![]() ![]() The method provides straight information about the endpoints and possible duration of the detected events as well as shows their significance level. A peak determination algorithm has also been developed to find significant peaks and to store the corresponding data for further evaluation. A novel method called Scaled Sequential Probability Ratio Test (SSPRT) produces 2D array of data via special cumulative sum calculation. In some cases background noise covers the events and simple threshold or power monitoring methods cannot be used effectively. This information can be used for condition monitoring, state identification, and many kinds of forecasting as well. Events in acquired data series, their duration, and statistical parameters provide useful information about the observed system and about its current state. Īccurate event detection has high priority in many technical applications. In these cases SPRT has been used for malfunction detection, place identification, or AE (acoustic emission) event detection. ![]() 2 Mathematical Problems in Engineering Some practical technical applications of SPRT have been also published by the authors. The latest published applications of SPRT focus for example on human core temperature prediction to prevent hyperthermia, diagnostics of the COMPASS tokamak, and W7X stellarator furthermore, many vibration measurement based applications of the methodology can be found in the literature. In the last decades, there have been a surge of practical applications of the SPRT methodology in many areas including low frequency sonar detection, passive acoustic detection of marine mammals, tracking of signals, target tracking, early detection of changes in signals, computer simulations, data mining, clinical trials, gene ordering, agricultural sciences, horticulture, pest management, educational testing, economics and finance. A comparison with other life prediction and SPRT methods is given to elucidate the efficacy of the proposed approach. The predicted and actual values of the residual life are compared, and the average relative error is 3.90 %, which verifies the validity of the proposed residual life prediction approach. Explicit expression for the distribution of RUL is derived in terms of the posterior probability that the system is in the unhealthy state. Using historical failure data and condition monitoring data, a life prediction model based on hidden Markov model (HMM) is established to describe the deterioration process of gearboxes, then the predicted remaining useful life (RUL) is transformed into failure data that is used in SPRT for further analysis, which can significantly save on testing time and reduce costs. The SPRT method for Weibull life distribution is derived in this paper, which enables the implementation of reliability compliance tests for gearboxes. However, for most of the mechanical products, Weibull distribution conforms to their life distributions better compared to other techniques. Assumptions accompanying exponential failure models are often not met in the standard sequential probability ratio test (SPRT) of many products. ![]()
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