Merve ERTARĞIN, Özal YILDIRIM, Ahmet ORHAN

Motor Yataklarında Meydana Gelen Arızaları Tespit Etmek için Yeni Bir Tek Boyutlu Konvolüsyonel Sinir Ağı Modeli

Elektrik motorları, çeşitli işlemleri otomatikleştirme ve kolaylaştırma yeteneklerinden dolayı endüstride önemli bir yere sahiptir. Elektrik motorlarında meydana gelen arızalar, cihazın veya sistemin çalışmasını etkileyebilmekte ve büyük maddi kayıplara neden olabilmektedir. Bu nedenle arızaların erken aşamada tespit edilmesi kritik bir öneme sahiptir. Arızaların tespitinde bilgisayar destekli yazılımlar kullanılması maliyetten ve zamandan tasarruf etme potansiyeli nedeniyle ön plana çıkmaktadır. Bu çalışmada, motor yatağı arıza türlerini tespit etmek için derin öğrenme tabanlı bir model önerilmiştir. Tek boyutlu konvolüsyonel sinir ağı (1D-CNN) mimarisi kullanan bu model ile sadece titreşim verileri kullanılarak arıza tipi tespiti sağlanmaktadır. Önerilen mimari, titreşim sinyallerini motor arıza teşhisinde hızlı ve güvenilir olarak kullanan etkin bir modeldir. Çalışma kapsamında farklı hız senaryoları kullanılarak eğitim ve test aşamalarının detaylı performans değerlendirmeleri sağlanmıştır. Genelleme kabiliyeti yüksek olan bu model ile, farklı senaryolarda yüksek doğruluk oranları ile arıza tespiti yapılmıştır.

Anahtar Kelimeler:

Derin öğrenme, konvolüsyonel sinir ağı, motor arıza tespiti

A New One-Dimensional Convolutional Neural Network Model for Detecting Motor Bearing Failures

Electric motors have an important place in industry due to their ability to automate and facilitate various processes. Faults that occur in electric motors may affect the operation of the device or system and cause great financial losses. It is therefore critical to detect faults at an early stage. The use of computer-aided software in the detection of faults comes to the fore due to its cost and time saving potential. In this study, a deep learning-based model is proposed to detect engine bearing failure types. With this model, which uses one-dimensional convolutional neural network (1D-CNN) architecture, fault type detection is provided by using only vibration data. The proposed architecture is an efficient model that uses vibration signals to diagnose engine faults quickly and reliably. Within the scope of the study, detailed performance evaluations of the training and testing stages were provided by using different speed scenarios. With this model, which has a high generalization ability, fault detection has been made with high accuracy rates in different scenarios.

Keywords:

Deep learning, convolutional neural network, motor fault detection,

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Jia F, Lei Y, Lin J, Zhou X, Lu N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical systems and signal processing 2016; 72: 303-315.
Kao IH, Wang WJ, Lai YH, Perng JW. Analysis of permanent magnet synchronous motor fault diagnosis based on learning. IEEE Transactions on Instrumentation and Measurement 2018; 68(2): 310-324.
Hoang DT, Kang HJ. A motor current signal-based bearing fault diagnosis using deep learning and information fusion. IEEE Transactions on Instrumentation and Measurement 2019; 69(6): 3325-3333.
Jing L, Zhao M, Li P, Xu X. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 2017; 111: 1-10.
Neupane D, Seok J. Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. IEEE Access 2020; 8: 93155-93178.
Akbani R, Kwek S, Japkowicz N. Applying support vector machines to imbalanced datasets. In: European conference on machine learning; September 2004; Springer, Berlin, Heidelberg. pp. 39-50.
Lou X, Loparo KA. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical systems and signal processing 2004; 18(5): 1077-1095.
Zhu H, He Z, Wei J, Wang J, Zhou H. Bearing fault feature extraction and fault diagnosis method based on feature fusion. Sensors 2021; 21(7): 2524.
Aydin I, Karakose M, Akin E. An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. ISA transactions 2014; 53(2): 220-229.
Banerjee TP, Das S. Multi-sensor data fusion using support vector machine for motor fault detection. Information Sciences 2012; 217: 96-107.
Bera A, Dutta A, Dhara AK. Deep learning based fault classification algorithm for roller bearings using time-frequency localized features. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS); 19-20 February 2021; Greater Noida, India. pp. 419-424.
Smith WA, Randall RB. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical systems and signal processing 2015; 64: 100-131.
Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Transactions on Industrial Electronics 2016; 63(11): 7067-7075.
Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification. Advanced Engineering Informatics 2017; 32: 139-151.
Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, ... & Van Hoecke S. Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration 2016; 377: 331-345.
Shen S, Lu H, Sadoughi M, Hu C, Nemani V, Thelen A, ... & Kenny S. A physics-informed deep learning approach for bearing fault detection. Engineering Applications of Artificial Intelligence 2021; 103: 104295.
Hu J, Deng S. Rolling bearing fault diagnosis based on wireless sensor network data fusion. Computer Communications 2022; 181: 404-411.
Oh JW, Jeong J. Data augmentation for bearing fault detection with a light weight CNN. Procedia Computer Science 2020; 175: 72-79.
Chen Z, Li C, Sanchez RV. Gearbox fault identification and classification with convolutional neural networks. Shock and Vibration 2015; 2015.
Wang X, Qin Y, Zhang A. An intelligent fault diagnosis approach for planetary gearboxes based on deep belief networks and uniformed features. Journal of Intelligent & Fuzzy Systems 2018; 34(6): 3619-3634.
Zhao H, Liu H, Xu J, Guo C, Deng W. Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network. Journal of Mechanical Science and Technology 2019; 33(9): 4165-4172.
Liu S, Xie J, Shen C, Shang X, Wang D, Zhu Z. Bearing fault diagnosis based on improved convolutional deep belief network. Applied Sciences 2020; 10(18): 6359.
Shao H, Jiang H, Zhang X, Niu M. Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology 2015; 26(11): 115002.
Lei Y, Jia F, Zhou X, Lin J. A deep learning-based method for machinery health monitoring with big data. Journal of Mechanical Engineering 2015; 51(21): 49-56.
Sun W, Shao S, Zhao R, Yan R, Zhang X, Chen X. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 2016; 89: 171-178.
Soother DK, Ujjan SM, Dev K, Khowaja SA, Bhatti NA, Hussain T. Towards soft real-time fault diagnosis for edge devices in industrial IoT using deep domain adaptation training strategy. Journal of Parallel and Distributed Computing 2022; 160: 90-99.
Huang H, Baddour N. Bearing vibration data collected under time-varying rotational speed conditions. Data in brief 2018; 21: 1745-1749.
Yıldırım Ö, Pławiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Computers in biology and medicine 2018; 102: 411-420.
Bengio Y. Learning deep architectures for AI. Now Publishers Inc., 2009.
Goodfellow I, Bengio Y, Courville A. Deep learning. MIT press., 2016.
Chollet, F. Deep learning with Python. Simon and Schuster, 2021.
Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing 2020; 138: 106587.