The rotating machinery health assessment and prediction task can be divided into several aspects, since determining and predicting the level of degradation is a function of the signal processing and feature extraction methods, the machine learning algorithm used for health assessment, and the regression methods or other methods used for predicting the future health state. The first aspect of this research was focused on the evaluation of the logistic regression, self-organizing map, and statistical pattern recognition health assessment algorithms. The benchmarking and evaluation of these three algorithms was conducted using vibratory and electrical data collected in a test-bed setting from a new and degraded automotive alternator component. Each of the three algorithms are evaluated based on their accuracy for calculating the bearing, diode and stator winding health, with the use of the logistic regression method providing the best performance accuracy with a combined false positive and false negative rate of 5%.
The second aspect of this research was focused on the evaluation of signal processing and feature extraction techniques for rotating machinery health assessment, facilitated by the use of data collected from a bearing test-rig with rolling element bearings of different levels of induced damage. The high frequency envelope technique and the empirical mode decomposition signal processing methods are compared on the basis of whether each method provides an enhanced level of indicators that can determine the health of rolling element bearings. Each of the methods is compared to the traditional Fourier transform and time domain feature extraction method to determine if the additional signal processing and computations provide more robust features for bearing health assessment. The empirical mode decomposition and envelope feature extraction methods provide a more robust set of features for detecting the lower levels of bearing damage compared to the features extracted from the Fourier transform of the time signal.
The last contribution focuses on the prediction aspect of the rotating machine health assessment framework. The accuracy of the remaining useful life prediction is calculated as a function of the prediction steps ahead as well as the current health status of the system and compared for different failure thresholds. This provides a quantitative constraint on when to use this prediction method, in that the accuracy of the remaining life prediction using this method provides accurate results for predictions once the detected level of degradation is 25% or greater.
|School:||University of Cincinnati|
|School Location:||United States -- Ohio|
|Source:||MAI 48/03M, Masters Abstracts International|
|Subjects:||Mechanical engineering, Computer science|
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