Abstract
Due to the unsteady state evolution of mechanical systems, the time series of state indicators exhibits volatile behavior and staged characteristics. To model hidden trends and predict deterioration failure utilizing volatile state indicators, an adaptive support vector regression (ASVR) machine is proposed. In ASVR, the width of an error-insensitive tube, which is a constant in the traditional support vector regression, is set as a variable determined by the transient distribution boundary of local regions in the training time series. Thus, the localized regions are obtained using a sliding time window, and their boundaries are defined by a robust measure known as the truncated range. Utilizing an adaptive error-insensitive tube, a stabilized tolerance level for noise is achieved, whether the time series occurs in low-volatility regions or in high-volatility regions. The proposed method is evaluated by vibrational data measured on descaling pumps. The results show that ASVR is capable of capturing the local trends of the volatile time series of state indicators and is superior to the standard support vector regression for state prediction.
Highlights
The state prognosis of mechanical systems is of critical importance in modern industry to prevent unexpected breakdowns, to improve machine availability, and to reduce maintenance costs
Because the trend is learned and memorized by neurons and network weights, Artificial neural network (ANN) provides a nontransparent solution to state prognosis, or rather, the way in which forecast results are inferred by a trained network cannot be observed
It is common for a deteriorating mechanical system to generate volatile time series of state indicators
Summary
The state prognosis of mechanical systems is of critical importance in modern industry to prevent unexpected breakdowns, to improve machine availability, and to reduce maintenance costs. Random coefficient models are another category of prognosis method for mechanical systems In these models, the trend in the state indicators is predefined as a linear, polynomial, exponential, or any other functional form [4, 5]. Nonparametric regression models, in which the trend needs not to take a predetermined form, overcome the barriers of prior knowledge and are commonly used for state prognosis [6] Among this category of models, support vector regression (SVR) [7], which has good generalization ability even if training samples are not abundant, is the most widely accepted method. A novel SVR machine, called adaptive support vector regression (ASVR), is proposed to model the trend of state indicators measured from mechanical systems.
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