scholarly journals A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1266
Author(s):  
Yuman Yao ◽  
Jiaxin Zhang ◽  
Wenjia Luo ◽  
Yiyang Dai
Keyword(s):  
Fault Diagnosis ◽  
Gas Absorption ◽  
Noise Levels ◽  
Adaptive Wavelet ◽  
Penalty Term ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Process Fault Diagnosis ◽  
The Impact

Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels.

scholarly journals An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 584
Author(s):  
Shuting Wan ◽  
Bo Peng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Sparse Coding ◽  
Optimal Frequency ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Frequency Window ◽  
Envelope Spectrum ◽  
The Impact ◽  
Periodic Components

Early fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal. Firstly, the paper introduces frequency window, which can slip on the frequency axis and extract the frequency band. Secondly, the double time domain feature entropy is proposed to evaluate the strength of periodic components in signal. The location of the optimal frequency window covering the resonance band caused by bearing fault is determined adaptively by this entropy index and the shifting/expanding frequency window. Thirdly, the signal corresponding to the optimal frequency window is reconstructed, and it is further filtered by the sparse coding shrinkage algorithm to highlight the impact feature and reduce the residue noise. Fourthly, the de-noised signal is demodulated by envelope operation, and the corresponding envelope spectrum is calculated. Finally, the bearing failure type can be judged by comparing the frequency corresponding to the spectral lines with larger amplitude in the envelope spectrum and the fault characteristic frequency. Two bearing vibration signals are applied to validate the proposed method. The analysis results illustrate that this method can extract more failure information and highlight the early failure feature. The data files of Case Western Reserve University for different operation conditions are used, and the proposed approach achieves a diagnostic success rate of 83.3%, superior to that of the AFW method, SCS method, and Fast Kurtogram method. The method presented in this paper can be used as a supplement to the early fault diagnosis method of rolling bearings.

scholarly journals A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Chenguang Huang ◽  
Jianhui Lin ◽  
Jianming Ding ◽  
Yan Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Values ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Periodic Impulse ◽  
Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System

2013 ◽  
Vol 760-762 ◽  
pp. 1062-1066 ◽  
Author(s):  
Xiang Gao ◽  
Tao Zhang ◽  
Hong Jin Liu ◽  
Jian Gong
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Telemetry Data ◽  
Tree Analysis ◽  
Analysis And Design ◽  
Diagnosis Method ◽  
The Impact

In this paper, a fault diagnosis method for spacecraft based on telemetry data mining and fault tree analysis was proposed. Decision trees are constructed from the history telemetry data of the spacecraft, and are used to classify the current data which is unknown whether it is fault. If there is a fault, the fault tree method will be used to analyze the fault reason and the impact on the spacecraft system. This method can effectively solve the problem of diagnostic knowledge acquisition. We design and construct a fault diagnosis expert system for spacecraft based on this diagnosis method. An experiment is presented to prove the effectiveness and practicality of the expert system.

Flocking with Fault Tolerant Control and Obstacles Avoidance for Mobile Robots Based on Grid Maps

2014 ◽  
Vol 631-632 ◽  
pp. 669-675
Author(s):  
Yong Xiong ◽  
Ji Liang Lin
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Mobile Robots ◽  
Control Strategy ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Actuator Failure ◽  
Diagnosis Method ◽  
Tolerance Control ◽  
The Impact

Taking α-lattice flocking as research object, the influence when faults occur in flock and its fault tolerance control algorithm is studied. The impact on flocking performance is analyzed by means of flocking property indexes when communication error, actuator failure or sensor malfunction occur. A flocking fault diagnosis method and fault tolerance control strategy based on communication and data association are introduced. Considering failure mobile robots as obstacles, a complex shaped obstacles avoidance algorithm is proposed. Simulation shows the effectiveness of the method.

scholarly journals Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 822
Author(s):  
Jiaxin Zhang ◽  
Wenjia Luo ◽  
Yiyang Dai
Keyword(s):  
Fault Diagnosis ◽  
Principal Component ◽  
Gas Absorption ◽  
Kernel Principal Component Analysis ◽  
Sensor Faults ◽  
Tennessee Eastman Process ◽  
Acid Gas ◽  
Diagnosis Method ◽  
Diagnostic Framework ◽  
Speed And Accuracy

This study considers the problem of distinguishing between process and sensor faults in nonlinear chemical processes. An integrated fault diagnosis framework is proposed to distinguish chemical process sensor faults from process faults. The key idea of the framework is to embed the cycle temporal algorithm into the dynamic kernel principal component analysis to improve the fault detection speed and accuracy. It is combined with the fault diagnosis method based on the reconstruction-based contribution graph to diagnose the fault variables and then distinguish the two fault types according to their characteristics. Finally, the integrated fault diagnosis framework is applied to the Tennessee Eastman process and acid gas absorption process, and its effectiveness is proved.

scholarly journals Fault Diagnosis Research on Impact System of Hydraulic Rock Drill Based on Internal Mechanism Testing Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yelin Li ◽  
Youxin Luo ◽  
Xiao Wu
Keyword(s):  
Fault Diagnosis ◽  
Fault Rock ◽  
Impact Performance ◽  
Rock Drill ◽  
Diagnosis Method ◽  
New Type ◽  
Internal Mechanism ◽  
Left And Right ◽  
Improved Model ◽  
The Impact

In the production and manufacturing process of hydraulic rock drill, there are small impact energy and low impact frequency, and a fault diagnosis method based on the internal mechanism testing and testing of the hydraulic rock drill is proposed. This method is used to test the change law of hydraulic oil in the rock drill, by retaining the test holes on the cylinder body and other important components of the hydraulic rock drill. At the same time, the mathematical method is used to deal with the test curve, and the influence of pressure pulsation characteristics of the front and rear chambers of the impact piston and the left and right valve chambers of the reversing valve on the impact performance of the rock drill is analyzed. Based on Newton’s law, the model of the impact piston and reversing valve is established. According to the velocity and other parameters of the impact piston, the size of the impact piston switch point is calculated. The coupling between the movement of the impact piston and the movement of the reversing valve is analyzed according to the model established. Comparing the results of the experimental data, the correctness of the fault diagnosis analysis method and model is verified. The effect of the fault rock drill and the improved model is compared, and the theoretical basis is provided for the fault diagnosis of the rock drill and the design of a new type of rock drill.

scholarly journals A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

2021 ◽  
Vol 11 (11) ◽  
pp. 4996
Author(s):  
Gang Yao ◽  
Yunce Wang ◽  
Mohamed Benbouzid ◽  
Mourad Ait-Ahmed
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Optimization Techniques ◽  
Fault Classification ◽  
Vibration Signals ◽  
Kernel Parameter ◽  
Diagnosis Method ◽  
The Impact ◽  
Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

Sign in / Sign up

Export Citation Format

Share Document