كليدواژه :
عيوب مكانيكي , لقي , شبكه عصبي مصنوعي , الگوريتم بهينه سازي ازدحام ذرات
چكيده فارسي :
لقي تكيه گاه هاي موتور و ياتاقان ها سيستم را با كوپلينگ 4 نوع مختلف عيب ابتدا با استفاده از روش تبديل سريع فوريه فركانس ها و جابجايي هاي عمودي شفت در محل دو ياتاقان استخراج نموده و سپس اثر لقي تكيه گاه ها را در حالت حضور و عدم حضور عيوب ديگر مورد بررسي قرار ميگيرد. حال براي دستيابي به يك مدل بهينه از شبكه عصبي بهمراه الگوريتم بهينه سازي ازدحام ذرات تك هدفه استفاده مي كنيم بدين صورت كه يكبار فركانس هاي سيستم معيوب و بدون بعنوان ورودي شبكه عصبي معرفي ميگردند و خروجي مطلوب آن فركانس سيستم در حالتي كه سيستم هيچ گونه عيبي ندارد مدلسازي مي شود و سپس در مرحله بعد فرآيند قبل جهت مدل سازي بيهنه با شبكه عصبي را با استفاده از جابجايي هاي معيوب(وروي شبكه عصبي) و جابجايي سيستم (ورودي مطلوب) مورد ارزيابي قرار ميگيرد.
چكيده لاتين :
Optimization, is the main aim of this paper. In mechanical systems in various
industries, are causing to reduce efficiency and performance of systems. So,
prediction and problem‐solving for mechanical faults that cannot be harmful on
systems, as well, and were dealing to prevent from so many problems and
negative impact on them. Now, rotary system is including shaft, propeller,
bearing and motor which are main components and also mechanical faults
always occur and show on these components. Due to in this paper, we are
discussing about one rotary shaft and multidisc were fixed on middle and end
of the shaft and rotary system worked in static and constant rotation. Now we
want to analysis multi‐diagnoses such as unhealthy bearing, unbalancing disc,
and misalignments (offset and angular) bedsides clearance on bolts which
located on bearing and electrical motor supports. In the other hand, these faults
were coupled each other at the same time. Firstly, by using Fast Fourier
Transform (FFT), it was monitored and secondly, artificial neural networks
(ANN) besides particle swarm optimization (PSO) were used to condition
monitoring, simultaneously. According to ANN‐PSO, there were explained data
as input and output (target) which were considered shaft displacements in
mechanical degradation and health conditions, respectively.
