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<p>In the industrial systems the degradation paths behaviour are different from operating period to another and which including additive and multiplicative effects may cause an abrupt jump or (and) an accelerate failure rate. Moreover, it caused it more challenging and complicated to monitor the operating systems. The question here, how to monitoring the degradation paths behaviour. For this reason, we present some concepts of model integrated covariates or a combined of two degradation process modes. This work focused on dynamic change detection between two hypothesis based on Wiener process model with statistical approaches. We consider a sequence of independent observations of degradation from a normal distribution. We classify each degradation stage under hypothesis test which are assumed normally distributed. Particularly, we make a sequential decision based on the Generalized Likelihood Ratio Test (GLRT) to know both important parameters, the moment when the degradation level change and its magnitude. For high reliability and long life time of dynamic system components, based on both parameters, the GLRT and Monte Carlo simulation set data, are used to predict the Remaining Useful Life (RUL) in real time before a failure occurs. Finally, several numerical examples are given to illustrate the concepts.</p>

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Monitoring Degradation Process Level and Reliability Improvement based on Decision Making with GLR Test

  • 期刊论文

  • 发表日期：

    2025年4月25日

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  • Med Hedi Moulahi,
  • Sami Zdiri

摘要

In the industrial systems the degradation paths behaviour are different from operating period to another and which including additive and multiplicative effects may cause an abrupt jump or (and) an accelerate failure rate. Moreover, it caused it more challenging and complicated to monitor the operating systems. The question here, how to monitoring the degradation paths behaviour. For this reason, we present some concepts of model integrated covariates or a combined of two degradation process modes. This work focused on dynamic change detection between two hypothesis based on Wiener process model with statistical approaches. We consider a sequence of independent observations of degradation from a normal distribution. We classify each degradation stage under hypothesis test which are assumed normally distributed. Particularly, we make a sequential decision based on the Generalized Likelihood Ratio Test (GLRT) to know both important parameters, the moment when the degradation level change and its magnitude. For high reliability and long life time of dynamic system components, based on both parameters, the GLRT and Monte Carlo simulation set data, are used to predict the Remaining Useful Life (RUL) in real time before a failure occurs. Finally, several numerical examples are given to illustrate the concepts.