Convolutional Neural Network with Automatic Learning Rate Scheduler for Fault Classification

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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AlexNet convolutional neural network to classify the types of Indonesian coffee beans

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/905/1/012059 ◽

2021 ◽

Vol 905 (1) ◽

pp. 012059

Author(s):

Y Hendrawan ◽

B Rohmatulloh ◽

F I Ilmi ◽

M R Fauzy ◽

R Damayanti ◽

...

Keyword(s):

Neural Network ◽

Computer Vision ◽

Sensitivity Analysis ◽

Convolutional Neural Network ◽

Confusion Matrix ◽

Learning Rate ◽

Agricultural Products ◽

Coffee Bean ◽

Coffee Beans ◽

Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

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IMPLEMENTASI DEEP LEARNING PADA PENGENALAN AKSARA SUNDA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK

INSERT : Information System and Emerging Technology Journal ◽

10.23887/insert.v2i1.37405 ◽

2021 ◽

Vol 2 (1) ◽

pp. 46

Author(s):

Shelvi Nur Rahmawati ◽

Eka Wahyu Hidayat ◽

Husni Mubarok

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Learning Rate

Aksara Sunda merupakan salah satu aksara daerah Indonesia khususnya masyarakat Sunda. Seiring dengan perkembangan teknologi seperti sekarang ini, bahasa daerah pun semakin tergerus dari waktu kewaktu. Aksara Sunda pun mulai terlupakan, bahkan jarang digunakan oleh masyarakat Sunda dalam kehidupan sehari-hari serta kurangnya memahami Bahasa daerahnya sendiri. Oleh karena itu, perlu adanya pelestarian Bahasa daerah yang dikembangkan menyesuaikan perkembangan jaman agar bisa terus dikenal dan dilestarikan, salahsatunya dengan identifikasi aksara Sunda menggunakan metode Convolutional Neural Network (CNN). Convolutional Neural Network (CNN) adalah bagian dari deep learning yang biasanya digunakan dalam pengolahan data gambar. Hasil dari penelitian ini menggunakan optimasi ADAM dengan penggunaan epoch 20, 50, 100 dan 500. Penggunaan epoch 500, learning rate 0.1 merupakan nilai tertinggi dengan akurasi 98.03%. Berdasarkan hasil data training dengan nilai epoch 100, learning rate 0.001 hasil akurasi sebesar 96.71% data training dan 92.02% data testing.

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IMPLEMENTASI DEEP LEARNING BERBASIS TENSORFLOW UNTUK PENGENALAN SIDIK JARI

Emitor: Jurnal Teknik Elektro ◽

10.23917/emitor.v18i01.6236 ◽

2018 ◽

Vol 18 (01) ◽

pp. 22-27 ◽

Cited By ~ 1

Author(s):

Royani Darma Nurfita ◽

Gunawan Ariyanto

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Input Data ◽

Data Preprocessing ◽

Learning Rate

Sistem pengenalan sidik jari banyak digunakan dala bidang biometrik untuk berbagai keperluan pada beberapa tahun terakhir ini. Pengenalan sidik jari digunakan karena memiliki pola yang rumit yang dapat mengenali seseorang dan merupakan identitas setiap manusia. Sidik jari juga banyak digunakan sebagai verifikasi maupun identifikasi. Permasalahan yang dihadapi dalam penelitian ini adalah komputer sulit melakukan klasifikasi objek salah satunya pada sidikjari. Dalam penelitian ini penulismenggunakan deep learning yang menggunakan metode Convolutional Neural Network (CNN) untuk mengatasi masalah tersebut. CNN digunakan untuk melakukan proses pembelajaran mesin pada komputer. Tahapan pada CNN adalah input data, preprocessing, proses training. Implementasi CNN yang digunakan library tensorflow dengan menggunakan bahasa pemrograman python. Dataset yang digunakan bersumber dari sebuah website kompetisi verifikasi sidik jari pada tahun 2004 yang menggunakan sensor bertipe opticalsensor “V300” by crossMatch dan didalamnya terdapat 80 gambar sidik jari. Proses pelatihan menggunakan data yang berukuran 24x24 pixel dan melakukan pengujian dengan membandingkan jumlah epoch dan learning rate sehingga diketahui bahwa jika semakin besar jumlah epoch dan semakin kecil learning rate maka semakin baik tingkat akurasi pelatihan yang didapatkan. Pada penelitian ini tingkat akurasi pelatihan yang dicapai sebesar 100%

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