non-Gaussian noise. The proposed approach is model free, and thus it is universal. The methodology can be applied for any long-term data where segmentation is crucial for the data processing.

Impact and Assessment of Suspension Stiffness on Vibration Propagation into Vehicle

“…An even more advanced algorithm for computing the averaged infogram for local damage detection in the presence of non-Gaussian impulsive noise was studied in [ 43 ]. Another approach for advanced signal processing in machine diagnostics as the method for long-term data segmentation in the context of machine health prognosis is presented in [ 44 ]. Signal processing methods can be used for any kind of signals, for example, the parameters of the engine operating condition and course of the combustion process, as presented in [ 45 ].…”

Robust switching Kalman filter for diagnostics of long-term condition monitoring data in the presence of non-Gaussian noise

“…Using the long-term condition monitoring data is a crucial element in both diagnostics and prognostics. Many methods published in recent years for CBM can be categorized into four main groups [1]: stochastic-based [2,3,4,5,6,7,8], machine learning-based [9,10,11,12], physics or model-based [13,14], and hybrid methods [15,16]. Both machine learning and stochastic approaches such as neural networks [12,17,18,19,20,21,22,23] and hidden Markov models (HMM) [24,25,26,27,28,29] have strong potential to be used for diagnostics and prognostics areas.…”