سیستم های تخصصی مبتنی بر شبکه عصبی مصنوعی مجهز به الگوریتم ژنتیک برای تشخیص وضعیت اجزای مختلف ماشین های دوار در ماشین آلات کشاورزی و صنعتی با استفاده از یک سیگنال تک لرزشی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|46786||2015||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 42, Issues 17–18, October 2015, Pages 6433–6441
This article proposes (i) the estimation method of an expert system to predict the statuses of several agro-industrial machine rotary components by using a vibration signal acquired from a single point of the machine; and, (ii) a learning method to fit the estimation method. Both methods were evaluated in an agricultural harvester. Vibration signal data were acquired from a single point of the harvester under working conditions, by varying (1) the engine speed status (high speed/low speed), (2) the threshing operating status (on/off), (3) the threshing balance status (balanced/unbalanced), (4) the chopper operating status (on/off), and (5) the chopper balance status (balanced/unbalanced). Positive frequency spectrum coefficients of the vibration signal were used as the only inputs of an Artificial Neural Network (ANN) that predicts the five rotary component statuses. Four Genetic Algorithm (GA) based learning methods to fit the ANN weights and biases were implemented and its performance was compared to select the best one. The prediction system that is developed was able to estimate the rotary component status under consideration with a mean success rate of 92.96%. Moreover, the best GA-based learning method that was implemented reduced the number of generations by 70% in the best case, compared with a random learning method, allowing a similar reduction in the time needed to reach the expected success rate. The results obtained suggest that (i) an ANN-based expert system could estimate the status of the rotary components of an agro-industrial machine to a high degree of accuracy by processing a vibration signal acquired from a single point on its structure; and, (ii) by using the best implementation of the GA-based learning method proposed to fit the ANN weights and biases, it is possible to improve the success rate and by doing so to reduce the time needed to perform the adjustment. The main contribution of this work is the proposal of a classification method that estimates the status of several rotary elements placed each one far from the others employing the signal acquired from only one accelerometer and non-requiring a feature extraction stage.