Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

Nguyen, Cong Dai; Ahmad, Zahoor; Kim, Jong‐Myon

doi:10.3390/app11167575

Cited by 13 publications

(5 citation statements)

References 51 publications

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“…But this structure is not lightweight enough to meet the requirement of timeliness for batch process quality prediction. CNN has also been used in the construction of rotating machinery fault diagnosis and fault detection research [36][37][38]. The essence of quality prediction for key variables of batch processes is to mine the mapping relationships in historical data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning with CBAM-based CNN for batch process quality prediction

Zhao,

Tuo,

Hui

2023

Meas. Sci. Technol.

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Data-driven quality prediction method has been widely used in product estimation of batch processes. However, the initial conditions of different batches in batch process are different, and the multiphase characteristics and nonlinearity in batch are not conducive to the quality prediction. To solve these problems, a model for batch process quality prediction based on a convolutional neural network (CNN) is proposed. Firstly, in order to enhance data characteristics and reduce model computing time, a maximum information coefficient (MIC) method based on mutual information is used to select variables according to the correlation between process variables and quality variables. Secondly, the quality prediction model of CBAM-CNN based on the attention mechanism is established. On the one hand, an improved convolutional block attention module (CBAM) is added to the CNN. The input feature mapping is re-calibrated to focus on useful feature information and weaken irrelevant redundant information in each sliding window. On the other hand, by introducing an improved convolutional module with double-band skip connection lines, the backpropagation speed of the CBAM-CNN model is increased, which can effectively avoid the occurrence of the overfitting problem. Finally, the data of batch process is used as the input of the prediction model. The superiority and effectiveness of the proposed model are verified by predicting the quality variable of the penicillin fermentation process simulation benchmark and the industrial-scale penicillin fermentation process. It is proved that the proposed model has better generalization performance in the quality prediction of the penicillin fermentation process with different control strategies.

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Section: Introductionmentioning

confidence: 99%

Deep learning with CBAM-based CNN for batch process quality prediction

Zhao,

Tuo,

Hui

2023

Meas. Sci. Technol.

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“…In recent years, many improved algorithms for VMD have been studied, but the problem of under-decomposition inevitably is caused by the limitations of the algorithm [23][24][25]. Kim presented real case studies of fault diagnosis based on deep convolutional networks and principal component analysis [26,27]. Regarding the topic of fault diagnosis for rotating machinery, many studies have been conducted; however, the following difficulties and challenges are posed:…”

Section: Introductionmentioning

confidence: 99%

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

Zhang

2023

Algorithms

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Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis.

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“…Deep learning (DL) methods can solve the problem of leak-related information extraction and classification [20]. DL algorithms enable feature extraction from the image without human intervention [21]. The issue of identifying transients due to the fact of unexpected leaks still hinders perfect leak detection.…”

Section: Introductionmentioning

confidence: 99%

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

Ahmad

Kim

et al. 2022

Sensors

Self Cite

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This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time–frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.

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Gearbox Fault Identification Framework Based on Novel Localized Adaptive Denoising Technique, Wavelet-Based Vibration Imaging, and Deep Convolutional Neural Network

Cited by 13 publications

References 51 publications

Deep learning with CBAM-based CNN for batch process quality prediction

Deep learning with CBAM-based CNN for batch process quality prediction

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning

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