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Communication Dans Un Congrès Année : 2024

Predictive Degradation Modelling Using Artificial Intelligence: Milling Machine Case Study

Résumé

Predictive degradation modelling using artificial intelligence involves employing artificial intelligence techniques to anticipate the deterioration or aging of systems, equipment, or materials over time. This approach is particularly valuable in various industries such as manufacturing, healthcare, energy, and transportation, where the timely prediction of degradation can enable proactive maintenance, reduce downtime, and enhance overall system reliability. In degradation modelling, the first hitting/passage time refers to the moment when degradation stochastic process or degradation random variable reaches a predetermined threshold or specific value for the first time, which is crucial in predicting the remaining useful life and making informed decisions regarding maintenance schedules and asset management strategies. Units that fail before reaching a degradation threshold often indicates premature failures, which is a significant concern in reliability analysis as it suggests that the units did not degrade as expected and failed earlier than anticipated. To address this challenge in this article, the first hitting degradation value is introduced to be modelled through artificial intelligence technics. Furthermore, the milling machine degradation data is used to model the machine status using LSTM model, and the degradation trend is predicted using sequential models to forecast the machine status.
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Dates et versions

hal-04564828 , version 1 (30-04-2024)

Identifiants

  • HAL Id : hal-04564828 , version 1

Citer

Hasan Misaii, Mitra Fouladirad, Amélie Ponchet-Durupt, Bahman Askari. Predictive Degradation Modelling Using Artificial Intelligence: Milling Machine Case Study. European Safety and Reliability Conference ESREL 2024, Jagiellonian University, Cracow, Poland, Jun 2024, Cracow, Poland. ⟨hal-04564828⟩
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