Deep Learning-Based Bearing Fault Detection Using 2-D Illustration of Time Sequence

Neupane, Dhiraj; Seok, Jongwon

doi:10.1109/ictc49870.2020.9289232

Cited by 8 publications

(7 citation statements)

References 26 publications

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“…CNNs have dramatically improved computer vision performance and efficiency, object recognition, natural language processing, and speech recognition. With the gradual increase in GPU production and memory, the use of CNNs in computer vision is becoming common [1], [2].…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

“…The rotating machines and induction motor, which are composed of numerous elements like rotor, stator, shaft, and bearings, play a crucial role in industrial systems. Bearings, also known as rolling element bearings (REBs), are the most crucial and the core component of any machinery, and their health state, i.e., healthy or faults and cracks at various locations, directly affects the performance, stability, efficiency, and lifespan of the machines [1], [2]. Bearings mainly consist of four elements: ball, inner-race (IR), outer-race (OR), and cage.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

2021

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Bearings play a vital role in all rotating machinery, and their failure is one of the significant causes of machine breakdown leading to a profound loss of safety and property. Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. This paper presents the model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). State-of-the-art accuracy was achieved with the proposed model using the Case Western Reserve University bearing dataset, which serves as the primary dataset for validating various algorithms for bearing failure detection. Batch normalization techniques were also employed and compared to the proposed model. The spectrogram images were also used as input for further comparison.

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Section: B Convolutional Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

2021

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“…The multilayer structure of CNN can automatically study several level characteristics, which are used to categorize the image class. Because of the computational efficiency of CNNs and their self-learning capacity, they provide robust and efficient performance in image processing; thus, they are used these days ubiquitously [77]. The use of CNNs in sonar ATR for object detection or classification has been practiced recently.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

2020

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Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

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“…The bearing fault, one of the most common faults in machinery, accounts for 30% of the total faults, causing the machine to break down and eventually resulting in a severe loss of safety, property, and even the loss of lives in some cases. Hence, bearing fault detection and diagnosis have attracted researchers and scientists and have become essential for scientific advancement [3,4]. With the growing concept of Industry 4.0 and smart manufacturing, intelligent methods for detecting and classifying machinery faults have been a key part of scientific research and interest.…”

Section: Introductionmentioning

confidence: 99%

“…Traditionally, the maintenance of these REBs used to be a posterior task, usually taking place after the occurrence of the fault. Moreover, this kind of posterior maintenance procedure leads the machine to break down, resulting in financial loss and other casualties [3]. Hence, it is of great significance to surveil the bearing condition during the working state of the engine.…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

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A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

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Deep Learning-Based Bearing Fault Detection Using 2-D Illustration of Time Sequence

Cited by 8 publications

References 26 publications

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

CNN-Based Fault Detection for Smart Manufacturing

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