Abstract
Accurate event detection has high priority in many technical applications. Events in acquired data series, their duration, and statistical parameters provide useful information about the observed system and about its current state. This information can be used for condition monitoring, state identification, and many kinds of forecasting as well. In some cases background noise covers the events and simple threshold or power monitoring methods cannot be used effectively. A novel method called Scaled Sequential Probability Ratio Test (SSPRT) produces 2D array of data via special cumulative sum calculation. A peak determination algorithm has also been developed to find significant peaks and to store the corresponding data for further evaluation. The method provides straight information about the endpoints and possible duration of the detected events as well as shows their significance level. The new method also gives representative visual information about the structure of detected events. Application example for thermomechanical fatigue test monitoring and another for vibration based rotational speed estimation of a four-cylinder internal combustion engine is discussed in this paper.
Highlights
Since the 18th century there has been a growing interest in statistical hypothesis testing
This article presents a new technique, which enhances the evaluation method of the classical sequential probability ratio test. This novel method is based on realization of parallel fixed size cumulative sum calculations with different predefined sizes on the calculated log likelihood ratio
The resulting data is called cumulative sum array and the values can be visualized as a three-dimensional surface
Summary
Since the 18th century there has been a growing interest in statistical hypothesis testing. New methodologies [1,2,3,4] and applications [5,6,7,8,9,10] of statistical hypothesis tests are published every year. In the case of the referenced fusion diagnostic [6, 10] and AE applications [16] the duration of events seems to be a critically important information. Based on this information burst events derived from the test material could be separated from other types of events. In this paper a suggested peak detection method is introduced which has been developed especially to evaluate the data resulted by the presented test method
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