An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

Huang, Jianfeng; Chen, Guohua; Shu, Lei; Wang, Shihua; Zhang, Yu

doi:10.1109/access.2016.2555902

Cited by 28 publications

(13 citation statements)

References 16 publications

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“…Step 4: Compute the within-class compactness S w and the between-class separability S b using Eq. (15) and (16) respectively.…”

Section: A Formulationmentioning

confidence: 99%

“…By investigating the characteristics of incipient faults, many methodologies for condition monitoring of rotating electrical machines have been explored, and most of them are vibration-based analysis [5]- [11]. Features representing machine health conditions are directly extracted from vibration signals via various signal processing algorithms, such as time-domain analysis, and frequencydomain analysis [12]- [15]. The feature set composed by these features is then employed as the initial inputs for the fault diagnosis process, whose reliability has a strong impact on the following steps, and thus finally affects the diagnostic performance of the entire process.…”

Section: Introductionmentioning

confidence: 99%

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Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

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It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this paper, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

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“…Step 4: Compute the within-class compactness S w and the between-class separability S b using Eq. (15) and (16) respectively.…”

Section: A Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

2020

IEEE Access

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“…In the investigation communal, reversible watermarking has appealed a percentage of responsiveness in recent times. Towards attaining improved strength, watermarking by means of transform domain which is attentive on the wavelet transform [3],is employed in numerous latest revisions.…”

Section: Introductionmentioning

confidence: 99%

Wavelet based Reversible Image Watermarking with Logistic Encryption for Health Informatics Systems

2019

IJRTE

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Health Informatics systems preserves the patient’s digital records. Two techniques that help in this process are watermarking and encryption. In this paper a reversible image watermarking scheme with logistic encryption is presented. The reversible watermarking is utilizing the concept of integer wavelet transform. The image is divided into sub bands and then the binary data is hidden in these sub bands. The watermarked wavelet sub bands are passed through logistic encryption module which scrambles the coefficients. These coefficients are then sent to inverse wavelet transform for image reconstruction. This process helps encrypt the image though spectral scrambling, thus resulting in faster and better encryption. The proposed algorithm outperforms the exiting algorithms in terms of execution time and the level of encryption.

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“…The WPT is more practical in fault diagnosis schemes because of its better time–frequency resolution. Numerous studies investigating the time domain, frequency domain, and time–frequency domain features have been carried out to design fault diagnosis schemes using vibration signals in collaboration with machine learning (ML) methods (e.g., regression models, support vector machines, and artificial neural networks (ANNs)) [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Huo et al [ 37 ] presented a multi-speed fault diagnosis scheme with the help of self-adaptive wavelet transform components.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

Sohaib

Kim

2017

Sensors

172

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Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).

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An Experimental Study of Clogging Fault Diagnosis in Heat Exchangers Based on Vibration Signals

Cited by 28 publications

References 16 publications

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

Wavelet based Reversible Image Watermarking with Logistic Encryption for Health Informatics Systems

A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis

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