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بررسی کاهش نویز در تحلیل سیگنال در عیبیابی ماشینهای دوار توسط شبکه عصبی | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| دوره 55، شماره 12، اسفند 1402، صفحه 1423-1442 اصل مقاله (2.46 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2024.22469.7629 | ||
| نویسندگان | ||
| حامد پورهاشم؛ علی جمالی* ؛ نادر نریمان زاده؛ علی چائی بخش | ||
| دانشکده مهندسی مکانیک، دانشگاه گیلان، گیلان، ایران | ||
| چکیده | ||
| عیبیابی سیستمهای مکانیکی برای عملکرد بهتر سیستم و همچنین محافظت از آن، دارای اهمیت ویژهای میباشد. در این پژوهش از یک سیستم آزمایشگاهی ماشین دوار برای تولید سیگنال استفاده شده است. دادههای بدست آمده در فرآیند پیش پردازش قرار میگیرند. در این مقاله برای بهبود عملکرد تحلیل سیگنال روش ترکیبی تحلیل توسط ویژگیهای سیگنال و فیلتر کالمن پیشنهاد میشود. ابتدا برای کاهش نویز سیگنالها از فیلتر کالمن استفاده میشود. و در ادامه برای پیش پردازش سیگنال ویژگیهای سیگنال در حوزه زمان و حوزه فرکانس پیشنهاد میشود که از آن به عنوان پیشپردازش یک بعدی سیگنال استفاده شده است. در ادامه برای تحلیل ویژگیهای بدست آمده از کلاسبندهای ماشین بردار پشتیبان، شبکه عصبی پرسپترون چند لایه و شبکه عصبی پیچشی استفاده شده است. برای بررسی نتایج دادهها به دسته دادههای آموزشی و دادههای صحت سنجی تقسیمبندی میشود. نتایج دقت برای دادههای صحت سنجی در روشهای مختلف مورد بررسی قرار میگیرد. نتایج حاکی از عملکرد بهتر شبکه عصبی پیچشی الکسنت در حضور فرآیند کاهش نویز با فیلتر کالمن میباشد. این شبکه به طور میانگین به دقت 96/1 درصد برای دادههای صحت سنجی رسیده است که نسبت به سایر کلاسبندها و همچنین نسبت به عیبیابی بدون کاهش نویز، دقت آن بهبود یافته است. | ||
| کلیدواژهها | ||
| عیبیابی؛ کاهش نویز؛ فیلتر کالمن؛ شبکه عصبی؛ پردازش سیگنال | ||
| عنوان مقاله [English] | ||
| Investigating Noise Reduction in Signal Analysis in Rotary Machines Fault Diagnosing by Neural Network | ||
| نویسندگان [English] | ||
| Hamed Pourhashem؛ Ali Jamali؛ Nader Nariman-zadeh؛ Ali Chaibakhsh | ||
| Faculty of Mechanical Engineering, Guilan University, Rasht, Iran | ||
| چکیده [English] | ||
| Fault diagnosis of mechanical systems is of special importance for better system performance as well as its protection. In this work, a rotary machine laboratory system is used to generate signals. The obtained data are placed in the pre-processing process. In this article, to improve the performance of signal analysis, the combined analysis methods using signal features and Kalman filter are proposed. First, the Kalman filter is used to reduce the signal noise. In the following, for signal pre-processing, the features of the signal in the time domain and frequency domain are suggested, which have been used as one-dimensional signal pre-processing. In the following, several neural networks such as support vector machine, multilayer perceptron, and convolutional neural networks have been used to analyze the obtained features. To check the results, the data is divided into training data and validation data. Accuracy results for validation data are examined in different methods. The results indicate the better performance of the AlexNet convolutional neural network in the presence of the Kalman filter noise reduction. In this case, this network has reached an average of 96.1% accuracy for validation data, which has been improved compared to other classifiers and fault diagnosis without noise reduction. | ||
| کلیدواژهها [English] | ||
| Fault diagnosis, Noise Reduction, Kalman Filter, Neural Network, Signal Processing | ||
| مراجع | ||
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