Fault Diagnosis of Gearbox with a Transferable Deep Neural Network

Mapping Intimacies ◽

10.20944/preprints202005.0347.v1 ◽

2020 ◽

Author(s):

Greg Smith ◽

Masayoshi Shibatani

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Fault Diagnosis ◽

Domain Adaptation ◽

Fault Detection And Diagnosis ◽

Training Data ◽

Operating Condition ◽

Cross Domain ◽

Testing Data

In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions

Measurement Science and Technology ◽

10.1088/1361-6501/ab64aa ◽

2020 ◽

Vol 31 (5) ◽

pp. 055601 ◽

Cited By ~ 3

Author(s):

Jaskaran Singh ◽

Moslem Azamfar ◽

Abhijeet Ainapure ◽

Jay Lee

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Domain Adaptation ◽

Variable Speed ◽

Cross Domain

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Deep Neural Network for Multi-Class Prediction of Student Performance in Educational Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2155.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 5073-5081

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Student Performance ◽

Activation Function ◽

Machine Learning Algorithms ◽

Training Data ◽

Fine Tuning ◽

Academic Excellence ◽

Data Set

Prediction of student performance is the significant part in processing the educational data. Machine learning algorithms are leading the role in this process. Deep learning is one of the important concepts of machine learning algorithm. In this paper, we applied the deep learning technique for prediction of the academic excellence of the students using R Programming. Keras and Tensorflow libraries utilized for making the model using neural network on the Kaggle dataset. The data is separated into testing data training data set. Plot the neural network model using neuralnet method and created the Deep Learning model using two hidden layers using ReLu activation function and one output layer using softmax activation function. After fine tuning process until the stable changes; this model produced accuracy as 85%.

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Fault Diagnosis of Gearbox using Machine Learning and Deep Learning Techniques

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1486.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3940-3943

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Fault Diagnosis ◽

Maintenance Cost ◽

Support Vector ◽

Machine Condition Monitoring ◽

Learning Techniques ◽

Catastrophic Failures ◽

Better Than

Gearbox is an important component used for automobiles, machine tools, industries etc. Failure of any component in gearbox will cause huge maintenance cost and production loss. Failure should be detected as early as possible in order to avoid sudden breakdown which even cause catastrophic failures. Vibration signals are used for machine condition monitoring for predictive maintenance and efficiently predicts fault in the gearbox. In this paper signals from vibration is used for diagnosis of gearbox fault. The experiment uses four different conditions of gearbox in four different load conditions. Then statistical feature extraction is done and obtained result is given to Decision Tree, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) for fault diagnosis. The efficiency of these four techniques is compared and shows that machine learning is better than deep learning in gearbox fault diagnosis.

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PERANCANGAN APLIKASI MOBILE UNTUK KLASIFIKASI SAYURAN MENGGUNAKAN DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK

Sebatik ◽

10.46984/sebatik.v24i2.1134 ◽

2020 ◽

Vol 24 (2) ◽

pp. 300-306

Author(s):

Muhamad Jaelani Akbar ◽

Mochamad Wisuda Sardjono ◽

Margi Cahyanti ◽

Ericks Rachmat Swedia

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Convolutional Neural Network ◽

Image Classification ◽

Test Accuracy ◽

Final Test ◽

Testing Data

Sayuran merupakan sebutan bagi bahan pangan asal tumbuhan yang biasanya mengandung kadar air tinggi dan dikonsumsi dalam keadaan segar atau setelah diolah secara minimal. Keanekaragaman sayur yang terdapat di dunia menyebabkan keragaman pula dalam pengklasifikasian sayur. Oleh karena itu diperlukan adanya pendekatan digital agar dapat mengenali jenis sayuran dengan cepat dan mudah. Dalam penelitian ini jumlah jenis sayuran yang digunakan sebanyak 7 jenis diantara: brokoli, jagung, kacang panjang, pare, terung ungu, tomat dan kubis. Dataset yang digunakan berjumlah 941 gambar sayur dari 7 jenis sayur, ditambah 131 gambar sayur dari jenis yang tidak terdapat pada dataset, selain itu digunakan 291 gambar selain sayuran. Untuk melakukan klasifikasi jenis sayuran digunakan algoritme Convolutional Neural Network (CNN), yang merupakan salah satu bidang ilmu baru dalam Machine Learning dan berkembang dengan pesat. CNN merupakan salah satu algoritme yang terdapat pada metode Deep Learning dengan memiliki kemampuan yang baik dalam Computer Vision, salah satunya yaitu image classification atau klasifikasi objek citra. Uji coba dilakukan pada lima perangkat selular berbasiskan sistem operasi Android. Python digunakan sebagai bahasa pemrograman dalam merancang aplikasi mobile ini dengan menggunakan modul Tensor flow untuk melakukan training dan testing data. Metode yang dapat digunakan dalam melakukan klasifikasi citra ini yaitu Convolutional Neural Network (CNN). Hasil final test accuracy yang diperoleh yaitu didapat keakuratan mengenali jenis sayuran sebesar 98.1% dengan salah satu hasil pengujian yaitu klasifikasi sayur jagung dengan akurasi sebesar 99.98049%.

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Autonomous Tomato Harvesting Robotic System in Greenhouses: Deep Learning Classification

Mekatronika ◽

10.15282/mekatronika.v1i1.1148 ◽

2019 ◽

Vol 1 (1) ◽

pp. 80-86

Author(s):

Ooi Peng Toon ◽

Muhammad Aizzat Zakaria ◽

Ahmad Fakhri Ab. Nasir ◽

Anwar P.P. Abdul Majeed ◽

Chung Young Tan ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

South America ◽

Solanum Lycopersicum ◽

Training Data ◽

New Classification ◽

The Past ◽

Testing Data ◽

Harvesting Robot

Solanum lycopersicum or generally known as tomato came from countries of South America and has been growing in many tropical countries and its healthy nutrients in tomato becomes one of the food demand by the locals in Malaysia when their lifestyle shifted to more concern for healthy food. Since export value and production has increased for the past few years, a vast amount of labours considered for the fruit-picking process. Hence, farmers are now preferring to look for automation to replace labour problems and high cost that they are facing. To pick a correct fruit within clusters, a harvesting robot requires guidance so that it can detect a fruit accurately. In this study, a new classification algorithm using deep learning specifically convolution neural network to classify the image is either a tomato or not tomato and next, the image is classified into either a ripe or unripe tomato. Furthermore, there are two classification neural networks which are tomato or not tomato and ripe and unripe tomato. Each network consists of 600 training data and 33 testing data. The accuracies that obtained from network 1 (tomato or not tomato) and network 2 (ripe or unripe tomato) are 76.366% and 98.788% respectively.

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Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Energies ◽

10.3390/en14165150 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5150

Author(s):

Shiza Mushtaq ◽

M. M. Manjurul Islam ◽

Muhammad Sohaib

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Fault Diagnosis ◽

Learning Algorithms ◽

Data Driven ◽

Support Vector ◽

Comprehensive Review ◽

Bearing Fault ◽

Bearing Fault Diagnosis

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

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Pengembangan Aplikasi Pengenalan Aksara Komering Menggunakan Metode Deep Learning Berbasis Android

Journal of Computer and Information Systems Ampera ◽

10.51519/journalcisa.v1i2.38 ◽

2020 ◽

Vol 1 (2) ◽

pp. 99-111

Author(s):

Mahmud Mahmud ◽

Yesi Novaria Kunang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Testing Data

Penggunaan aksara Komering dari waktu ke waktu mulai ditinggalkan di kalangan masyarakat suku Komering dengan banyaknya budaya luar masuk ke Indonesia. Penelitian ini bertujuan untuk melestarikan dan menghidupkan kembali aksara Komering beserta bahasanya dengan menggunakan pemanfaatan teknologi Deep learning yang dibenamkan kedalam aplikasi android. Teknologi DL yang merupakan suatu sub ilmu dari bidang Machine Learning yang dikembangkan atas dasar cara berfikir dari struktur otak manusia sehingga dapat menghasilkan klasifikasi yang lebih baik. Salah satu algoritma dari DL yang terkenal baik dalam mengenali gambar ialah Convolutional Neural Network. Pada penelitian ini percobaan pengklasifikasian gambar aksara Komering dilakukan dengan data sebanyak 1540 gambar, 29 class. Hasil yang didapatkan dari data testing dengan data asli yaitu 58%, sedangkan untuk testing data augmented 80%. Persentase dalam pengujian aplikasi android melalui kamera 84,14%, galeri 87,58% dan penulisan 93,79%

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Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

Energies ◽

10.3390/en14164886 ◽

2021 ◽

Vol 14 (16) ◽

pp. 4886

Author(s):

David Gonzalez-Jimenez ◽

Jon del-Olmo ◽

Javier Poza ◽

Fernando Garramiola ◽

Izaskun Sarasola

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

High Resistance ◽

Failure Modes ◽

Induction Machines ◽

High Energy ◽

Fault Detection And Diagnosis ◽

Training Data ◽

Motor Power ◽

Human Errors

Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help maintenance assistants on identifying faults in the power connections of induction machines. Specifically, a strategy for failure modes such as high resistance connections, single phasing faults and opposite wiring connections has been designed. In this case, as field data under the aforementioned faulty events are scarce in industry, a simulation-driven ML-based fault diagnosis strategy has been implemented. Hence, training data for the ML algorithm has been generated via Software-in-the-Loop simulations, to train the machine learning models.

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Pengolahan Citra untuk Membedakan Ikan Segar dan Tidak Segar Menggunakan Convolutional Neural Network

Indonesian Journal of Applied Informatics ◽

10.20961/ijai.v5i1.41770 ◽

2021 ◽

Vol 5 (1) ◽

pp. 11

Author(s):

Arif Agustyawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Training Data ◽

Independent Learning ◽

Fresh Fish ◽

Testing Data ◽

Neural Network Algorithm ◽

Sorting Process ◽

Manual Methods

Abstrak: Proses penyortiran ikan yang dilakukan oleh nelayan atau penjual, untuk menyeleksi ikan berdasar kualitasnya masih menggunakan metode manual dan terkadang meleset karena faktor keterbatasan indra penglihatan ketika lelah. Selama ini pemeriksaan hanya dillihat secara fisik. Akibatnya, saat akan dikonsumsi ikan tersebut kerap kali sudah rusak. Penelitian ini mencoba menerapkan algoritma Convolutional Neural Network (CNN) untuk membedakan ikan segar dan tidak segar. Convolutional Neural Network merupakan salah satu metode deep learning yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek, dan klasifikasi objek. Pada penelitian ini, diterapkan algoritma Convolutional Neural Network untuk membedakan ikan segar dan tidak segar. Proses learning jaringan menghasilkan akurasi 100% terhadap data training dan data validation. Pengujian terhadap data testing juga menghasilkan akurasi 100%. Hasil penelitian ini menunjukan bahwa penggunaan metode Convolutional Neural Network mampu mengidentifikasi dan mengklasifikasikan ikan segar dan tidak segar dengan sangat baik.___________________________Abstract:The fish sorting process carried out by fishermen or sellers, to select fish based on quality is still using manual methods and sometimes misses due to the limited sense of sight when tired. So far the examination has only been seen physically. As a result, the fish will often be damaged when consumed. This study tries to apply the Convolutional Neural Network (CNN) algorithm to distinguish between fresh and non-fresh fish. Convolutional Neural Network is a method of deep learning that is capable of conducting independent learning processes for object recognition, object extraction, and object classification. In this study, the Convolutional Neural Network algorithm is applied to distinguish between fresh and non-fresh fish. Network learning process produces 100% accuracy of training data and data validation. Testing of testing data also results in 100% accuracy. The results of this study indicate that the use of the Convolutional Neural Network method can identify and classify fresh and non-fresh fish very well.

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