کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1135923 956129 2008 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network
چکیده انگلیسی
Fault diagnosis and condition surveillance of rotating machinery in a plant is very important for guaranteeing production efficiency and plant safety. In a large plant, with an enormous number of rotating machines, condition surveillance and fault diagnosis for all rotating machines is not only time consuming and labor intensive, but the accuracy of condition judgment cannot be ensured. These difficulties may cause serious machine accidents and consequently great production losses. In order to improve the efficiency of condition surveillance and detect faults at an early stage, this paper proposes a method of condition surveillance and fault discrimination for rotating plant machinery using non-dimensional symptom parameters in a time domain and “Partially-linearized Neural Network” (PLNN), from which the state of a rotating machine can be discriminated automatically. The verification results of precise diagnosis for rolling bearings show that the PLNN can effectively distinguish bearing faults. The verification results for condition surveillance of rotating machinery in a real plant show that the PLNN correctly judges the machine state of the inspected rotating machine as normal or abnormal.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Industrial Engineering - Volume 55, Issue 4, November 2008, Pages 783-794
نویسندگان
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