Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions

Choudhary, Anurag; Mian, Tauheed; Fatima, Shahab; Panigrahi, B. K.

doi:10.1109/jsen.2022.3164430

Cited by 37 publications

(10 citation statements)

References 34 publications

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“…Catalin Teodoriu et al [5] utilize sound signals to detect Mud Pumps. Gang Li et al [6] propose a dual-source gramian angular (DS-GAF) method to fuse vibration-strain signals to provide more fault information. By using the DS-GAF method, the vibration-strain signals at the fluid end are fused and converted into a fault diagnosis image dataset.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of three-cylinder mud pump based on transfer learning

Yan,

Liu,

Liao

et al. 2024

Eng. Res. Express

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Mud pumps serve as vital components within the circulating system of oil drilling platforms, primarily facilitating the circulation of drilling fluid. With the rapid advancement of deep learning technology, there has been a growing focus on fault diagnosis techniques for mud pumps based on deep learning methodologies. However, existing deep learning approaches often struggle with fault diagnosis of mud pumps under varying operational conditions, as adjustments to the working conditions are necessary in real-time based on drilling depth. To address this challenge, this study introduces an enhanced transfer learning method for diagnosing faults in mud pumps across different operating conditions. Initially, the collected vibration data undergoes resampling to standardize frequency, followed by the utilization of the short-term autocorrelation method to discern phase information of signal impact. Leveraging this phase information, the signal is segmented into distinct segments with uniform phases, thereby minimizing distribution discrepancies between the source and target domains. Subsequently, the transformer is employed as a feature extractor for the model. Finally, a deep sub-domain adaptation network is employed to facilitate transfer from the source domain to the target domain. Validation of the proposed method was conducted using an experimental dataset, with results demonstrating its efficacy compared to other contemporary approaches.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of three-cylinder mud pump based on transfer learning

Yan,

Liu,

Liao

et al. 2024

Eng. Res. Express

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“…However, gas turbines are prone to different problems during operation due to their complex construction and long-term operation in severe working environments, such as high speed, high temperature, and high pressure. Bearing faults are one of the most common causes of equipment failures, accounting for 30%-40% of cases [2,3]. Reliable bearing fault diagnosis is therefore essential to ensure the safety, efficiency, and reliability of mechanical equipment [4,5].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network

Xu,

Li,

Zhao

et al. 2023

Meas. Sci. Technol.

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Gas turbine bearings operate continuously under complex and harsh conditions such as high temperatures, high pressures and high speeds. Bearing fault monitoring data often exhibits anomalies, noise, missing values, and strong coupling and non-linearity due to real-world random factors. In addition, the traditional convolutional neural network is still limited by the scarcity of labelled samples in real-world conditions and cannot fully extract fault features. To address the complexities of strongly coupled fault data, inconsistent data quality, and the shortage of authentic labeled samples, this paper presents a novel method for bearing fault diagnosis. This method utilizes an enhanced valuable sample strategy in conjunction with convolutional neural networks, integrating data enhancement and active learning for preprocessing to create valuable training sets for the networks. At the outset, random dropout and scaling jitter operations are applied to the original data, effectively removing anomalies and gaps in the fault signals. This process simultaneously emulates variations in the frequency spectrum and amplitude of vibration signals under real operating conditions, thereby enhancing the quality of gas turbine bearing fault data. Subsequently, the method incorporates active learning techniques, iteratively selecting unlabeled data samples with the highest information value, and annotating them using support vector machines. This strategy creates a valuable training dataset that further enhances the fault diagnosis capabilities of the model. In the subsequent phase, a three-dimensional convolutional neural network is employed to extract fault feature information efficiently from the fault data, culminating in precise fault classification. In order to validate the effectiveness and superiority of the algorithm, validation and comparison analyses were carried out using the CWRU and XJTU open datasets. To evaluate the practical effectiveness, a gas turbine main bearing dataset collected in a real environment, named the BaiChuan dataset, is used for the engineering validation of the proposed method.

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“…Over the * Author to whom any correspondence should be addressed. last three decades, many industry experts and researchers have given importance to automated online bearing fault diagnosis in rotating machines using several techniques, such as vibration analysis [4,5], oil monitoring and wear debris analysis [6], acoustic emission [7], motor current signature analysis [8] and infrared thermography [9,10]. Among these techniques, vibration analysis was found to be a proven most successful technique for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A self-adaptive multiple-fault diagnosis system for rolling element bearings

Mishra¹,

Choudhary²,

Fatima³

et al. 2022

Meas. Sci. Technol.

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The inevitable simultaneous formation of multiple faults in bearings generates severe vibration, propelling it for premature component failure and unnecessary downtime. For accurate diagnosis of multiple faults, Machine Learning (ML) models need to be trained with the signature of different multi-faults, which increases the data acquisition time and expenses. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single fault vibration signatures. A time-frequency based hybrid signal processing technique, which involves Discrete Wavelet Transform (DWT) and Hilbert Transform (HT) was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimised metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The result showed that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination by using single fault data. It will be helpful where accessibility to a large amount of data is limited for multiple fault diagnosis.

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Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions

Cited by 37 publications

References 34 publications

Fault diagnosis of three-cylinder mud pump based on transfer learning

Fault diagnosis of three-cylinder mud pump based on transfer learning

Fault diagnosis of gas turbine generator bearings using enhanced valuable sample strategy and convolutional neural network

A self-adaptive multiple-fault diagnosis system for rolling element bearings

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