Research on Micro-Fault Detection and Multiple-Fault Isolation for Gas Sensor Arrays Based on Serial Principal Component Analysis

Xu, Yong; Meng, Ruotong; Yang, Zixuan

doi:10.3390/electronics11111755

Cited by 4 publications

(3 citation statements)

References 54 publications

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“…However, the MOX gas sensor array inevitably suffers from external interference (corrosive gas influence, dust adhesion, temperature, and humidity changes) or self-failure (aging, poisoning, and damage to gas sensing materials) during operation. Once the sensor fails, the application of inaccurate measurements will lead to decreases in the accuracy and reliability of the classification results or even complete errors [ 9 ]. Therefore, appropriate fault diagnosis algorithms must be employed to monitor the abnormal states of the gas sensor array (fault detection), identify fault types (fault identification), and locate faulty gas sensors (fault localization).…”

Section: Introductionmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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With the development of gas sensor arrays and computational technology, machine olfactory systems have been widely used in environmental monitoring, medical diagnosis, and other fields. The reliable and stable operation of gas sensing systems depends heavily on the accuracy of the sensors outputs. Therefore, the realization of accurate gas sensor array fault diagnosis is essential to monitor the working status of sensor arrays and ensure the normal operation of the whole system. The existing methods extract features from a single dimension and require the separate training of models for multiple diagnosis tasks, which limits diagnostic accuracy and efficiency. To address these limitations, for this study, a novel fault diagnosis network based on multi-dimensional feature fusion, an attention mechanism, and multi-task learning, MAM-Net, was developed and applied to gas sensor arrays. First, feature fusion models were applied to extract deep and comprehensive features from the original data in multiple dimensions. A residual network equipped with convolutional block attention modules and a Bi-LSTM network were designed for two-dimensional and one-dimensional signals to capture spatial and temporal features simultaneously. Subsequently, a concatenation layer was constructed using feature stitching to integrate the fault details of different dimensions and avoid ignoring useful information. Finally, a multi-task learning module was designed for the parallel learning of the sensor fault diagnosis to effectively improve the diagnosis capability. The experimental results derived from using the proposed framework on gas sensor datasets across different amounts of data, balanced and unbalanced datasets, and different experimental settings show that the proposed framework outperforms the other available methods and demonstrates good recognition accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Huang,

Wang,

Chen

et al. 2023

Sensors

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show abstract

“…In addition, CNNs have complex structures, converge slowly and may even lead to overfitting, and require high-performance hardware for huge computations [4]. To address these problems, several researchers have proposed simpler deep subspace learning models [5][6][7][8], including principal component analysis network (PCANet) [9], linear discriminant analysis network (LDANet) [9], independent component analysis network (ICANet) [10], canonical correlation analysis network (CCANet) [11], and local binary pattern network (LBPNet) [12].…”

Section: Introductionmentioning

confidence: 99%

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

Li,

Feng,

Wang

et al. 2023

Electronics

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A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.

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“…Automatic fault detection is one stage of multi-stage processes system to detect-diagnose-correct any fault at sensors array in a complex control systems. Literature review to some techniques was presented to detect the faults such as serial principal component analysis (SPCA) [1], Decision Tree, Random Forest, Nearest Neighbors [2] Support-Vector Machine (SVM) [3], [4], Fuzzy Deep N.N (FDNN) [5], principal component analysis (PCA) [6], independent component analysis (ICA) [7], Serial Principal Component Analysis [8], lossless compression method [9], KNN rules [10], Kalman Filter [11], and hidden Markov models (HMM) [12], and other methods [13], [14], [15] , [16], [17], , [18], [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Multiple Neural Networks for Fault Detection of Sensors Array in Oil Heating Reactor

Mustafa¹,

Gharghory²,

Kamal³

2022

IJACSA

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Fault detection is an important issue for early failure revelation and machine components preserving before the damage. The processes of fault detection, diagnosis and correction especially in oil heating reactor sensors are among the most crucial steps for reliable and proper operation inside the reactor.The fault detection in sensors array of heating reactor is considered as an important tool to guarantee that the controller can take the best possible action to insure the quality of the output. In this paper, fault detection for the temperature sensor in oil heating reactor using different types of faults with different levels is addressed. Multiple approaches based on Neural Network (NN)s such as the classical Fully Connected Neural Network (FCNN), Bidirectional Long Short Term Memory network (BiLSTM) based on Recurrent Neural Network (R.N.N.) and Convolutional Neural Network (CNN) are suggested for this purpose. The suggested networks are trained and tested on real dataset sequences taken from sensors array readings of real heating reactor in Egypt. The performance comparison of the suggested networks is evaluated using different metrics such as "confusion matrix", accuracy, precision, etc. The various NN are simulated, trained and tested in this paper using MATLAB software 2021 and the advanced tool of "DeepNetworkDesigner". The simulation results prove that CNN outperforms the other comparative networks with classification accuracy reached to 100% with different levels and different types of faults.

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Research on Micro-Fault Detection and Multiple-Fault Isolation for Gas Sensor Arrays Based on Serial Principal Component Analysis

Cited by 4 publications

References 54 publications

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

Gas Sensor Array Fault Diagnosis Based on Multi-Dimensional Fusion, an Attention Mechanism, and Multi-Task Learning

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

Performance Comparison of Multiple Neural Networks for Fault Detection of Sensors Array in Oil Heating Reactor

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