首页|Reports Outline Machine Learning Research from University of Manchester (Two-ste p vibration-based machine learning model for the fault detection and diagnosis i n rotating machine and its blind application)
Reports Outline Machine Learning Research from University of Manchester (Two-ste p vibration-based machine learning model for the fault detection and diagnosis i n rotating machine and its blind application)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news reporting from Manchester, Uni ted Kingdom, by NewsRx journalists, research stated, “A robust and reliable cond ition monitoring and fault diagnosis system is crucial for an efficient operatio n of industries. Because of the advances in technologies over the past few decad es, there is an increased interest in developing intelligent systems to perform tasks that traditionally rely on knowledge, experience and expertise of an indiv idual.” The news editors obtained a quote from the research from University of Mancheste r: “It is known that unexpected breakdowns have wide implications in production processes. Thus, it is vital to be able to know the machine condition and detect at the earliest possible stage the defects when they occur. Aiming at an indust rial application, in this study, a two-step approach is proposed for the fault d etection and diagnosis of rotor-related faults. The implemented algorithm is a p attern recognition supervised artificial neural network, which through informati on extracted from vibration signals allows one to identify the health status of the machine. In the first step, the model identifies whether the machine is heal thy or faulty. This is important information for any industry to operate the mac hines. Once the machine condition (healthy or faulty) is known and if it is faul ty, then only faulty machine parameters are used in the second step to know the specific fault.”
University of ManchesterManchesterUn ited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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