Prophase Failure Prediction of the Mechanical Transmission Systems Based on the Biological Evolutionary Algorithm

2013 ◽  
Vol 572 ◽  
pp. 447-450
Author(s):  
Xiao Hui Chen ◽  
Lei Xiao ◽  
Zhen Xiang Liu ◽  
Chuang Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Early Stage ◽  
Gear Tooth ◽  
Fault Prediction ◽  
Learning Ability ◽  
Support Vector ◽  
Mechanical Transmission ◽  
Test Bed ◽  
Transmission Systems ◽  
Artificial Intelligence Algorithm

There are several types of the mechanical transmission failure, such as gear tooth broken, fatigue, pitting etc. The deterioration pattern of each failure varies according to the different environment. Furthermore, setting up the fault prediction model is quite difficult, especially at the early stage of the fault. In order to predict the prophase failure of the mechanical transmission systems depends on the condition monitoring signal, this paper researched on the biological evolutionary algorithm combined with other artificial intelligence algorithm. As the case study, the typical failure of the gearbox test-bed, for example gear tooth broken or fatigue at the test-bed was monitored by several sensors. An improved support vector machine (SVM) optimized by genetic algorithm (GA) was chosen to predict the prophase failure of the gear, due to its self-adaption and self-learning ability. The prediction results showed that it simulated the failure pattern well on the condition of a few sample data.

scholarly journals A Brief Study on a Novel Approach for the Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Subrata Mukherjee ◽  
Vikash Kumar ◽  
Somnath Sarangi
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Early Stage ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Support Vector ◽  
Temporal Moments ◽  
The Impact

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

Fluoxetine Protects against Dendritic Spine Loss in Middle-aged APPswe/PSEN1dE9 Double Transgenic Alzheimer’s Disease Mice

2020 ◽  
Vol 17 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Jing Ma ◽  
Yuan Gao ◽  
Wei Tang ◽  
Wei Huang ◽  
Yong Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Early Stage ◽  
Learning Ability ◽  
Middle Aged ◽  
Protein Levels ◽  
Spine Pathology ◽  
The Impact

Background: Studies have suggested that cognitive impairment in Alzheimer’s disease (AD) is associated with dendritic spine loss, especially in the hippocampus. Fluoxetine (FLX) has been shown to improve cognition in the early stage of AD and to be associated with diminishing synapse degeneration in the hippocampus. However, little is known about whether FLX affects the pathogenesis of AD in the middle-tolate stage and whether its effects are correlated with the amelioration of hippocampal dendritic dysfunction. Previously, it has been observed that FLX improves the spatial learning ability of middleaged APP/PS1 mice. Objective: In the present study, we further characterized the impact of FLX on dendritic spines in the hippocampus of middle-aged APP/PS1 mice. Results: It has been found that the numbers of dendritic spines in dentate gyrus (DG), CA1 and CA2/3 of hippocampus were significantly increased by FLX. Meanwhile, FLX effectively attenuated hyperphosphorylation of tau at Ser396 and elevated protein levels of postsynaptic density 95 (PSD-95) and synapsin-1 (SYN-1) in the hippocampus. Conclusion: These results indicated that the enhanced learning ability observed in FLX-treated middle-aged APP/PS1 mice might be associated with remarkable mitigation of hippocampal dendritic spine pathology by FLX and suggested that FLX might be explored as a new strategy for therapy of AD in the middle-to-late stage.

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

2016 ◽  
Vol 27 (02) ◽  
pp. 1650039 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Maurizio Campolo ◽  
Nadia Mammone ◽  
Mario Versaci ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Early Stage ◽  
Processing System ◽  
Fine Tuning ◽  
Permutation Entropy ◽  
Support Vector ◽  
Progressive Dementia ◽  
Time Frequency ◽  
Jakob Disease

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Observation of Fatigue Damage to Gear Tooth Using Image Processing

2007 ◽  
Author(s):  
Tomoya Masuyama ◽  
Takuya Ikeda ◽  
Satoshi Yoshiizumi ◽  
Katsumi Inoue
Keyword(s):  
Image Processing ◽  
Acoustic Emission ◽  
Strain Distribution ◽  
Cyclic Load ◽  
Early Stage ◽  
Gear Tooth ◽  
Digital Camera ◽  
Correlation Method ◽  
Point Of View ◽  
Strain Diagram

The detection of damage in early stage of fatigue is important for a reliable evaluation of gear life and strength. From this point of view, the variation of strain distribution in a tooth due to cyclic load contains useful information because the fatigue crack will initiate as a result of the accumulation of plastic strain. Meanwhile, digital image equipments are widely used in our life and the performance is in progress. We took digital pictures of cyclic loaded tooth by the digital camera and compared with the picture of no load to find displacement. The strain distribution of tooth is calculated by the correlation method using those pictures. The initiation of a micro crack is observed by the method. It is also confirmed by the detection of acoustic emission wave with higher energy. The variation of stress-strain diagram in fatigue process is presented, and this illustrates the increase of strain in the final stage of fatigue.

scholarly journals Sustainability Evaluation of Renewable Energy Incubators Using Interval Type-II Fuzzy AHP-TOPSIS with MEA-MLSSVM

2021 ◽  
Vol 13 (4) ◽  
pp. 1796
Author(s):  
Guangqi Liang ◽  
Dongxiao Niu ◽  
Yi Liang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Evolutionary Algorithm ◽  
Least Squares ◽  
Fuzzy Ahp ◽  
Indicator System ◽  
Support Vector ◽  
Type Ii ◽  
Sustainability Evaluation ◽  
Interval Type

With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

Early Stage Fault Prediction via Inter-Project Rule Transfer

2021 ◽  
Author(s):  
Nazli Ece Uykur ◽  
Begum Mutlu ◽  
Ebru A. Sezer
Keyword(s):  
Early Stage ◽  
Fault Prediction

An Advisory Conceptual Design Tool for Mechanical Transmission Systems

1994 ◽  
Author(s):  
C. P. Huang ◽  
F. W. Liou ◽  
J. J. Malyamakkil ◽  
W. F. Lu
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Conceptual Design ◽  
Design Process ◽  
Design Tool ◽  
Mechanical Transmission ◽  
Transmission Systems ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Packaging Machine

Abstract This paper presents an advisory conceptual design tool for mechanical transmission systems. Space consideration was taken into account during the design process. A prototype function tree was built in the form of knowledge-based system to transfer a designer’s idea into a set of mechanical components. An advisory expert system was also developed to help a designer in decision making. As an example, a packaging machine is designed using the developed system.

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